Ffmpeg Skill

kajisho52.9kstars

Local video editor for coding agents: 42 FFmpeg tools with a machine-readable contract and an MCP server, for Claude Code, Cursor and Codex. Cut, captions, reframe, loudness, platform exports. No clou

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1# Ffmpeg Skill

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Status: Local

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Transport: stdio
License: MIT
Last checked: 2026-10-28

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Overview

About Ffmpeg Skill

Tools

No tool list yet. Remote servers get one after the first health check.

FAQ

Questions

How do I install Ffmpeg Skill?

Copy the stdio config for Cursor, Claude Desktop, Claude Code, or VS Code on this page. It uses the publisher's install, not a PlugMCP proxy.

Does Ffmpeg Skill need an API key?

No required environment variable is listed on this record.

What transport does Ffmpeg Skill use?

Ffmpeg Skill runs locally over stdio.

When was Ffmpeg Skill last checked?

Last checked 2026-10-28. Local video editor for coding agents: 42 FFmpeg tools with a machine-readable contract and an MCP server, for Claude Code, Cursor and Codex. Cut, captions, reframe, loudness, platform exports. No cloud, no API keys.

From the publisher

Documentation

npx ffmpeg-skill

Left half is the input, right half is what the command produced. All 66 before/after demos, with the exact command under each one → — all of it generated from synthetic footage by python3 demos/build.py, so you can rebuild every frame of it yourself.

ffmpeg-skill is an Agent Skill for Claude Code, Cursor, Codex and any agent that reads SKILL.md. It teaches the agent a fixed workflow (probe → edit losslessly where possible → check → verify) and ships 42 tools that do the actual work with ffmpeg / ffprobe: cut, join, silence removal, fit to duration and aspect, captions and karaoke, overlays and motion graphics, HDR → SDR and LUTs, audio clean-up and typed dynamics, sync with drift correction, multicam, loudness, delivery checks, whole-edit project rendering, batch folders. Every tool is also callable as an MCP tool (tools/list advertises a core 12 by default to keep client context small, with FFMPEG_SKILL_MCP_FULL=1 listing all 42; every tool is reachable by name through tools/call either way), and the whole set is described by a machine-readable contract.

If ffmpeg and python3 are on your PATH, it works: offline, on footage you would rather not upload.

SPEC (Self-Producing Execution Contract): each tool's input_schema — the part of its contract and MCP tool definition that has to track the CLI flag-for-flag — is never hand-authored beside the code. It is derived, at run time, from the same argparse parser that already defines the CLI, and CI fails the build if any of it drifts. → full explanation

Standalone, and in an ecosystem

Standalone, this is a local FFmpeg engine: probe → edit → verify, npx ffmpeg-skill and nothing else. No API key, no account, no other repo required. Everything above and below this section describes that standalone tool, and none of it changes if you never read the rest of this one.

In kajisho5's wider video-production ecosystem, this repo is the hands: it cuts, measures and exports files, and reports back in structured JSON. It does not decide what to cut, whether a deliverable is approvable, what makes a highlight interesting, or what a caption should say (the user's cue text is burned as written, never rewritten to fit) — those are a brain's job, sitting in front of this engine, not inside it.

You want to...	Use
Cut / join / measure / export a file right now	this repo (`ffmpeg-skill`), standalone
Decide cut points, approve a deliverable, plan a whole edit	`video-production-agent` / `AI-video-production-OS`
Build a typed editing graph across a workspace, without writing raw `ffmpeg`	`video-editing-skill` / `audio-production-skill`
Put Remotion motion graphics (animated captions, silence jump cuts, audio-peak motion) on a real recording	`remotion-ffmpeg-skill`: its compositions take their length, fps, frame, silences, scene cuts, audio peaks and beats from this repo's `probe.py`, `silence.py` and `scenes.py`. It verifies its render with `check.py` and `look.py`, composites a graphics layer with `overlay.py` (`--place` for its `--sparse` mode), and delivers through `render.py --template`

Other repos in the ecosystem — media-analysis-skill, transcription-skill, subtitle-skill, thumbnail-skill, color-grading-skill, motion-graphics-skill, qc-skill — read this repo's contract --json, its tools' --json output and doctor, the same way any agent framework would; this repo does not call into any of them. The dependency runs one way.

Contents Standalone, and in an ecosystem · Why · Quick start · How it works · Design principles · Tools · Audio · Built for agents · FAQ · FFmpeg compatibility · Tested on real footage · Install · Requirements · Development · Docs

Why

An agent that "knows FFmpeg" still guesses: it assumes a frame rate, picks a codec the container cannot hold, re-encodes a file that only needed a stream copy, and reports "done" without opening the result. This skill takes the guessing out:

Real files first. Every job starts with probe.py; the agent decides from the measured duration, fps, resolution, colour and audio layout, not from the file name.
Structured tools, not shell strings. Each operation is a script with typed arguments. Nothing runs through a shell; no filter graph is accepted from the caller.
A contract the agent can read. contract --json states, for every tool, what it takes, what it writes, which FFmpeg components it needs and how the result is verified. The MCP surface is derived from it.
Verification after execution. The result is probed, checked against the destination's spec and, when the picture changed, looked at as a contact sheet.
Local first. No cloud, no API keys, no Python dependencies. Optional local transcription is used when a speech engine (Parakeet or a whisper) is installed, never required — by caption.py --transcribe and, since 1.17, by silence.py --filler --transcribe; both take a transcript you already have instead, and both refuse with the install lines rather than guessing. Both take --engine and --model; FFMPEG_SKILL_ASR_ENGINE and FFMPEG_SKILL_ASR_MODEL set this machine's defaults (e.g. large-v3-turbo where that whisper.cpp model is installed; without either, auto and base). The engine runs on your machine and the audio is never uploaded; parakeet-mlx, faster-whisper and openai-whisper download their model weights the first time they run (parakeet-mlx's default from Hugging Face).

Paper

The design discipline behind this skill — per-operation verification for AI agents — is written up as a position paper. The paper scores ffmpeg-skill itself as only partially conformant with the discipline it defines.

Kajiyama, S. (2026). What Is Verification-Driven Coding? — A Discipline for Not Trusting Generation. Zenodo. https://doi.org/10.5281/zenodo.23720216

Quick start

# 1. install the skill for Claude Code (Cursor: --cursor, Codex: --codex, all three: --all)
npx ffmpeg-skill

# 2. check the machine: ffmpeg, ffprobe and every FFmpeg component the tools need
npx ffmpeg-skill doctor

# 3. (for agent frameworks) read the machine-readable contract
npx ffmpeg-skill contract --json | head -40

Already installed? re-run npx ffmpeg-skill to refresh ~/.claude/skills/ffmpeg-skill. Copies are not updated automatically.

doctor's overall ok and a single tool's usable: no are different signals: ok means nothing required by every tool is missing, but a plain Homebrew ffmpeg on macOS can still be ok while caption.py specifically can't run (no subtitles filter) — check doctor --json's tools field for the per-tool answer, not just ok.

Then talk to your agent:

"Take interview.mp4, keep 0:45–3:10 and 5:00–6:30, and make it exactly 60 seconds for Reels."

The agent runs probe.py, cut.py --segments 0:45-3:10,5:00-6:30, fit.py --duration 60 --aspect 9:16 --fit crop, export.py --preset reels, check.py --platform reels and look.py, then reports "final.mp4: 59.98 s, 1080×1920, 30 fps, AAC stereo" with the contact sheet it inspected.

Deliver to a platform

One command per destination, with the app's own UI taken into account:

python3 $S/render.py talk.mp4 --template tiktok --cues cues.txt

That fills the shipped templates/tiktok.json: 9:16 crop, captions popped word by word above TikTok's description bar and clear of its like column, −14 LUFS, the tiktok export preset, and a check.py --platform tiktok on the file it wrote. The caption size comes from the delivery table, so since 1.17.1 the filled project also states "fit_size": "on": a size nobody asked for shrinks to fit the cue instead of splitting the sentence across two cues. A template that states its own fit_size, and a --brand that states a caption size, still win, and a project with "fit_size": "off" renders the captions 1.17.0 rendered. Templates ship for tiktok, reels, shorts, youtube-shorts, youtube, x, linkedin, facebook and podcast; --template all (or a comma-separated list) renders every destination from the same edit and writes a <name>_pack.md table of what each one produced. Files land next to the input unless -o says otherwise, and --dry-run shows every planned command rather than a result. render.py --list-templates prints them with their frames, limits and safe zones. Alias spellings work everywhere a platform is named (youtube-shorts = shorts, ig = reels, twitter = x, fb = facebook).

The tools also work on their own, from any shell:

S=~/.claude/skills/ffmpeg-skill/scripts
python3 $S/probe.py input.mp4 --compact
python3 $S/fit.py input.mp4 --duration 60 --aspect 9:16 --dry-run    # print the plan, run nothing
python3 $S/export.py input.mp4 --preset reels --json                 # structured result with a probe of the output

On Windows in Git Bash, python3 is only on PATH if Python was installed from the Microsoft Store; a python.org install exposes python (or the py launcher) instead — replace python3 with python above if you see a "command not found". bin/install.js and doctor/contract already handle this for you; only the raw script examples above need it spelled out manually.

More requests and the commands behind them: examples/README.md. To see everything run end-to-end on generated footage: npm run demo (the gallery in docs/demos.md).

How it works

flowchart TD
    U[User request] --> A[AI agent<br/>Claude Code · Cursor · Codex]
    A -->|reads| S[SKILL.md<br/>workflow, request → tool map, report format]
    A -->|runs| T[Structured tool<br/>scripts/&lt;name&gt;.py, typed argparse flags]
    T --> C[Contract<br/>input schema · role · capabilities · verification policy]
    C --> D[Capability detection<br/>doctor: available / missing / unknown]
    D --> F[FFmpeg execution<br/>no shell, stream copy when possible]
    F --> V[Verification<br/>probe · check · look.py contact sheet]
    V --> R[Structured result<br/>--json: status, output, commands, probe]
    R --> A

Over MCP the same tools are reached through a transport that holds no tool table of its own:

flowchart LR
    M[MCP client<br/>Claude Desktop · Cursor · any client] --> P[mcp/server.py<br/>stdio JSON-RPC]
    P -->|tools/list| C[Contract-derived ToolSpecs<br/>names · order · inputSchema]
    P -->|tools/call| T[scripts/&lt;name&gt;.py]
    C -.derived from.-> K[scripts/_contract.py]
    T -.described by.-> K

Names, order and inputSchema in tools/list are translated from each tool's argparse parser at start-up, so a new flag or a new script appears in MCP with no edit to mcp/. A test copies the skill, adds, removes and edits a script, and reads tools/list again to prove it.

Design principles

These are the rules the skill file gives the agent and the code enforces.

Probe first. No tool decides from the file name. probe.py measures duration, fps (with variable-frame-rate detection), resolution, rotation, bit depth, HDR format including Dolby Vision, colour tags and every audio stream before anything is cut.
Lossless when possible. cut.py, join.py and loudness.py stream-copy what they do not need to touch. Re-encoding happens only when it must: frame-accurate cuts, filters, format changes, or a keyframe farther than the tolerance.
Plan before render. Every tool takes --dry-run (print the ffmpeg command lines, write nothing), --plan FILE (the dry run saved as a plan with fingerprinted inputs that render.py FILE executes later, refusing if an input changed), --json (structured result with a probe of the output), --fast (preview quality), --progress (percent and ETA), --timeout (a hung ffmpeg is killed and reported, never waited on forever; Ctrl-C or SIGTERM likewise stops the running ffmpeg, removes its partial output and reports kind: interrupted) and --overwrite (explicit consent before an existing output is replaced). A test runs every tool under --dry-run behind a fake ffmpeg and asserts that no ffmpeg call happened and no file appeared.
Machine-readable contract. contract --json describes all 42 tools: input schema generated from the parser, output schema, role, required and conditional FFmpeg capabilities, dry-run support, the verification tools to run afterwards, whether a visual check is required, mutates_input: false. provides lists all 42 by a cross-repository Capability id (ffmpeg-skill.cut, ffmpeg-skill.loudness, ...) for kajisho5/AI-video-production-OS's CapabilityContract.provides — see docs/contract.md.
Contract-derived MCP. mcp/server.py builds its tools/list from the contract. Tool names, order and inputSchema cannot drift from the scripts; a test keeps the two byte-identical.
Capability detection. doctor reads ffmpeg -encoders / -filters / -bsfs and reports which of the components the tools need are present on this build (libx264, libass, zscale, loudnorm, xfade, …), before a job fails inside ffmpeg.
Unknown is not missing. When a listing cannot be read (a layout the parser does not know, ffmpeg exiting non-zero) the affected capabilities are unknown: never missing, never silently available. An installed filter is not reported absent; a failed detection is not a pass.
Verify the result. The output is probed, and when the picture changed (captions, overlays, crops, colour, transitions) the agent runs look.py and inspects the PNG. The report is not finished until its Look: line names that image; audio-only jobs say Look: not needed. "Inspects" means the calling agent's own vision, not a feature of this skill: look.py only renders a PNG; nothing in this repository detects faces, products, subjects, or "the interesting part" of a frame or a scene. When a crop or reframe needs to keep a specific part of the frame (fit.py --fit crop --crop-x/-y, see Tools), it is the multimodal agent looking at that PNG and choosing the anchor — a non-visual caller (a script, a CLI user without eyes on the sheet) has to supply that decision itself, and the default is a plain centre crop. Likewise scenes.py --highlights ranks candidate scenes by a measured proxy (--rank-by audio or --rank-by duration), never by content; it is the agent that turns a look at the sheet into a judgement.
One label per report. A finished job is Done:, a failure or a refusal is Failed:, and a partial result is Done: with the shortfall named in Notes: — never a third label such as Done (partially):.
Keep originals. No tool overwrites its input. Outputs are new files named <input>_<operation>.<ext> unless told otherwise, and a test hashes every input after the run.

Tools

42 public tools, all Python 3.9 standard library, all with --help, --dry-run, --json, --plan FILE (a dry run written as a plan render.py executes later), non-zero exit and a reason on stderr on failure. --json-brief (1.11.0) prints the same result document trimmed to what a caller acts on — status, output, verified, a compact summary of the output probe, the tool's own keys and the command count instead of the command lines — for roughly a third of the bytes; --json itself is unchanged. Every re-encoding tool takes --codec h264|hevc|av1|prores and --quality N (1.8) and, with export.py, --hw/--no-hw (Apple VideoToolbox on Apple Silicon, opt-in; see doctor below), and every time flag takes seconds, mm:ss, hh:mm:ss.fff or SMPTE hh:mm:ss:ff with an optional @fps suffix (1.9).

Analysis and inspection

Tool	What it does
`probe.py`	Duration, fps (+ VFR detection), resolution, codecs, bit depth, HDR format incl. Dolby Vision (`hdr` for a real PQ/HLG/DV transfer only, `bt2020_or_hdr` also for BT.2020 SDR), colour space, rotation, every audio stream; `--analyze` flags Log footage
`scenes.py`	Scene changes, audio peaks, highlight proposals (`--rank-by audio` loudest, or `--rank-by duration` longest — both proxies, not "best") and a per-scene sheet; cut list for `cut.py --segments`; `--beats` measures the music's beat grid (tempo, beat times, confidence); `--shots` classifies each shot static/pan/motion by measured optical flow; `--audio-peaks` lists per-second dBFS; `--speech` reports a speech-vs-music ratio, not a classification
`look.py`	Contact sheet, single frames, side-by-side comparison as PNG so the agent can see what it made; `--safe NAME` shades the zones a platform's own UI covers

Editing

Tool	What it does
`cut.py`	In/out or multi-segment cuts, lossless `-c copy` first, re-encode fallback, `--accurate` for frame-exact video and sample-exact audio, `--keep-hevc` to re-encode an HEVC source as HEVC, `--edit-list` to keep an `.mp4` copy's start exact, `--vfr-guard` to choose the VFR check; reports `precision`; `--snap beats` moves the in/out points onto a measured beat, or refuses when there is no measurable pulse
`join.py`	Concatenate clips with xfade transitions, normalising size, fps, sample rate and channel layout (the widest clip's, or `--channels`); audio-only inputs are joined as audio; `--list parts.txt` reads generated segments (TTS lines) and checks every one first — missing, empty or unreadable parts are all named in one refusal, or skipped and reported with `--on-missing skip`
`silence.py`	Detect and remove dead air (jump cuts) with a margin around speech; list or export the cut list; `--filler` also removes filler words, but only where a speech engine timed them; `--speech-aware` keeps a breath inside a sentence and cuts only at sentence-boundary pauses, composing with `--filler` through the same `keep_ranges()`
`fit.py`	Fit to a duration (pitch-preserving speed change or trim, smooth slow-mo) and/or aspect ratio (pad, crop or `--fit blur`'s blurred, darkened fill, with `--crop-x`/`--crop-y` to keep an off-centre subject) and/or exact `--width`/`--height`; rotate 90/180/270, flip h/v; force constant fps
`crop.py`	Crop to an exact pixel rectangle (`--x --y --width --height`) — distinct from `fit.py --fit crop`, which crops to an aspect ratio it computes itself
`cropdetect.py`	Measure existing black letterbox/pillarbox bars and report the `crop.py`-ready rectangle that removes them — analysis only, writes no file; `--motion-centre` reports the per-second motion centroid instead, for a 9:16 reframe — report only, the calling agent picks the crop, no auto-reframing
`deinterlace.py`	Deinterlace interlaced source footage (`yadif`), `--mode frame`/`field`, `--parity`
`denoise.py`	Reduce video noise/grain (`hqdn3d`), `--strength low/medium/high` or individual spatial/temporal overrides
`redact.py`	Blur, pixelate, or (`--mode delogo`) interpolate away an exact pixel rectangle for the whole clip. The box is given: nothing here finds a face, a plate or a logo, and `delogo` is a border blend, not inpainting
`sphere.py`	Extract a flat rectilinear viewport from a 360/spherical video (`--yaw --pitch --roll --h-fov --v-fov`); no subject tracking, only the aim you give it
`straighten.py`	Rotate by an arbitrary angle for horizon correction (`--degrees`, `--fit crop/pad`) — distinct from `fit.py --rotate`'s exact 90-degree turns
`insert.py`	Turn a still image into a silent, fixed-duration video clip (title card, end slate) at an exact frame size / fps, with an optional Ken Burns zoom/pan
`background.py`	Generate a solid-colour or two-colour gradient clip at an exact size/duration — no input file
`reverse.py`	Reverse playback (video and, unless `--no-audio`, audio)
`stabilize.py`	Two-pass motion stabilisation (`vidstabdetect`/`vidstabtransform`)
`sequence.py`	Numbered (`frame_%04d.png`) or glob-matched still images into a video
`waveform.py`	Render an audio track as a waveform or spectrum visualization video (`showwaves`/`showspectrum`) — for audio-only inputs with no picture worth showing; `--image` puts the visualisation over a still plate (an audiogram), with `--platform`, `--title` and burnt-in captions
`freeze.py`	Hold a frame for N seconds (`--at`, `--hold`, `--mode insert/extend`) — an end-card hold or a comedic beat
`pad.py`	Add black/silent padding at the start and/or end of the timeline (`--start`, `--end`) — distinct from `fit.py --fit pad`'s per-frame letterbox bars
`speedramp.py`	Step through different constant speeds across a clip via `--segment START-END:FACTOR` (repeatable) — distinct from `fit.py`'s single whole-clip speed factor
`loop.py`	Repeat a clip `--times` N or to a target `--duration` — for background loops and filling a fixed slot length; `--boomerang` plays it forward then backward
`broll.py`	Cut away to a B-roll clip over the A-roll for a window (`--insert B --at T --duration D`, repeatable) and come back; A's length and audio untouched by default
`metadata.py`	Write container chapter markers from a `TIME TITLE` text file and title/artist/comment tags, every stream copied bit for bit; `--auto-chapters` proposes the markers from measured pauses and scene cuts and names them `Chapter N` for you to rename
`grid.py`	Composite `--cols`x`--rows` clips into one grid, each cell letterboxed and labelled with its filename by default (`--label none` to skip)

Audio

Tool	What it does
`audio.py`	Voice clean-up chain at three strengths (`--voice light\|medium\|strong`), FFT denoise, typed compressor / limiter / gate, music bed with sidechain ducking (`--duck-amount/-threshold/-attack/-release`), a never-ducked effects bed (`--effects`), `--stereo-widen`, fades, 5.1 → stereo, track replacement, extraction (`-o out.wav`), `--audio-stream N`
`sync.py`	Offset between two recordings by audio cross-correlation (1 ms, pure Python), clock-drift correction; aligned video or audio out (audio-to-audio only — no lip-sync/face detection); a third or later camera is an additional `more_sources` positional (`sync.py REF A B ...`), the `second` positional itself keeps its name
`loudness.py`	Two-pass EBU R128 `loudnorm` to −14 LUFS / −1 dBTP or any target (`--lra` for the range), video stream-copied; the written file is measured again and re-encoded until it meets `--tp` (lossy encoders overshoot); `--measure-only`

Picture

Tool	What it does
`caption.py`	Burn SRT/ASS with font, size, colour, outline, position; build SRT from timed plain text; wraps to the safe area — with `--platform`, the destination's own left/right safe zone, which since 1.17.2 is also what the burnt ASS states as its side margins — with a phrase-aware breaker (`--wrap phrase
`overlay.py`	Logos, watermarks and titles with position, time range, opacity, fades; `--platform NAME` keeps them clear of that destination's UI; `--video` for picture-in-picture, `--chromakey` for green-screen compositing, `--place` to lay ranges of a layer at chosen times
`graphics.py`	Lower-thirds, title cards, chapter chips, progress bars, countdowns, corner bugs, social stickers, opening hook cards and meme captions drawn by FFmpeg from a brand kit; `--platform NAME` keeps them inside that destination's safe zone; `--text-render` routes shaping scripts through libass and `--emoji-assets` composites colour emoji
`color.py`	HDR10 / HLG / Dolby Vision → SDR BT.709 tone mapping, DV layer stripping, 3D LUT (.cube), colour-tag rewriting, typed primary correction (exposure/contrast/saturation/gamma/white balance/lift-gain/levels/curves)

Delivery

Tool	What it does
`export.py`	Presets `youtube`, `youtube4k`, `reels`, `tiktok`, `shorts`, `linkedin`, `facebook`, `x`, `youtube-hdr` (HEVC Main10, source HDR tags kept), `youtube-av1`, `prores`, `h265`, `gif`, `copy`, all tagged BT.709 unless they carry HDR (a BT.601-matrix or RGB source is converted to BT.709, not relabelled); `--normalize` meets the platform's loudness in the same call (`render.py` turns it on by default for platform presets)
`proxy.py`	Small, low-bitrate proxy for downstream AI analysis/preview/editing decisions — resize by `--width`/`--scale`, proxy-grade `--quality` (default 30), `--fps`, `--no-audio`; not a delivery preset
`check.py`	PASS / WARN / FAIL against YouTube, Shorts, Reels, TikTok, X, LinkedIn, Facebook, broadcast and podcast specs, from the same delivery table the export presets and templates read (podcast also reports chapter markers and channel count), with the fix for each failure and a `format` / `judgement` kind per row; `--content` adds black-frame, frozen-frame, silence and short audio-dropout rows
`report.py`	Single-file HTML delivery report: before/after sheets, media facts, loudness, compliance, the commands run; `--pack` renders a social pack table

Orchestration

Tool	What it does
`render.py`	Render a whole edit from a declarative `project.json` (clips, transitions, captions, overlays including the social sticker/hook/meme graphics and a picture-in-picture or chroma-keyed `video` layer sized for the final frame, music and stem levels, loudness, export, chapter markers, check); `--init`, `--dry-run`, `--stop-after`, `--cache DIR`/`--from STAGE` (reuse the stages that did not change); the captions block takes the fit-size policy (`fit_size`, `min_size`, `fit_size_scope`) and the run reports the caption stage's counts as `caption`; `--template NAME INPUT` renders a shipped delivery template (`--template all` writes the whole social pack plus its table); `--export-timeline edit.fcpxml\|.edl\|.otio` (2.1) writes the cut as an editor timeline for Final Cut, DaVinci Resolve or Premiere instead of rendering it — clips, speed, dissolves, the music bed and chapter markers carry over, and `not_exported` names what stays behind
`batch.py`	Apply a step recipe or a project to a folder with a content-hash cache; `--watch`; `--jobs N` processes several files at once under one shared `--timeout`
`multicam.py`	Align any number of cameras and recorders by audio (with drift correction) and cut between them from a switch list; `--switch energy` auto-cuts to whichever camera is loudest/talking instead of a hand-built list, with `--min-shot` (minimum shot length) and `--edl` (write a plain START-END cut list, not a CMX EDL; for Premiere/Resolve use `--write-project` then `render.py --export-timeline`)
`verify.py`	Run the toolchain on real device files and report PASS / FAIL per step

Not tools, but part of the surface: mcp/server.py (the MCP transport) and scripts/_contract.py (contract --json, doctor). Per-flag reference for every tool: references/scripts.md.

Audio is a first-class input

WAV, FLAC, MP3, M4A/AAC, OGG and Opus go through probe, cut, join, silence, loudness, audio, sync and check --platform podcast with the same commands as video. The output extension picks the codec: -o out.wav writes PCM, -o out.flac FLAC, -o out.mp3 MP3, -o out.m4a AAC.

Extraction. An audio extension on a video input drops the picture: audio.py talk.mp4 -o talk.wav, or --voice -o talk.m4a to clean it on the way. --audio-stream N picks a track; probe lists them under audio_streams.
Join. join.py intro.wav episode.m4a outro.wav -o full.flac resamples every clip to one rate and channel layout and crossfades them (--transition none for a butt join). Audio and video inputs cannot be mixed in one join.
Sample-accurate trims. cut.py talk.wav --start 1.2345 --end 2.3456 --accurate trims at the sample; the JSON reports precision (packet for a stream copy, sample for PCM / FLAC, codec_frame when a lossy encoder frames the audio again, frame for video) and the measured duration_error_ms. A .wav never receives compressed packets.
Typed dynamics. audio.py --compress --comp-threshold -20 --comp-ratio 4, --limit --limit-ceiling -1, --gate --gate-threshold -45. Each flag is one documented option of FFmpeg's acompressor, alimiter or agate, range-checked before ffmpeg runs; no filter string is accepted from the caller.
Loudness. loudness.py talk.wav -I -16 --tp -1.5 -o talk.m4a for podcast levels; check.py talk.m4a --platform podcast measures LUFS and true peak and reports the chapter markers and channel count.

Picture tools (fit, caption, overlay, graphics, color, export, scenes, look) refuse an audio file with "input has no video stream" instead of inventing a picture.

Built for agents

What is SPEC?

SPEC (Self-Producing Execution Contract) is the name this project's author, kajisho5, gave the pattern the tool layer is built on: each tool's input_schema — the part of its contract that has to track the CLI exactly, flag for flag — is never hand-authored side by side with the code. It is derived, at run time, from the one thing that has to be correct for the CLI to work at all: the script's own argparse parser.

Concretely, scripts/_contract.py's _capture_parser() imports every tool script and intercepts its parse_args() call to get the live, fully-built parser object — flags, types, choices, defaults, required/positional, mutually exclusive groups, all of it. input_schema is built straight from that object. (The rest of a ToolSpec — role, capabilities, inputs, outputs, output_schema — comes from a hand-authored table, TOOL_META, since those facts aren't things a parser can express; only input_schema is parser-derived.)

The contract's input_schema for every tool is generated from the live parser directly.
SKILL.md is two-tier (1.11.0): the file the agent loads every session keeps the workflow, the request→script table and one line per gotcha; the long-form detail lives in references/gotchas.md and the other references/ files, read only when a job needs it.
The MCP server (mcp/server.py) carries no schema of its own; tools/list is translated straight from the contract, input_schema included.
The docs (docs/contract.md's field reference, this README's tool table) describe the same shape. tests/test_contract.py runs on every CI run and fails the build if any of them drift out of sync with what the code actually does — it catches drift, it doesn't fix it for you.

So adding a flag to a script's argparse block updates input_schema and the MCP tool definition with no second edit, and a docs page or TOOL_META entry that falls behind fails CI rather than drifting silently. There is no separate input_schema file to forget to update.

Machine-readable contract

npx ffmpeg-skill contract --json            # or: python3 scripts/_contract.py --json
npx ffmpeg-skill contract --json --static   # without environment detection

The contract is generated from the code that runs, not maintained beside it. For each of the 42 tools (ffmpeg-skill/<name>) it states:

Field	Meaning
`input_schema`	generated from the tool's argparse parser: properties, types, enums, defaults, required, positional order, mutually exclusive groups
`output_schema`	what `--json` prints: `status`, `output`, `commands`, `probe`, plus tool-specific fields (`precision`, `checks`, `offset_seconds`, …)
`role`	`analysis`, `analysis_and_execution`, `execution` or `verification`
`capabilities`	the FFmpeg encoders, filters and bitstream filters the tool always needs, and the ones needed only for a flag or input
`supports_dry_run`, `supports_json`, `supports_json_brief`	measured by the tests, not declared
`verification`	which tools to run on the output afterwards (`probe`, `check`, `look`)
`requires_visual_verification`	the picture changed; inspect the contact sheet
`audio_only`, `video_required`	whether an audio-only input is accepted or refused
`mutates_input`	always `false`
`idempotency_hint`	`bit_exact`, `content_equivalent`, `cached` or `environment_dependent`

Next to the tool list the document carries a top-level deprecated list (1.10): what 2.0.0 removes, since when, the replacement and the surface it lives on. docs/contract.md "What 2.0 changes" is written from it.

contract_version (1.0) is separate from the skill version, so a consumer can pin the shape and read the version for provenance. The document also states the invocation mapping (structured arguments → argv), the JSON shapes for success and failure ({"status": "failed", "error": {"kind": "input | ffmpeg | output | missing_tool | timeout | verification | interrupted", "message": …}}; a refusal adds error.reason, a stable code such as output_exists or plan_inputs_changed, and error.details, the facts behind it; warnings, fallbacks and skipped steps a run decided on its own are in diagnostics), and that no tool runs a shell or executes anything other than the named script, ffmpeg and ffprobe. Field-by-field reference: docs/contract.md.

MCP

{"mcpServers": {"ffmpeg-skill": {"command": "python3", "args": ["/Users/you/.claude/skills/ffmpeg-skill/mcp/server.py"]}}}

On Windows, python3 is only on PATH if Python was installed from the Microsoft Store; a python.org install exposes python (or the py launcher) instead — if your MCP client reports the server failed to start, change "command" above to "python" (or the full path from where python).

mcp/server.py is a stdio JSON-RPC transport with no tool table of its own. tools/list is derived from the contract at start-up, in contract order, with inputSchema translated from each tool's input_schema. By default it lists only the core 12 (render, look, caption, export, check, fit, cut, audio, loudness, graphics, silence, probe) so a client doesn't pay context for 30 schemas it rarely calls directly; set FFMPEG_SKILL_MCP_FULL=1 to list all 42. Every tool, listed or not, is callable through tools/call, which maps structured arguments to argv and runs the named script; a raw argv form is accepted for compatibility and marked non-canonical. python3 mcp/server.py --list prints the tools; --call probe '{"inputs": ["a.mp4"]}' runs one from the shell.

No inputSchema lists json or progress: they are transport flags the server sets itself, not tool arguments (2.0; 1.x listed them unless FFMPEG_SKILL_MCP_LEAN=1). A client that still sends them is not refused.

The server also advertises a prompts capability: five canned workflow recipes an agent can ask for by name instead of composing the individual tool calls itself — reel, podcast, multicam, delivery_check, hdr. prompts/list returns each one's name, description and arguments; prompts/get {name, arguments} fills the matching template and returns one text message naming the actual command lines to run and what to check afterward. These are recipes, not new tool calls — every command line a prompt names is one tools/call (or the CLI) can already run.

Capability detection

npx ffmpeg-skill doctor          # human-readable
npx ffmpeg-skill doctor --json   # available / missing / missing_optional / unknown / detection / errors / tools / gpu_encoders / ffmpeg_security

doctor reads ffmpeg -encoders, -filters and -bsfs and resolves every capability the contract declares against this machine's build. Three states per capability: available, missing, unknown. Exit 0 when everything required is available, 1 when something required is missing, 2 when nothing is proven missing but a required capability is unknown. With detection on (the default), contract --json carries the same lists under capabilities. doctor also reports fonts: the default drawtext family, and fonts.scripts — one available/missing/unknown per writing system (ja, zh, ko, ar, he, hi, th, ru, el) with the file it would use — so "can this machine render Korean captions" is answered before the job, not after. doctor --json's tools field folds that down to one answer per tool — {"caption": {"usable": "no", "missing": ["filter:subtitles"], "fix": "..."}, ...} — so "is doctor overall ok" and "can I run caption.py on this machine" are answered separately: a plain Homebrew ffmpeg is ok for tools that don't need subtitles/drawtext/zscale, while caption's own usable is "no".

doctor --json's gpu_encoders reports which GPU-backed encoders (nvenc, videotoolbox, qsv, vaapi, amf) this ffmpeg build was compiled with — read from -encoders alone, so it proves the capability shipped, not that the GPU/driver on this machine will actually accept a job (that needs a real encode, which doctor's introspection never runs). It never affects ok or any tool's usable. The one GPU path a tool uses is Apple VideoToolbox, opt-in: --hw on every re-encoding tool (h264/hevc/prores on the GPU, AV1 stays on SVT-AV1), FFMPEG_SKILL_HW=1 to make it this machine's default for everything except export.py's delivery presets (those take --hw explicitly, or render.py --hw for a whole project). It runs only on Apple Silicon and never by default, except on a build with no libx264/libx265 (an LGPL ffmpeg that has only h264_videotoolbox/hevc_videotoolbox): there it is the only encoder, so every re-encoding tool and export.py's presets use it without --hw, the result says hw.source: "auto", and doctor lists the missing x264/x265 under covered_by instead of calling the tools unusable (--no-hw still asks for the CPU). It falls back to the CPU encoder — reported in the result's hw.fallback/hw.notes — only when VideoToolbox itself refuses a job (any other ffmpeg failure is reported as it happened, not retried); every result names the encoder that actually ran (batch.py, which encodes nothing itself: each results row), and a GPU encode adds a notes line that its quality is not CRF-equivalent. Measured on an M4 Max, 2–7× faster than x264/x265 medium; at matched SSIM (tests/bench_vt.py) 1.1–2.9× the bytes on SDR and 1.9–3.5× on HDR, which is why it is a flag and not the default (docs/design-decisions.md). doctor --json's hw says whether this machine can run it (platform_ok) and whether it is the default here (default_on).

doctor also compares the FFmpeg version with a small, dated table of high-severity FFmpeg CVEs (doctor --json's ffmpeg_security; source ffmpeg.org/security.html, checked 2026-10-22). ffmpeg-skill does not ship FFmpeg, so a decoder bug in the build you have stays yours until you upgrade it. When the version is older than the first upstream release on its branch that carries a fix (today: CVE-2026-8461, the MagicYUV decoder heap write, fixed in 8.0.3, 8.1.2 and 9.0), doctor prints one warning: line on stderr with the update command and lists it under ffmpeg_security.warnings. It is advice, never a failure: ok, each tool's usable and the exit code do not change. A distribution package (Ubuntu 8.0.1-3ubuntu2, Debian +deb13u1) may carry the fix as a backport under the old version number, so the line says the build is older than the fix, not that it is vulnerable. A version it cannot read (a git snapshot such as N-12345-g…) gets no warning, and nothing is fetched: the table ships with the skill.

Gotchas and best practices

The short list for humans. The agent-facing version, with the reasoning, is the "Things that look right but are wrong" and "Gotchas" sections of SKILL.md.

Variable frame rate (phone and screen recordings). probe.py flags it; every re-encoding tool conforms to a constant rate automatically. cut.py switches to frame-accurate mode on it, because copy-cuts on VFR land on the wrong frame, and samples the frame timing to say whether that was needed (--vfr-guard sampled decides on the sample instead; --vfr-guard off keeps the copy). Choose the rate yourself with fit.py input.mp4 --fps 30 when the measured average is odd.
Lossless cuts snap to keyframes. A stream-copy cut can start up to one GOP earlier than asked. cut.py re-encodes when the snap exceeds 0.5 s (--tolerance changes the limit). For a strictly lossless file pass --tolerance -1 (plus --vfr-guard off on a variable-frame-rate source), and expect the cut to land on the nearest earlier keyframe; the JSON result lists them under nearest_keyframes. A --segments join can still be re-cut: open-GOP video, a later segment of B-frame .mp4/.mov video that starts between keyframes (start it on a keyframe, or write .mkv), or a join that fails its check. For a single .mp4/.mov cut, --edit-list keeps the copy and starts the picture where asked: the keyframe's pre-roll is stored but hidden by an MP4 edit list, which a player that ignores edit lists shows.
HDR stays HDR. When the probe reports HDR (HDR10, HLG, Dolby Vision, BT.2020), the tools keep it rather than flatten it. Convert deliberately with color.py --to-sdr before H.264 deliverables or LUT work. export.py platform presets are SDR and warn on HDR input.
Loudness targets. −14 LUFS / −1 dBTP for YouTube and social platforms (the loudness.py default), -I -16 --tp -1.5 for podcasts, -I -23 for broadcast. A clip measured at −40 LUFS or below is room tone, not content; raising it raises the noise. Check true peak as well as LUFS: check.py file --platform podcast measures both.
Frame changes first, text second. Captions and overlays burned before a crop or resize end up off-frame. Reframe, then caption.
Cropping 16:9 to 9:16 discards 70 % of the width. fit.py --fit crop centres by default; pass --crop-x/--crop-y toward the subject, or pad with --fit pad --pad-fill blur. Look at the contact sheet before deciding.
Phrase-aware caption breaking (1.16). caption.py/graphics.py --wrap phrase (the default) never breaks inside a word or on the wrong side of a hyphen, never leaves a lone digit, kana or punctuation pair on a line, prefers Japanese sentence ends and particles over a mid-word break, and never ends a line on an article or preposition. All four are penalties over break positions that already fit, so no line is widened and the line count never changes; --wrap measured restores 1.15's width-only wrap. The text itself is never rewritten or shortened. Since 1.16.1 a Thai run and a katakana word are never broken inside (Thai writes no space inside a phrase and there is no dictionary: the break goes where you put a space or |), and a line wider than the safe width is reported as overlong with the fix named.
Caption size fitted to the cue (1.17, reachable from the templates since 1.17.1). At a platform caption size a line holds about six em, so an ordinary sentence needs four lines and --max-lines 2 used to cut it into consecutive cues — half the sentence arriving late. caption.py --fit-size (default auto) walks the size down until every cue fits, before laying the cues out, with a legibility floor of 4.5 % of the frame height (--min-size, default 13 ASS units). An explicit --size or a brand.json size is a statement about the look and is never overridden — which in 1.17.0 also silenced the fitter on every render.py --template run, since a template fills the size from the platform table; 1.17.1 marks that size as the default it is ("fit_size": "on" in the filled project), so the type shrinks and no cue is split. --fit-size off restores that earlier behaviour byte for byte, and the caption text is still never rewritten to make it fit.
Beat-synced cuts (1.17). scenes.py --beats reports the measured grid — tempo, beat times, and a confidence built from how far the winning autocorrelation lag stands above the others and how many onsets land on it. cut.py --snap beats (and a "snap" block in a render.py project) moves in/out points to the nearest beat within --snap-tolerance — and only onto the grid points a measured onset actually marks, never onto the regular grid's continuation through a passage with no music in it. Below --min-confidence it refuses: a cut point may move to a measured beat and may not appear from one, so speech and ambience get an honest "no steady pulse here" instead of an invented grid.
Filler words (1.17). silence.py --filler --words transcript.json removes "um" and "uh" through the same cut graph the silences use. Never without measured word timings — there is no heuristic that finds an "um" without them that would not also cut real speech — and like, tipo and cioè are deliberately not in the built-in lists, because a discourse marker is a content word. Whisper stays optional: --transcribe with no engine installed refuses and names the five installs.
Throughput (1.17). batch.py --jobs N runs several files at once, capped at min(N, cpu_count, 8) and sharing one --timeout budget rather than one per item; the per-item table keeps its order. render.py --cache DIR reuses stages whose inputs and arguments did not change, so swapping an export preset re-runs export only. The cache is opt-in with no default directory, and the ffmpeg, skill and contract versions are part of every key, so a cache is never reused across them.
Audiogram (1.16). waveform.py --image cover.png (or render.py --template audiogram) puts the waveform over a still plate for an episode that has no picture, with --platform for the frame, --title and burnt-in captions. The image is a local file you give: nothing is fetched and no cover art is ever invented.
Emoji in captions and titles (1.15). caption.py/graphics.py --emoji-assets DIR composites a PNG per emoji (Twemoji/Noto naming, 1f389.png) on top of the text, because drawtext cannot load a colour emoji font at all and an installed one does not prove libass will draw it in colour — doctor --json's fonts.emoji answers that from a render probe. Without assets the run still succeeds and reports mode: mono. Nothing is ever downloaded.
Indic and Thai text shaped correctly in titles and lower-thirds (1.15). graphics.py renders Devanagari, Bengali, Tamil, Thai and Lao through libass automatically (text_renderer: "ass"), because drawtext never reorders matras or re-clusters marks; Arabic and Hebrew were already correct on a fribidi build. --text-render drawtext with such a script is refused, never rendered wrongly.
Non-Latin text picks a font by script (1.12). Japanese, Chinese, Korean, Arabic, Hebrew, Devanagari, Thai, Cyrillic and Greek cues, titles and overlays resolve a font file that covers them automatically, and a machine with no such font fails the job (kind: input) instead of rendering boxes. doctor --json's fonts.scripts says which languages this machine can render; --lang ja|ko disambiguates Han-only text; an explicit --font/--font-file is always kept.
Silence detection finds nothing? The default threshold is −35 dBFS. The tool prints a hint with the track's measured level; raise the threshold (silence.py --threshold -25) or shorten --min-silence.
Sync results carry a confidence. Below 0.3, or an offset near the edge of the analysis window, is probably wrong: enlarge --analyze-seconds or find a clap. Recordings over ten minutes from separate devices need sync.py --fix-drift.
Outputs are never overwritten silently. An existing output path is refused (kind: input, error.reason: output_exists, before anything runs); pass --overwrite where a replacement is intended.
Nothing happens silently. A refusal names its error.reason and error.details; a stale output lock taken over, a run without a lock, a plan input nobody could fingerprint, a fallback (an odd-sized frame scaled to even, a substituted font, libaom for SVT-AV1), a skipped step and every warning are recorded in diagnostics and on stderr (codes: json_output.diagnostic_codes); tools that draw text name the font they drew with (font_used). A refused plan and a lock takeover also append a line to an audit log (<plan>.audit.jsonl, or render.py --audit-log FILE). A degraded or partial result says so: render.py's verified is every stage's, an unmeasured loudness or a report section that could not be built is a failing verification step, export.py reports trimmed, multicam.py's cuts name the camera rendered, and batch.py rows carry the failing step's error, verified, and a row for every input after Ctrl-C.
Long masters need a longer --timeout. The default is 1800 s per ffmpeg call, which is there to catch a hang. A full re-encode of an hour of 1080p at the default preset ran past it on a 4-core machine (see A one-hour recording). Pass --timeout 0 or a larger value, or --fast. Analysis and stream-copy cuts are far below it.
Long chains belong in a plan. Three hand-chained re-encodes lose quality and are hard to change; render.py runs the whole edit from one JSON file, and --dry-run shows every ffmpeg command before anything is written.

FAQ

Can I pass raw ffmpeg arguments, or a filter_complex?

Each operation is a script whose flags are its argparse parser, and that parser is what contract --json and the MCP inputSchema are built from. CI fails the build when those drift from the scripts. A caller-supplied filter graph, -vf string, or other ffmpeg option is not an argument any script accepts. doctor reads ffmpeg -filters (and -encoders, -bsfs) and reports a filter a tool needs before a job runs into it. MCP does accept an argv array, and that array is the named script's own CLI flags, still bound to that script: the script's flags, never an ffmpeg command line and never a shell.

How does ffmpeg-skill compare with Remotion?

ffmpeg-skill edits footage you already have, on this machine, with FFmpeg tools whose flags are a machine-readable contract (contract --json). Remotion renders a video from React components. They complement each other: this repository does not call Remotion, and a file Remotion writes is an ordinary input (probe.py, cut.py, fit.py, export.py, check.py). remotion-ffmpeg-skill joins the two. It measures the footage with this repo, draws the graphics in Remotion from those measurements, and checks and delivers the result with this repo again. Install both (npx ffmpeg-skill, then npx remotion-ffmpeg-skill). As with the other skills, the dependency runs one way: it calls these tools, and nothing here calls it. HyperFrames renders a video from HTML and is the same kind of neighbour: a file it writes is an ordinary input too.

Does it validate the filter graph before running?

No. --dry-run prints the ffmpeg command a writing tool would run and does not start ffmpeg, so ffmpeg never parses that filter graph. ffprobe still runs when the tool probes an input that exists. Unless --overwrite is passed, an existing output is refused (kind: input) before ffmpeg. An output that is the same file as an input is refused either way. No media file is written, and verified is false; --plan FILE does write the plan JSON. A path that is not on disk yet is not measured: the probe is a stub (0×0, duration 0), so a number taken from it is not a measurement. The measuring tools (probe, check, sync, multicam, scenes, cropdetect, report, silence, loudness, stabilize) still run their measurements; only the artifact is skipped, and silence with --filler --transcribe also runs the speech engine, which may download a model. overlay measures the lines of a multi-line --text with one drawtext on a generated frame on FFmpeg 8.0, 8.0.1 and 8.1.0, where it draws them one by one. verify accepts --dry-run and ignores it: its steps run. --plan FILE is that dry run written down — the planned argv, the command lines, and a fingerprint of each existing input (size, plus a sha256 of the first and last 8 MiB). render.py and verify.py refuse --plan. render.py FILE runs a plan only when those fingerprints still match (a mismatch is refused with each input's expected and actual fingerprint in error.details, and logged to <plan>.audit.jsonl), by running the tool again with the planned argv, not by executing the recorded ffmpeg lines; --dry-run on that run only prints them. doctor reports whether the filters, encoders and bitstream filters the contract names are in this build (ffmpeg -filters, -encoders, -bsfs) and which fonts are installed. A name in that listing is not a check of the graph this command would build, and a listed GPU encoder is not a successful encode.

Can it remove burned-in subtitles or logos?

A rectangle you already know, sitting still for the whole clip, can be filled from the picture around it with redact.py --mode delogo (ffmpeg's delogo filter). That is a 1 px border blend. It is not inpainting: a logo that moves, or burned-in subtitles whose position changes, is out of scope. The same known box can be obscured instead, with --mode blur or --mode pixelate. None of the three finds the box. delogo also needs the box at least 1 px inside the frame, and it only interpolates 8-bit YUV, so an HDR or 10-bit source is refused until color.py --to-sdr.

Does it auto-track faces or subjects for reframing?

fit.py --fit crop centres the crop unless you pass --crop-x and --crop-y. crop.py cuts the rectangle you pass. cropdetect.py measures letterbox bars, and --motion-centre reports where motion sat each second; it writes no file and picks no crop. sphere.py points a 360 view where you aim it. look.py writes a contact sheet. Nothing in this repository detects a face, a product or a subject. Choosing what to keep in frame is the caller's.

Does it encode on the GPU?

The encode path a tool will actually take is Apple VideoToolbox, for h264, hevc and prores, and only on Apple Silicon. --hw asks for it on that call. FFMPEG_SKILL_HW=1 makes it the default for every re-encoding tool except export.py's delivery presets, which take --hw on their own (or render.py --hw for a whole project). AV1 stays on SVT-AV1. nvenc, QSV, VAAPI and AMF are reported by doctor --json's gpu_encoders when the ffmpeg build was compiled with them, and no tool encodes with them. One build is the exception to opt-in: with no libx264 and no libx265, and with h264_videotoolbox or hevc_videotoolbox, those encodes use VideoToolbox without --hw and the result says hw.source is "auto". --no-hw still asks for the CPU encoder. A VideoToolbox job the hardware refuses is re-encoded on the CPU and the result says so (hw.fallback).

FFmpeg compatibility

The tools need FFmpeg 5.0 or later and Python 3.9 or later (standard library only). FFmpeg 6.1+ is recommended: FFmpeg 5.x still works but is deprecated since 2.14.0, doctor and the npx installer warn on it, and 3.0 requires 6.1. Python 3.10+ is recommended too: 3.9 still works but is deprecated since 2.10.9 (CPython 3.9 is end of life), doctor and the npx installer warn on it, and 3.0 drops it. What CI actually exercises on every pull request is FFmpeg 5.1.1 (static build), 6.1 (Ubuntu apt), 7.1 (Debian trixie apt), 8.x (macOS Homebrew) and 9.x (Windows gyan.dev), on Python 3.9 and 3.13 (the two ends of the supported range). The capability parser has been run against the listings of these builds:

FFmpeg	`-filters` row layout	Source
5.1.1	three flag characters, same as 6.x	johnvansickle.com static build on the Linux CI runner
6.1.1	three flag characters: `..C acompressor A->A`	Ubuntu 24.04 apt, captured
7.1.x	same as 6.x	Debian trixie apt in a CI container (plus a constructed fixture in tests/)
8.1.2	two flag characters: `TS aap AA->A`, three-character legend, `------` separator	Homebrew on the macOS CI runner, captured
9.0.1	same as 8.x, CRLF	gyan.dev build on the Windows CI runner, captured

FFmpeg 8 shortened the flag column of ffmpeg -filters. A parser anchored on the old width matches nothing on FFmpeg 8 and, if "nothing matched" is read as "nothing installed", reports every filter missing; that is what 0.9.0 did on macOS and Windows. Since 0.9.1 rows are recognised by their io-spec token (A->A, AA->A, |->V, N->N), so the flag width, the legend and the separator do not matter, and a listing that still cannot be read yields unknown rather than missing. The captured listings live in tests/fixtures/ with their provenance; CI uploads each runner's listing and doctor --json as an artifact so a new layout is visible before it bites.

Tested on real footage

What is tested where. The contract and the test suite (tests/test_contract.py, tests/test_all.py, which aggregates one module per tool group — test_analysis.py, test_editing.py, test_audio.py, test_picture.py, test_delivery.py, test_orchestration.py, test_accel.py, test_asr.py, test_cut_copy.py — over the shared footage in tests/_fixtures.py) run on Linux, macOS and Windows on every pull request, minus the handful of POSIX-shim tests listed under Development. The real-device media corpus (tests/corpus.py) has been run on Linux and macOS; the full corpus has not been run on Windows yet, and neither has an install by someone other than the maintainer been reproduced there — issue #143 tracks both. Treat the numbers below as measured on Linux (and, where stated, macOS), not as a claim about every file type on every OS.

Result	Measurement
92 / 92	verification steps on a 10-file real-device corpus (GoPro, DJI, iPhone incl. Dolby Vision, Android screen recordings, HDR10, 24p, Tears of Steel), 0.8.0, local ffmpeg 6.1
40 / 40 within 10 ms	`sync.py` offset detection, ±30 s offsets with gain, noise and EQ changes on real dialogue and music, 120 s windows (max error 1.1 ms); 60 s stress windows 95 % within 10 ms, 4 of 5 misses flagged by confidence
0 missed gaps	`silence.py`, 20 cases with known gaps, ≤ 1 ms leftover silence
F1 0.97	`scenes.py`, 53 hard cuts between single takes, precision 0.95, recall 1.00 at the default threshold
exact to the sample	`cut.py --accurate` on WAV, FLAC (44.1 kHz) and AAC → WAV; WAV stream copy within 2 ms; AAC output +21 ms of encoder priming, reported as `codec_frame` (0.9.1)
72 / 72	agent runs of 24 prompts (12 English edits, 8 Japanese, 4 that must be declined), three repeats, graded by an independent model: routing, honest refusals and user's language 72/72, report format 71/72, visual check whenever the picture changed 24/24 (0.8.4)
7 / 8 routed	eval 22 at 1.18.3 (2026-09-17, eight new symptom-only prompts for the five 1.18.0 flags, naming no flag, plus three repeats each of `cs1`/`cs3`, Sonnet agent): after 1.18.1's SKILL.md routing rows and 1.18.2's matching README rows, `scenes.py --shots`, `cropdetect.py --motion-centre`, `silence.py --speech-aware`, `sync.py`'s N-source form and `multicam.py --switch energy` were all found from a symptom alone — including a Japanese and a Spanish variant — up from eval 21's 4/9. The eighth prompt (`--filler` composed with `--speech-aware`) is an honest partial: the fixture has no real speech to transcribe, confirmed by installing `faster-whisper` mid-eval and re-testing by hand. `cs3` no longer rewrites the user's captions in any of 3 runs — the 1.17.3 fix holds through 1.18.1-1.18.3. Written up in `evals/results/iteration-22.json`
8 / 12 routed	eval 21 at 1.18.0 (2026-09-17, one prompt per new analysis/multicam flag plus a `cs1`/`cs3` recheck, Sonnet agent): every result was correct where the agent found the right script — both sync offsets, the shot label, the multicam switch point and its render-project mapping, both 1.17.2/1.17.3 rechecks — but 4 of 12 prompts hit a script the agent could not find, because SKILL.md named none of the five new flags (confirmed by grep). Three refused honestly rather than fabricate a number; one reached a correct answer without the intended flag, by luck of one fixture's silence durations. 1.18.1 (2026-09-17) is the SKILL.md fix, since evaluated by eval 22. Written up in `evals/results/iteration-21.json`
20 / 20	1.17.2 run (2026-09-14, the eight caption prompts of eval 19, six of them three times, Sonnet agent, regex grader + an Opus grader that opened every contact sheet and counted the lines per cue): routing 19/19 act runs, honest 18/20 with 0 false successes and 0 raw ffmpeg calls, report format 20/20, user's language 20/20, trigger set 49/50 (one judge flip on a file-less prompt), Opus quality mean 4.25 (3.65 at eval 19). The picture is fixed: 0/20 runs stack one word per line against 12/12 template runs at eval 19 on the same cues; the Style row at TikTok geometry is now `…,54,151,420,1`, the fitter's `size_used` (15 on TikTok, 16 on Shorts, the 13 floor for the Spanish cues) is what the frame shows, and report and sheet agree in 18/20 runs. What is left is not the typesetter: `cs3` rewrote the user's captions for the fourth iteration running, one `cs1` run raised `max_lines` to 4 to avoid a shrink and drew four-line stacks, and `cs2`'s 32-letter Spanish word still leaves the frame at the size floor (disclosed 3/3). Written up in `evals/results/iteration-20.json`
26 / 26	1.17.1 run (2026-09-14, targeted re-run of the 18 prompts eval 18's follow-up named, plus three repeats each of the four caption-size prompts, Sonnet agent, regex grader + a full Opus grader over all 26 runs, every PNG opened and every written output re-probed): routing 23/26, honest refusals and failures 24/26 with 0 false successes and 0 raw ffmpeg calls, report format 26/26 with the third label gone, user's language 26/26, trigger set 50/50, Opus quality mean 3.65. 1.17.1's fix holds — `--fit-size` now fires on the `render.py --template` path in 12/12 caption runs (24 → 16, `dl4` to the 13-unit floor, `split` 0, `text_unchanged` true, identical across repeats), the beat, filler and `--jobs` prompts route on the first try, and `bt2` quotes its measured 0.184 confidence instead of denying the capability exists. The honest part: the picture is unchanged. `caption.py`'s `write_ass` writes the platform's vertical safe margin into `MarginL`, `MarginR` and `MarginV` alike (tiktok 63 ASS units → 420 px), so at `PlayResX` 1080 the text column is 240 px and libass wraps every word — the fitter budgets `play_w × 0.9`, which is why `split: 0` is true of the ASS text and false of the frame. It is the `--animate`/`--karaoke` path only, present since 1.14, and it explains eval 17's and eval 18's "one word per line" too; 1.17.2 is the patch and the finding is written up in `evals/results/iteration-19.json`
100 / 100	1.17.0 run (2026-09-14, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 28 runs, every written output re-probed, `check.py` re-run on every delivery output) on the set grown to 100 prompts (caption size fitting, beat-synced cuts, filler removal, batch `--jobs`, render `--cache`): routing 95% over the 64 act prompts, honest refusals and failures 22/25 with 0 false successes and 0 raw ffmpeg calls, report format 98/100 (two runs label an honest partial result with a third label), user's language 100/100 across seventeen languages, visual check 24/24, real execution 6/6 with honest failure 5/5, trigger set 50/50 including all five new 1.17 prompts, Opus quality mean 3.71. The honest part: `--fit-size` is unreachable on the template path (`render.py` forwards the platform table's caption size as an explicit `--size`, so the fitter declines to shrink a size it thinks the user chose, and the project schema rejects `fit_size` outright — only the one run that called `caption.py` by hand got 24 → 16, `split` 0), and SKILL.md names none of the 1.17 features, so beats, filler and `--cache` were each used in one run at most — 1.17.1 is the patch and the finding is written up in `evals/results/iteration-18.json`
90 / 90	1.16.0 run (2026-09-14, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 30 runs, chapters and subtitle streams re-probed, check.py re-run on every delivery output) on the set grown to 90 prompts (audiogram, auto chapters, multi-language tracks, caption breaking): routing 90/90, honest refusals and failures 90/90 with 0 false successes and 0 raw ffmpeg calls, report format 89/90 (one `Done (partially):`), user's language 90/90 by regex (89/90 by Opus), audiogram 2/2 with the cover behind the waveform and nothing fetched, auto chapters 2/2 with `Chapter N` titles only, delivery 16/16 platform pass, trigger set 45/45, Opus quality mean 4.17. The honest part: the phrase breaker never gets to act at the platform caption sizes (a five-word cue does not fit two lines at TikTok size, so the split is byte-identical to 1.15.1), Thai still breaks inside words, and a katakana word was split — 1.16.1 is the patch and the finding is written up in `evals/results/iteration-17.json`
82 / 82	1.15.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 28 runs, stills extracted inside the emoji window, check.py re-run on every delivery output) on the set grown to 82 prompts (emoji captions and title cards, a Hindi and a Thai lower-third): routing 82/82, honest refusals and failures 82/82 with 0 false successes and 0 raw ffmpeg calls, report format 82/82 (both iteration-15 label defects closed: `dl8` and `he2` now carry one `Failed:`), user's language 82/82 by regex (81/82 by Opus: one Spanish report with three English labels), non-Latin glyphs 11/11 (Devanagari through `graphics.py` is fixed; Thai lower-third and captions correct), emoji visible in colour in 3/3 runs given PNG assets and reported monochrome in the one that was not, visual check 23/24, delivery 12/13 one encode and 13/13 platform pass, trigger set 40/40, Opus quality mean 4.68. Still open: the caption breaker splits phrases (`dl1`, `dl4` unchanged) — queued for 1.16.0. Tokens per run flat at 73.3k on the same 76. Details in `evals/results/iteration-16.json`
76 / 76	1.14.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 26 runs, check.py re-run on every delivery output): routing 76/76, honest refusals and failures 76/76 with 0 false successes and 0 raw ffmpeg calls, report format 76/76, user's language 76/76 by regex (75/76 by Opus: one Spanish report with three English labels), visual check 18/18, trigger set 38/38, Opus quality mean 4.58. The delivery templates did their job: 12 of 13 delivery requests went through `render.py --template`, finished in one encode (was 3 of 7) and all 13 pass their platform check (was 7 of 8). Tokens per run flat at 73.4k. Details in `evals/results/iteration-15.json`
76 / 76	1.13.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader) on the set grown to 76 prompts: 18 in Thai, Hindi, Hebrew, Russian, Greek, Vietnamese, Indonesian, Turkish and Italian, and 8 delivery requests (TikTok, Reels, Shorts, LinkedIn, Douyin, podcast): routing 76/76, honest refusals and failures 76/76 with 0 false successes and 0 raw ffmpeg calls, report format 76/76, user's language 76/76 across seventeen languages, visual check 18/18, trigger set 38/38, Opus quality mean 4.65 over the 26 new runs. One real defect found: Hindi through `graphics.py` (drawtext) comes out wrong-shaped even though the font covers Devanagari; captions through libass are fine (queued for 1.15.0). Four delivery runs spent a second encode for loudness, which 1.14.0's templates address. Tokens per run flat at 72.3k. Details in `evals/results/iteration-14.json`
50 / 50	1.12.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader) on the set grown to 50 prompts with two each in Chinese, Korean, Spanish, Portuguese, French, German and Arabic: routing 50/50, honest refusals and failures 50/50 with 0 false successes and 0 raw ffmpeg calls, report format 50/50, user's language 50/50 across nine languages, visual check 13/13, trigger set 29/29, Opus quality mean 4.83. Every non-Latin caption and lower-third picked a covering font by itself and rendered real glyphs (Arabic shaped and right-to-left); tokens per run unchanged at 72.3k. Details in `evals/results/iteration-13.json`
36 / 36	1.11.1 re-run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader): routing 36/36, honest refusals and failures 36/36 with 0 false successes and 0 raw ffmpeg calls, report format 36/36, user's language 36/36, visual check 8/8, trigger set 22/22, Opus quality mean 4.75. The 1.11.1 wording did what it said (`doctor` before a job 23 of 36 runs → 0, `--json-brief` 4 → 23) and tokens per run stayed flat at 71.8k, because about 64k of every run is the host's own context; the token-diet theme closes here. Details in `evals/results/iteration-12.json`
36 / 36	1.11.0 re-run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader): routing 36/36, honest refusals and failures 36/36 with 0 false successes and 0 raw ffmpeg calls, report format 36/36, user's language 36/36, visual check 8/8, trigger set 22/22. First iteration to measure tokens per run: mean 72.2k against 68.7k at 1.10.0, mostly a fixed per-run floor the skill does not control (a refusal run that only reads SKILL.md costs about 64k), plus `doctor` on 23 of 36 runs; 1.11.1 rewords step 0 and iteration 12 re-measures. Details in `evals/results/iteration-11.json`
108 / 108	1.10.0 re-run (2026-09-13, three passes per prompt, Sonnet agent, regex grader + focused Opus grader): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, report format 108/108 by both graders (the harness now names the five labels), user's language 105/108 (every Japanese request in Japanese; 3 English requests drifted to Spanish or Portuguese), visual check 21/24, trigger set 22/22; real-device corpus 101/101 steps PASS. Details in `evals/results/iteration-10.json`
108 / 108	1.9.0 re-run (2026-09-13, three passes per prompt, Sonnet agent, regex grader + independent Opus grader): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, visual check 23/26, user's language 105/108 (every Japanese request answered in Japanese; 3 English requests drifted to Spanish), report format 108/108 by regex (65/108 by the stricter grader, which now counts any missing label), trigger set 22/22; 12 of 15 platform jobs were one encode and `render.py` rendered once in 3/3 (was 1/3). The 1.9.0 time grammar was not used by any agent. Details in `evals/results/iteration-9.json`
108 / 108	1.8.0 re-run (2026-09-12, three passes per prompt, Sonnet agent, regex grader + independent Opus grader that re-probed 22 outputs): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, visual check 22/24, report format 108/108 by regex (91/108 by the stricter grader: 'What/how:' in place of Steps:), user's language 98/108 by the stricter grader (Japanese labels-only reports counted), trigger set 22/22; 13 of 14 platform exports used `--normalize` and platform jobs went from three encodes to one; r04/f01 now answered in the request's language 5/6 (was 0/6). Details in `evals/results/iteration-8.json`
108 / 108	1.7.0 re-run (2026-09-12, three passes per prompt, Sonnet agent, regex grader + independent Opus grader that re-probed 24 outputs): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, visual check 25/25, report format 108/108 by regex (104/108 by the stricter grader), user's language 101/108 (six English refusals answered in Spanish or Portuguese, one Japanese request in English); trigger set 22/22. Iteration-6 fixes held (fade-in only, Japanese audio trims, music no longer shortens the video). Details in `evals/results/iteration-7.json`
36 / 36	1.4.15 re-run (2026-09-12, one pass per prompt, Sonnet agent, regex grader + manual review): 24-prompt set routing 20/20, honest refusals 5/5, visual check 8/8, report format 25/25, user's language 9/9; exec set real execution 6/6, honest failure on bad inputs 5/5 with 0 false successes, audio-as-audio 3/3, one Japanese report with English labels; trigger set 22/22. Both iteration-5 defects gone (no raw ffmpeg fallback, music no longer shortens the video). Details in `evals/results/iteration-6.json`
36 / 36	1.4.0 re-run (2026-09-11, one pass per prompt, Sonnet agent, regex grader + manual review): 24-prompt set routing 20/20, honest refusals 5/5, visual check 8/8, user's language 8/9; exec set real execution 6/6, honest failure on bad inputs 5/5 with 0 false successes, audio-as-audio 3/3; trigger set 22/22. Details and the six findings in `evals/results/iteration-5.json`
6 / 6	0.9.1 audio evals (audio join, extraction, track selection, sample-accurate trim, typed dynamics; 2 in Japanese): routing, report format and audio-as-audio handling 6/6

python3 tests/corpus.py --fetch --verify     # ~1.4 GB download, then verify (slow on 4K)
python3 tests/bench_sync.py --cases 100
python3 tests/bench_silence.py
python3 tests/bench_scenes.py

Benchmarks live in tests/bench_*.py, agent evals in evals/, results by iteration in evals/results/.

A one-hour recording

Measured at 2.17.0 (2026-10-25) on one 60-minute 1080p30 H.264 file (4.3 GB, AAC mono 48 kHz, synthetic testsrc2 picture and a tone). The machine was a 4-vCPU Xeon at 2.1 GHz with FFmpeg 6.1.1 on Linux, and each tool ran alone. The analysis rows read the whole hour (silence.py, scenes.py and check.py report 3600 s). The edit and export rows used 10-minute segments cut from it with stream copy, and the last row is the whole hour.

Step	Wall time	Note
`probe.py`	0.3 s
`silence.py --list`	14 s	one audio decode
`loudness.py --measure-only`	66 s
`look.py` (contact sheet)	97 s
`scenes.py`	241 s
`check.py --platform youtube --content`	308 s	the rows, `dropouts` included, cover the full hour
`cut.py`, a 10-minute stream-copy segment	2.4–4.4 s	no re-encode
`join.py`, two 10-minute segments	624 s	`join.py` always re-encodes, so it can crossfade and conform
`export.py --preset youtube`, a 10-minute segment	514 s	about 1.2× real time at the default `medium` preset
`export.py --preset youtube --fast`, the same segment	162 s	about 3.7× real time at `veryfast`
`export.py --preset youtube`, the whole hour	killed at 1802 s	`kind: timeout` at the default `--timeout` of 1800 s (below)

A full-length re-encode of an hour does not fit in the default --timeout. The limit is 1800 s per ffmpeg call. On this machine export.py --preset youtube on the whole hour was killed at 1802 s with kind: timeout (exit 124), and nothing was written. At the 10-minute segment's rate it would have needed about 50 minutes. A whole-hour join.py would land

You want to...

Use

Cut / join / measure / export a file right now

this repo (ffmpeg-skill), standalone

Decide cut points, approve a deliverable, plan a whole edit

video-production-agent / AI-video-production-OS

Build a typed editing graph across a workspace, without writing raw ffmpeg

video-editing-skill / audio-production-skill

Put Remotion motion graphics (animated captions, silence jump cuts, audio-peak motion) on a real recording

remotion-ffmpeg-skill: its compositions take their length, fps, frame, silences, scene cuts, audio peaks and beats from this repo's probe.py, silence.py and scenes.py. It verifies its render with check.py and look.py, composites a graphics layer with overlay.py (--place for its --sparse mode), and delivers through render.py --template

# 1. install the skill for Claude Code (Cursor: --cursor, Codex: --codex, all three: --all) npx ffmpeg-skill # 2. check the machine: ffmpeg, ffprobe and every FFmpeg component the tools need npx ffmpeg-skill doctor # 3. (for agent frameworks) read the machine-readable contract npx ffmpeg-skill contract --json | head -40

S=~/.claude/skills/ffmpeg-skill/scripts python3 $S/probe.py input.mp4 --compact python3 $S/fit.py input.mp4 --duration 60 --aspect 9:16 --dry-run # print the plan, run nothing python3 $S/export.py input.mp4 --preset reels --json # structured result with a probe of the output

flowchart TD U[User request] --> A[AI agent Claude Code · Cursor · Codex] A -->|reads| S[SKILL.md workflow, request → tool map, report format] A -->|runs| T[Structured tool scripts/<name>.py, typed argparse flags] T --> C[Contract input schema · role · capabilities · verification policy] C --> D[Capability detection doctor: available / missing / unknown] D --> F[FFmpeg execution no shell, stream copy when possible] F --> V[Verification probe · check · look.py contact sheet] V --> R[Structured result --json: status, output, commands, probe] R --> A

flowchart LR M[MCP client Claude Desktop · Cursor · any client] --> P[mcp/server.py stdio JSON-RPC] P -->|tools/list| C[Contract-derived ToolSpecs names · order · inputSchema] P -->|tools/call| T[scripts/<name>.py] C -.derived from.-> K[scripts/_contract.py] T -.described by.-> K

Tool

What it does

probe.py

Duration, fps (+ VFR detection), resolution, codecs, bit depth, HDR format incl. Dolby Vision (hdr for a real PQ/HLG/DV transfer only, bt2020_or_hdr also for BT.2020 SDR), colour space, rotation, every audio stream; --analyze flags Log footage

scenes.py

Scene changes, audio peaks, highlight proposals (--rank-by audio loudest, or --rank-by duration longest — both proxies, not "best") and a per-scene sheet; cut list for cut.py --segments; --beats measures the music's beat grid (tempo, beat times, confidence); --shots classifies each shot static/pan/motion by measured optical flow; --audio-peaks lists per-second dBFS; --speech reports a speech-vs-music ratio, not a classification

look.py

Contact sheet, single frames, side-by-side comparison as PNG so the agent can see what it made; --safe NAME shades the zones a platform's own UI covers

Tool

What it does

cut.py

In/out or multi-segment cuts, lossless -c copy first, re-encode fallback, --accurate for frame-exact video and sample-exact audio, --keep-hevc to re-encode an HEVC source as HEVC, --edit-list to keep an .mp4 copy's start exact, --vfr-guard to choose the VFR check; reports precision; --snap beats moves the in/out points onto a measured beat, or refuses when there is no measurable pulse

join.py

Concatenate clips with xfade transitions, normalising size, fps, sample rate and channel layout (the widest clip's, or --channels); audio-only inputs are joined as audio; --list parts.txt reads generated segments (TTS lines) and checks every one first — missing, empty or unreadable parts are all named in one refusal, or skipped and reported with --on-missing skip

silence.py

Detect and remove dead air (jump cuts) with a margin around speech; list or export the cut list; --filler also removes filler words, but only where a speech engine timed them; --speech-aware keeps a breath inside a sentence and cuts only at sentence-boundary pauses, composing with --filler through the same keep_ranges()

fit.py

Fit to a duration (pitch-preserving speed change or trim, smooth slow-mo) and/or aspect ratio (pad, crop or --fit blur's blurred, darkened fill, with --crop-x/--crop-y to keep an off-centre subject) and/or exact --width/--height; rotate 90/180/270, flip h/v; force constant fps

crop.py

Crop to an exact pixel rectangle (--x --y --width --height) — distinct from fit.py --fit crop, which crops to an aspect ratio it computes itself

cropdetect.py

Measure existing black letterbox/pillarbox bars and report the crop.py-ready rectangle that removes them — analysis only, writes no file; --motion-centre reports the per-second motion centroid instead, for a 9:16 reframe — report only, the calling agent picks the crop, no auto-reframing

deinterlace.py

Deinterlace interlaced source footage (yadif), --mode frame/field, --parity

denoise.py

Reduce video noise/grain (hqdn3d), --strength low/medium/high or individual spatial/temporal overrides

redact.py

Blur, pixelate, or (--mode delogo) interpolate away an exact pixel rectangle for the whole clip. The box is given: nothing here finds a face, a plate or a logo, and delogo is a border blend, not inpainting

sphere.py

Extract a flat rectilinear viewport from a 360/spherical video (--yaw --pitch --roll --h-fov --v-fov); no subject tracking, only the aim you give it

straighten.py

Rotate by an arbitrary angle for horizon correction (--degrees, --fit crop/pad) — distinct from fit.py --rotate's exact 90-degree turns

insert.py

Turn a still image into a silent, fixed-duration video clip (title card, end slate) at an exact frame size / fps, with an optional Ken Burns zoom/pan

background.py

Generate a solid-colour or two-colour gradient clip at an exact size/duration — no input file

reverse.py

Reverse playback (video and, unless --no-audio, audio)

stabilize.py

Two-pass motion stabilisation (vidstabdetect/vidstabtransform)

sequence.py

Numbered (frame_%04d.png) or glob-matched still images into a video

waveform.py

Render an audio track as a waveform or spectrum visualization video (showwaves/showspectrum) — for audio-only inputs with no picture worth showing; --image puts the visualisation over a still plate (an audiogram), with --platform, --title and burnt-in captions

freeze.py

Hold a frame for N seconds (--at, --hold, --mode insert/extend) — an end-card hold or a comedic beat

pad.py

Add black/silent padding at the start and/or end of the timeline (--start, --end) — distinct from fit.py --fit pad's per-frame letterbox bars

speedramp.py

Step through different constant speeds across a clip via --segment START-END:FACTOR (repeatable) — distinct from fit.py's single whole-clip speed factor

loop.py

Repeat a clip --times N or to a target --duration — for background loops and filling a fixed slot length; --boomerang plays it forward then backward

broll.py

Cut away to a B-roll clip over the A-roll for a window (--insert B --at T --duration D, repeatable) and come back; A's length and audio untouched by default

metadata.py

Write container chapter markers from a TIME TITLE text file and title/artist/comment tags, every stream copied bit for bit; --auto-chapters proposes the markers from measured pauses and scene cuts and names them Chapter N for you to rename

grid.py

Composite --colsx--rows clips into one grid, each cell letterboxed and labelled with its filename by default (--label none to skip)

Tool

What it does

audio.py

Voice clean-up chain at three strengths (--voice light|medium|strong), FFT denoise, typed compressor / limiter / gate, music bed with sidechain ducking (--duck-amount/-threshold/-attack/-release), a never-ducked effects bed (--effects), --stereo-widen, fades, 5.1 → stereo, track replacement, extraction (-o out.wav), --audio-stream N

sync.py

Offset between two recordings by audio cross-correlation (1 ms, pure Python), clock-drift correction; aligned video or audio out (audio-to-audio only — no lip-sync/face detection); a third or later camera is an additional more_sources positional (sync.py REF A B ...), the second positional itself keeps its name

loudness.py

Two-pass EBU R128 loudnorm to −14 LUFS / −1 dBTP or any target (--lra for the range), video stream-copied; the written file is measured again and re-encoded until it meets --tp (lossy encoders overshoot); --measure-only

Tool

What it does

caption.py

Burn SRT/ASS with font, size, colour, outline, position; build SRT from timed plain text; wraps to the safe area — with --platform, the destination's own left/right safe zone, which since 1.17.2 is also what the burnt ASS states as its side margins — with a phrase-aware breaker (`--wrap phrase

overlay.py

Logos, watermarks and titles with position, time range, opacity, fades; --platform NAME keeps them clear of that destination's UI; --video for picture-in-picture, --chromakey for green-screen compositing, --place to lay ranges of a layer at chosen times

graphics.py

Lower-thirds, title cards, chapter chips, progress bars, countdowns, corner bugs, social stickers, opening hook cards and meme captions drawn by FFmpeg from a brand kit; --platform NAME keeps them inside that destination's safe zone; --text-render routes shaping scripts through libass and --emoji-assets composites colour emoji

color.py

HDR10 / HLG / Dolby Vision → SDR BT.709 tone mapping, DV layer stripping, 3D LUT (.cube), colour-tag rewriting, typed primary correction (exposure/contrast/saturation/gamma/white balance/lift-gain/levels/curves)

Tool

What it does

export.py

Presets youtube, youtube4k, reels, tiktok, shorts, linkedin, facebook, x, youtube-hdr (HEVC Main10, source HDR tags kept), youtube-av1, prores, h265, gif, copy, all tagged BT.709 unless they carry HDR (a BT.601-matrix or RGB source is converted to BT.709, not relabelled); --normalize meets the platform's loudness in the same call (render.py turns it on by default for platform presets)

proxy.py

Small, low-bitrate proxy for downstream AI analysis/preview/editing decisions — resize by --width/--scale, proxy-grade --quality (default 30), --fps, --no-audio; not a delivery preset

check.py

PASS / WARN / FAIL against YouTube, Shorts, Reels, TikTok, X, LinkedIn, Facebook, broadcast and podcast specs, from the same delivery table the export presets and templates read (podcast also reports chapter markers and channel count), with the fix for each failure and a format / judgement kind per row; --content adds black-frame, frozen-frame, silence and short audio-dropout rows

report.py

Single-file HTML delivery report: before/after sheets, media facts, loudness, compliance, the commands run; --pack renders a social pack table

Tool

What it does

render.py

Render a whole edit from a declarative project.json (clips, transitions, captions, overlays including the social sticker/hook/meme graphics and a picture-in-picture or chroma-keyed video layer sized for the final frame, music and stem levels, loudness, export, chapter markers, check); --init, --dry-run, --stop-after, --cache DIR/--from STAGE (reuse the stages that did not change); the captions block takes the fit-size policy (fit_size, min_size, fit_size_scope) and the run reports the caption stage's counts as caption; --template NAME INPUT renders a shipped delivery template (--template all writes the whole social pack plus its table); --export-timeline edit.fcpxml|.edl|.otio (2.1) writes the cut as an editor timeline for Final Cut, DaVinci Resolve or Premiere instead of rendering it — clips, speed, dissolves, the music bed and chapter markers carry over, and not_exported names what stays behind

batch.py

Apply a step recipe or a project to a folder with a content-hash cache; --watch; --jobs N processes several files at once under one shared --timeout

multicam.py

Align any number of cameras and recorders by audio (with drift correction) and cut between them from a switch list; --switch energy auto-cuts to whichever camera is loudest/talking instead of a hand-built list, with --min-shot (minimum shot length) and --edl (write a plain START-END cut list, not a CMX EDL; for Premiere/Resolve use --write-project then render.py --export-timeline)

verify.py

Run the toolchain on real device files and report PASS / FAIL per step

Field

Meaning

input_schema

generated from the tool's argparse parser: properties, types, enums, defaults, required, positional order, mutually exclusive groups

output_schema

what --json prints: status, output, commands, probe, plus tool-specific fields (precision, checks, offset_seconds, …)

role

analysis, analysis_and_execution, execution or verification

capabilities

the FFmpeg encoders, filters and bitstream filters the tool always needs, and the ones needed only for a flag or input

supports_dry_run, supports_json, supports_json_brief

measured by the tests, not declared

verification

which tools to run on the output afterwards (probe, check, look)

requires_visual_verification

the picture changed; inspect the contact sheet

audio_only, video_required

whether an audio-only input is accepted or refused

mutates_input

always false

idempotency_hint

bit_exact, content_equivalent, cached or environment_dependent

FFmpeg

-filters row layout

Source

5.1.1

three flag characters, same as 6.x

johnvansickle.com static build on the Linux CI runner

6.1.1

three flag characters: ..C acompressor A->A

Ubuntu 24.04 apt, captured

7.1.x

same as 6.x

Debian trixie apt in a CI container (plus a constructed fixture in tests/)

8.1.2

two flag characters: TS aap AA->A, three-character legend, ------ separator

Homebrew on the macOS CI runner, captured

9.0.1

same as 8.x, CRLF

gyan.dev build on the Windows CI runner, captured

Result

Measurement

92 / 92

verification steps on a 10-file real-device corpus (GoPro, DJI, iPhone incl. Dolby Vision, Android screen recordings, HDR10, 24p, Tears of Steel), 0.8.0, local ffmpeg 6.1

40 / 40 within 10 ms

sync.py offset detection, ±30 s offsets with gain, noise and EQ changes on real dialogue and music, 120 s windows (max error 1.1 ms); 60 s stress windows 95 % within 10 ms, 4 of 5 misses flagged by confidence

0 missed gaps

silence.py, 20 cases with known gaps, ≤ 1 ms leftover silence

F1 0.97

scenes.py, 53 hard cuts between single takes, precision 0.95, recall 1.00 at the default threshold

exact to the sample

cut.py --accurate on WAV, FLAC (44.1 kHz) and AAC → WAV; WAV stream copy within 2 ms; AAC output +21 ms of encoder priming, reported as codec_frame (0.9.1)

72 / 72

agent runs of 24 prompts (12 English edits, 8 Japanese, 4 that must be declined), three repeats, graded by an independent model: routing, honest refusals and user's language 72/72, report format 71/72, visual check whenever the picture changed 24/24 (0.8.4)

7 / 8 routed

eval 22 at 1.18.3 (2026-09-17, eight new symptom-only prompts for the five 1.18.0 flags, naming no flag, plus three repeats each of cs1/cs3, Sonnet agent): after 1.18.1's SKILL.md routing rows and 1.18.2's matching README rows, scenes.py --shots, cropdetect.py --motion-centre, silence.py --speech-aware, sync.py's N-source form and multicam.py --switch energy were all found from a symptom alone — including a Japanese and a Spanish variant — up from eval 21's 4/9. The eighth prompt (--filler composed with --speech-aware) is an honest partial: the fixture has no real speech to transcribe, confirmed by installing faster-whisper mid-eval and re-testing by hand. cs3 no longer rewrites the user's captions in any of 3 runs — the 1.17.3 fix holds through 1.18.1-1.18.3. Written up in evals/results/iteration-22.json

8 / 12 routed

eval 21 at 1.18.0 (2026-09-17, one prompt per new analysis/multicam flag plus a cs1/cs3 recheck, Sonnet agent): every result was correct where the agent found the right script — both sync offsets, the shot label, the multicam switch point and its render-project mapping, both 1.17.2/1.17.3 rechecks — but 4 of 12 prompts hit a script the agent could not find, because SKILL.md named none of the five new flags (confirmed by grep). Three refused honestly rather than fabricate a number; one reached a correct answer without the intended flag, by luck of one fixture's silence durations. 1.18.1 (2026-09-17) is the SKILL.md fix, since evaluated by eval 22. Written up in evals/results/iteration-21.json

20 / 20

1.17.2 run (2026-09-14, the eight caption prompts of eval 19, six of them three times, Sonnet agent, regex grader + an Opus grader that opened every contact sheet and counted the lines per cue): routing 19/19 act runs, honest 18/20 with 0 false successes and 0 raw ffmpeg calls, report format 20/20, user's language 20/20, trigger set 49/50 (one judge flip on a file-less prompt), Opus quality mean 4.25 (3.65 at eval 19). The picture is fixed: 0/20 runs stack one word per line against 12/12 template runs at eval 19 on the same cues; the Style row at TikTok geometry is now …,54,151,420,1, the fitter's size_used (15 on TikTok, 16 on Shorts, the 13 floor for the Spanish cues) is what the frame shows, and report and sheet agree in 18/20 runs. What is left is not the typesetter: cs3 rewrote the user's captions for the fourth iteration running, one cs1 run raised max_lines to 4 to avoid a shrink and drew four-line stacks, and cs2's 32-letter Spanish word still leaves the frame at the size floor (disclosed 3/3). Written up in evals/results/iteration-20.json

26 / 26

1.17.1 run (2026-09-14, targeted re-run of the 18 prompts eval 18's follow-up named, plus three repeats each of the four caption-size prompts, Sonnet agent, regex grader + a full Opus grader over all 26 runs, every PNG opened and every written output re-probed): routing 23/26, honest refusals and failures 24/26 with 0 false successes and 0 raw ffmpeg calls, report format 26/26 with the third label gone, user's language 26/26, trigger set 50/50, Opus quality mean 3.65. 1.17.1's fix holds — --fit-size now fires on the render.py --template path in 12/12 caption runs (24 → 16, dl4 to the 13-unit floor, split 0, text_unchanged true, identical across repeats), the beat, filler and --jobs prompts route on the first try, and bt2 quotes its measured 0.184 confidence instead of denying the capability exists. The honest part: the picture is unchanged. caption.py's write_ass writes the platform's vertical safe margin into MarginL, MarginR and MarginV alike (tiktok 63 ASS units → 420 px), so at PlayResX 1080 the text column is 240 px and libass wraps every word — the fitter budgets play_w × 0.9, which is why split: 0 is true of the ASS text and false of the frame. It is the --animate/--karaoke path only, present since 1.14, and it explains eval 17's and eval 18's "one word per line" too; 1.17.2 is the patch and the finding is written up in evals/results/iteration-19.json

100 / 100

1.17.0 run (2026-09-14, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 28 runs, every written output re-probed, check.py re-run on every delivery output) on the set grown to 100 prompts (caption size fitting, beat-synced cuts, filler removal, batch --jobs, render --cache): routing 95% over the 64 act prompts, honest refusals and failures 22/25 with 0 false successes and 0 raw ffmpeg calls, report format 98/100 (two runs label an honest partial result with a third label), user's language 100/100 across seventeen languages, visual check 24/24, real execution 6/6 with honest failure 5/5, trigger set 50/50 including all five new 1.17 prompts, Opus quality mean 3.71. The honest part: --fit-size is unreachable on the template path (render.py forwards the platform table's caption size as an explicit --size, so the fitter declines to shrink a size it thinks the user chose, and the project schema rejects fit_size outright — only the one run that called caption.py by hand got 24 → 16, split 0), and SKILL.md names none of the 1.17 features, so beats, filler and --cache were each used in one run at most — 1.17.1 is the patch and the finding is written up in evals/results/iteration-18.json

90 / 90

1.16.0 run (2026-09-14, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 30 runs, chapters and subtitle streams re-probed, check.py re-run on every delivery output) on the set grown to 90 prompts (audiogram, auto chapters, multi-language tracks, caption breaking): routing 90/90, honest refusals and failures 90/90 with 0 false successes and 0 raw ffmpeg calls, report format 89/90 (one Done (partially):), user's language 90/90 by regex (89/90 by Opus), audiogram 2/2 with the cover behind the waveform and nothing fetched, auto chapters 2/2 with Chapter N titles only, delivery 16/16 platform pass, trigger set 45/45, Opus quality mean 4.17. The honest part: the phrase breaker never gets to act at the platform caption sizes (a five-word cue does not fit two lines at TikTok size, so the split is byte-identical to 1.15.1), Thai still breaks inside words, and a katakana word was split — 1.16.1 is the patch and the finding is written up in evals/results/iteration-17.json

82 / 82

1.15.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 28 runs, stills extracted inside the emoji window, check.py re-run on every delivery output) on the set grown to 82 prompts (emoji captions and title cards, a Hindi and a Thai lower-third): routing 82/82, honest refusals and failures 82/82 with 0 false successes and 0 raw ffmpeg calls, report format 82/82 (both iteration-15 label defects closed: dl8 and he2 now carry one Failed:), user's language 82/82 by regex (81/82 by Opus: one Spanish report with three English labels), non-Latin glyphs 11/11 (Devanagari through graphics.py is fixed; Thai lower-third and captions correct), emoji visible in colour in 3/3 runs given PNG assets and reported monochrome in the one that was not, visual check 23/24, delivery 12/13 one encode and 13/13 platform pass, trigger set 40/40, Opus quality mean 4.68. Still open: the caption breaker splits phrases (dl1, dl4 unchanged) — queued for 1.16.0. Tokens per run flat at 73.3k on the same 76. Details in evals/results/iteration-16.json

76 / 76

1.14.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader on 26 runs, check.py re-run on every delivery output): routing 76/76, honest refusals and failures 76/76 with 0 false successes and 0 raw ffmpeg calls, report format 76/76, user's language 76/76 by regex (75/76 by Opus: one Spanish report with three English labels), visual check 18/18, trigger set 38/38, Opus quality mean 4.58. The delivery templates did their job: 12 of 13 delivery requests went through render.py --template, finished in one encode (was 3 of 7) and all 13 pass their platform check (was 7 of 8). Tokens per run flat at 73.4k. Details in evals/results/iteration-15.json

76 / 76

1.13.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader) on the set grown to 76 prompts: 18 in Thai, Hindi, Hebrew, Russian, Greek, Vietnamese, Indonesian, Turkish and Italian, and 8 delivery requests (TikTok, Reels, Shorts, LinkedIn, Douyin, podcast): routing 76/76, honest refusals and failures 76/76 with 0 false successes and 0 raw ffmpeg calls, report format 76/76, user's language 76/76 across seventeen languages, visual check 18/18, trigger set 38/38, Opus quality mean 4.65 over the 26 new runs. One real defect found: Hindi through graphics.py (drawtext) comes out wrong-shaped even though the font covers Devanagari; captions through libass are fine (queued for 1.15.0). Four delivery runs spent a second encode for loudness, which 1.14.0's templates address. Tokens per run flat at 72.3k. Details in evals/results/iteration-14.json

50 / 50

1.12.0 run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader) on the set grown to 50 prompts with two each in Chinese, Korean, Spanish, Portuguese, French, German and Arabic: routing 50/50, honest refusals and failures 50/50 with 0 false successes and 0 raw ffmpeg calls, report format 50/50, user's language 50/50 across nine languages, visual check 13/13, trigger set 29/29, Opus quality mean 4.83. Every non-Latin caption and lower-third picked a covering font by itself and rendered real glyphs (Arabic shaped and right-to-left); tokens per run unchanged at 72.3k. Details in evals/results/iteration-13.json

36 / 36

1.11.1 re-run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader): routing 36/36, honest refusals and failures 36/36 with 0 false successes and 0 raw ffmpeg calls, report format 36/36, user's language 36/36, visual check 8/8, trigger set 22/22, Opus quality mean 4.75. The 1.11.1 wording did what it said (doctor before a job 23 of 36 runs → 0, --json-brief 4 → 23) and tokens per run stayed flat at 71.8k, because about 64k of every run is the host's own context; the token-diet theme closes here. Details in evals/results/iteration-12.json

36 / 36

1.11.0 re-run (2026-09-13, one pass per prompt, Sonnet agent, regex grader + focused Opus grader): routing 36/36, honest refusals and failures 36/36 with 0 false successes and 0 raw ffmpeg calls, report format 36/36, user's language 36/36, visual check 8/8, trigger set 22/22. First iteration to measure tokens per run: mean 72.2k against 68.7k at 1.10.0, mostly a fixed per-run floor the skill does not control (a refusal run that only reads SKILL.md costs about 64k), plus doctor on 23 of 36 runs; 1.11.1 rewords step 0 and iteration 12 re-measures. Details in evals/results/iteration-11.json

108 / 108

1.10.0 re-run (2026-09-13, three passes per prompt, Sonnet agent, regex grader + focused Opus grader): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, report format 108/108 by both graders (the harness now names the five labels), user's language 105/108 (every Japanese request in Japanese; 3 English requests drifted to Spanish or Portuguese), visual check 21/24, trigger set 22/22; real-device corpus 101/101 steps PASS. Details in evals/results/iteration-10.json

108 / 108

1.9.0 re-run (2026-09-13, three passes per prompt, Sonnet agent, regex grader + independent Opus grader): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, visual check 23/26, user's language 105/108 (every Japanese request answered in Japanese; 3 English requests drifted to Spanish), report format 108/108 by regex (65/108 by the stricter grader, which now counts any missing label), trigger set 22/22; 12 of 15 platform jobs were one encode and render.py rendered once in 3/3 (was 1/3). The 1.9.0 time grammar was not used by any agent. Details in evals/results/iteration-9.json

108 / 108

1.8.0 re-run (2026-09-12, three passes per prompt, Sonnet agent, regex grader + independent Opus grader that re-probed 22 outputs): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, visual check 22/24, report format 108/108 by regex (91/108 by the stricter grader: 'What/how:' in place of Steps:), user's language 98/108 by the stricter grader (Japanese labels-only reports counted), trigger set 22/22; 13 of 14 platform exports used --normalize and platform jobs went from three encodes to one; r04/f01 now answered in the request's language 5/6 (was 0/6). Details in evals/results/iteration-8.json

108 / 108

1.7.0 re-run (2026-09-12, three passes per prompt, Sonnet agent, regex grader + independent Opus grader that re-probed 24 outputs): routing 108/108, honest refusals and failures 108/108 with 0 false successes and 0 raw ffmpeg calls, visual check 25/25, report format 108/108 by regex (104/108 by the stricter grader), user's language 101/108 (six English refusals answered in Spanish or Portuguese, one Japanese request in English); trigger set 22/22. Iteration-6 fixes held (fade-in only, Japanese audio trims, music no longer shortens the video). Details in evals/results/iteration-7.json

36 / 36

1.4.15 re-run (2026-09-12, one pass per prompt, Sonnet agent, regex grader + manual review): 24-prompt set routing 20/20, honest refusals 5/5, visual check 8/8, report format 25/25, user's language 9/9; exec set real execution 6/6, honest failure on bad inputs 5/5 with 0 false successes, audio-as-audio 3/3, one Japanese report with English labels; trigger set 22/22. Both iteration-5 defects gone (no raw ffmpeg fallback, music no longer shortens the video). Details in evals/results/iteration-6.json

36 / 36

1.4.0 re-run (2026-09-11, one pass per prompt, Sonnet agent, regex grader + manual review): 24-prompt set routing 20/20, honest refusals 5/5, visual check 8/8, user's language 8/9; exec set real execution 6/6, honest failure on bad inputs 5/5 with 0 false successes, audio-as-audio 3/3; trigger set 22/22. Details and the six findings in evals/results/iteration-5.json

6 / 6

0.9.1 audio evals (audio join, extraction, track selection, sample-accurate trim, typed dynamics; 2 in Japanese): routing, report format and audio-as-audio handling 6/6

Step

Wall time

Note

probe.py

0.3 s

silence.py --list

14 s

one audio decode

loudness.py --measure-only

66 s

look.py (contact sheet)

97 s

scenes.py

241 s

check.py --platform youtube --content

308 s

the rows, dropouts included, cover the full hour

cut.py, a 10-minute stream-copy segment

2.4–4.4 s

no re-encode

join.py, two 10-minute segments

624 s

join.py always re-encodes, so it can crossfade and conform

export.py --preset youtube, a 10-minute segment

514 s

about 1.2× real time at the default medium preset

export.py --preset youtube --fast, the same segment

162 s

about 3.7× real time at veryfast

export.py --preset youtube, the whole hour

killed at 1802 s

kind: timeout at the default --timeout of 1800 s (below)