Download packages/omo-senpi/skills/onboarding/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does onboarding live?
The publisher keeps it at https://github.com/code-yeongyu/oh-my-openagent.
Which agents can use onboarding?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.
Guides a first-time omo user through setup and the core workflow. Use when a new user asks to get started with omo.
name: onboarding
description: "Guides a first-time omo user through setup and the core workflow. Use when a new user asks to get started with omo."
onboarding - the first conversation with omo
Purpose
This skill introduces OmO through six lanes, in order: the feature tour, migration help, session archaeology, value mapping,
memory recording (which runs through the whole flow, not at the end), and the first-session
init-deep proposal. Three of the lanes are opt-in. When the user declines one, move on without
argument and without repeating the offer.
Detect the user's language from their message and respond in that language for the rest of the
conversation. The skill is written in English; your output is not. Match them exactly, including
tone.
Use Senpi-native tools only: , , , , the tools, and skill
invocations. Never assume a tool from another agent product exists here.
read
bash
edit
write
memory
Be concrete, never generic. "omo caches your context" is a failure of this skill. "Your last week
of Claude Code sessions read 4.7M tokens from cache at a 78% hit rate; here is what that would
have cost cold" is the bar.
Entry modes
Context on the first prompt: answer the user's actual request fully first. Then greet them
briefly in their language and offer the tour in one line. Wait for acceptance before starting
the lanes below; weave useful onboarding into their task without replacing it.
Explicit tour:--onboard, a request for the tour or getting started, or acceptance of the
offer starts the lanes immediately. In TUI, --onboard starts the guided conversation at
launch; in RPC, it applies to the first real user prompt.
1. Feature tour
Open by introducing yourself and giving a short tour of what omo adds on top of a plain coding
agent. The catalog below is baked in at authoring time because the user's machine has no omo or
senpi source tree to explore. Present it conversationally, three to five highlights at a time, and
let the user ask for depth on any item. Do not dump the whole list as a wall of text.
The baked catalog:
Eleven-agent roster: primary workers, plan specialists, architecture consultation, codebase
explorers, research librarians, and focused review agents are routed by the work rather than
forced through one general-purpose persona.
The Senpi component layer: startup config and migration, native status, onboarding,
init-deep advising, anonymous telemetry, ultrawork arming, ulw-execute continuation, ulw-loop
continuation, todo fan-out reminders, fallback architecture, comment checking, ast-grep, LSP,
task delegation, memory, and live config watching cooperate as independent components.
Senpi-native skills: init-deep, ultrawork, ulw-loop, ulw-plan, ulw-research,
hyperplan, coding-agent-sessions, and give-me-tips provide reusable workflows that the
agent reads and follows only when relevant.
Three MCP tiers: built-in servers, user or project .mcp.json servers, and skill-embedded
servers give projects a layered tool surface without forcing every integration into core.
Team mode: cooperating agent sessions can share work, messages, and task state when the
selected omo harness exposes that surface.
Goal and boulder state: durable objective and work-plan state let long work resume from
recorded progress instead of relying on conversation context alone.
Ultrawork and the ulw keyword: don't try to memorize the workflows up front — you'll
pick them up by using them. For now, remember one word: ulw. Put ulw in your prompt and
the agent gets sharper: it plans, researches, loops, and fans out work with evidence-bound
continuation until the goal is actually proven. Whenever something makes you curious — a tip
you saw, a feature you want explained — ask give-me-tips and it walks you through it.
Mass-ulw: mass ulw opens dependency-ordered multi-agent DAG orchestration — many child
agents run in parallel waves, some waiting on others, so big jobs finish faster. Combined
requests such as mass ulw research load both mass-ulw and ulw-research at once.
The fallback architect: refusal metadata can route the unresolved engineering question
through an architecture consultation lane while the active model continues execution.
Memory: dedicated memory tools record durable user and project facts so later sessions begin
with the right stack, preferences, and working habits.
Telemetry: privacy-bounded anonymous lifecycle signals and local preview commands make omo
behavior measurable and auditable, with documented opt-outs.
Init-deep: hierarchical AGENTS.md generation, snapshot state, local or committed mode, and
later drift detection keep project instructions aligned with the codebase. On larger
repositories init-deep runs through mass-ulw's DAG map-reduce, so the work is spread across
parallel scanner and writer agents instead of one session.
Tips with a live source of truth: list the tips with the command this product is installed
as - omo --list-tips on OmO Native (omo-ai installs, where senpi is not on PATH) or senpi --list-tips on a plain senpi install - then read and follow give-me-tips for any visible tip
the user wants explained from the implementation.
Interactive UI primitives: real pickers, confirms, inputs, notifications, editors, custom
views, and widgets let components ask structured questions instead of burying choices in prose.
Re-running this tour: onboarding context accompanies the first real message, not launch.
The user can bring the tour back any time
with the --onboard flag of the command this product is installed as - omo --onboard on OmO
Native (omo-ai installs, where senpi is not on PATH) or senpi --onboard on a plain senpi
install - or shut the auto-start off with the omo-senpi-onboarding-disabled flag.
The init-deep advisor: after this first session, omo watches each project for AGENTS.md
coverage gaps and drift, and proposes an init-deep run only when the numbers justify one. On
this first session, you carry that proposal yourself in lane 6.
While the user reacts to the tour, start lane 5: record what you learn about them through the
memory tools as you learn it.
2. Migration help
Ask whether the user is coming from another coding agent and would like their setup carried over.
This lane is opt-in. If they say no, skip to lane 3.
If they say yes, scrape their existing configuration from as many sources as exist on this
machine. Check at least:
Claude Code: ~/.claude/settings.json, project and global CLAUDE.md files, MCP server
definitions in .mcp.json or settings.
Codex: ~/.codex/config.toml, any AGENTS.md files it manages.
Global OpenCode MCP servers and global OpenCode skills are not yours to move by hand. omo setup
imports them: MCP servers from ~/.config/opencode/opencode.json[c] into the engine's global
~/.omo/agent/mcp.json, and skills from ~/.config/opencode/skills/ into ~/.omo/agent/skills/,
converted to the shapes omo reads, consent-gated, and never overwriting a name that already exists.
Run omo setup --dry-run to show the user exactly what would land. Once they accept, run
omo setup --yes: your shell is not a terminal, so plain omo setup stops at its consent prompt
and imports nothing.
Copying a global server into a project .mcp.json yourself is a bug: it disappears the moment the
user opens any other directory.
Read what you find, then present one concrete migration plan: which settings map to
~/.omo/omo.json[c], which MCP servers omo setup carries over globally and which project-only
servers still belong in that project's .mcp.json, which CLAUDE.md content becomes project
AGENTS.md content, and which personal facts belong in memory instead of files.
Show the plan and WAIT for the user to accept it. Apply nothing before they say yes. If they accept
part of it, apply that part only. Record their agent-product history and migration choices through
the memory tools.
3. Session archaeology
Ask the user, in your own voice and their language, a question that means: "may I look through
your previous coding-agent sessions?" Phrase it naturally; do not read that sentence out like a
script. This lane is opt-in. If they say no, skip to lane 4 and base it on nothing.
If they say yes, drive the coding-agent-sessions skill: read its SKILL.md and follow it. Use the
bundled finder to list sessions across every platform present on the machine, then go deep on the
interesting ones. Mine for:
how they actually work: hands-on-the-wheel back-and-forth versus long one-shot delegations,
their planning stance, feeding the seed described in lane 5: opening-message shape (requirements
and constraints enumerated upfront, or vague starts?), how they answer agent questions (pick an
offered option, answer several at once holistically, redirect, or delegate the decision), how
they approve (a verbal yes versus commanding execution), and whether the style shifts by repo or
domain,
what frustrates them: repeated corrections, abandoned sessions, prompts that read as annoyed, in
any language,
how much they run in parallel, and how long their longest sessions run,
which repos, stacks, and models dominate their history.
Weave in migration suggestions where the history invites them, lightly. When a pattern you find
maps to an omo feature from the tour, say so with the evidence: "you corrected the agent about
your test runner in nine sessions; memory ends that" lands, a generic pitch does not. Record every
durable finding about the user through the memory tools as you go.
4. Value mapping + savings
From the session data gathered in lane 3, give the user quantified estimates of what omo's caching
would have been worth on their real workload. Compute, do not guess:
Quantify token and cost savings only from Senpi JSONL v3 sessions. Use other agent stores for
session counts, duration, concurrency, and qualitative work-pattern analysis, not token savings.
Fetch https://models.dev/api.json. Treat its top level as the provider map. Each provider owns
a models object, and each model cost uses input, output, cache_read, and cache_write
USD prices per one million tokens. If the API is unavailable, every session has insufficient
pricing data.
Process session files in chronological filename order. Process entries inside a file in JSONL
line order. A malformed timestamp skips that entry. Entries whose timestamps differ by at most
one millisecond retain JSONL line order.
Read usage only from message.usage. Map input, cacheRead, cacheWrite, and output
exactly. The three input categories are disjoint:
total_input_tokens = input + cacheRead + cacheWrite. A session whose total input is zero is
skipped.
Attribute each usage entry to message.provider plus message.model when both are present.
Otherwise use the latest model_change provider and modelId. Usage before any model
information uses the first later model_change in that file when one exists; otherwise skip it.
Resolve prices first by exact provider/model composite identity. If that misses, search for an
exact model id across provider keys sorted alphabetically, then model keys sorted alphabetically.
The first deterministic hit wins. If any required price field is missing, mark that entire
session as insufficient data.
Aggregate usage by provider and model, then compute grand totals. The caching rate is
cacheRead / (input + cacheRead + cacheWrite) * 100.
Compute actual cost as
(input * price.input + output * price.output + cacheRead * price.cache_read + cacheWrite * price.cache_write) / 1_000_000.
Compute omo savings as
cacheRead * (price.input - price.cache_read) / 1_000_000.
Round only final aggregated values with Math.round(value * 100) / 100. Never round per message,
model, or session. Label the result estimate.
Use real correction or frustration prompts found in lane 3 to demonstrate omo's
language-agnostic intent routing. Explain, in the user's language, how a phrase such as
"no, that is not what I meant" or its actual non-English equivalent is treated as corrective
steering for the active task rather than misread as a disconnected request. Tie each example to
the observed transcript and the omo feature that addresses it.
Label the result as an estimate and give exactly one line of methodology, in this shape: "Estimate
from your local Senpi session logs: message-level model attribution, models.dev input/output/cache
read/cache write pricing, summed across sessions, rounded at the end." One line, then the numbers,
then stop.
If the user skipped lane 3, skip this lane too; there is no data to map.
5. Memory recording
This lane has no fixed position: it runs through the entire flow. Whenever any lane teaches you
something durable about the user, write it through the memory tools at that moment, not in a
batch at the end. Worth recording:
their host and machine facts relevant to future work,
their language and communication style,
which agent products they came from and what they kept from them,
their stacks, main repos, and working patterns from lane 3,
their stated preferences and every accept or decline decision from this conversation.
Write durable personal and cross-project facts through the memory tool into system/human.md.
Write repository-specific stack, commands, constraints, and migration decisions through the memory
tool into system/project.md. Do not edit those backing files directly.
Planning-stance observations from lane 3 are the one exception: write them as SEEDS under a
## Seed heading in system/human/planning-style.md (create the block through the memory tool if
absent), never into system/human.md. Seeds are inferences and MUST stay weak:
Phrase every seed as a hypothesis ("appears to enumerate upfront and delegate the rest"), never
a conclusion, and set confidence: low ALWAYS - never medium or high, however consistent the
history looks. Behavior in another tool reflects that tool's affordances, not necessarily this
user; omo's own planning sessions will confirm or replace these within a few runs.
Record full provenance on every seed line: the source harness name(s) (e.g. Claude Code,
Codex, OpenCode), the model(s) under which the pattern was mainly observed, the session ids
mined, and today's date as seeded:. Format:
- [YYYY-MM-DD] (seed) appears to <hypothesis + context>
Seeds carry interaction STYLE only - never session content, code, file paths, or secrets.
Record facts, not narration. "Prefers Korean, migrated from Claude Code, works one repo at a time
in long sessions" is a memory. "The user went through onboarding today" is not.
6. First-session init-deep proposal
On the true first session the advisor component stays quiet, so this proposal is yours to carry.
Before saying anything about it, run the same eligibility gate the advisor uses, in this order:
The current directory must be inside a git repository. Check with
git rev-parse --show-toplevel. Not a repo: ineligible.
There must be candidate directories worth documenting: directories within depth 3 of the repo
root (excluding node_modules, .git, dist, build, vendor, .next, __pycache__,
.venv, target, coverage, third_party) holding at least 8 source files or at least 500
lines of source directly in them. Zero candidates: ineligible, regardless of anything else.
Compute coverage: a candidate counts as covered when it or an ancestor up to the repo root has
an AGENTS.md. The missing ratio is uncovered candidates over all candidates. Below 0.50:
ineligible. A new project with candidates and no AGENTS.md anywhere is missing ratio 1.0 and
eligible.
If any gate fails, skip this lane SILENTLY. Do not mention AGENTS.md, do not explain why you are
not proposing, do not hint that a check ran. Close the conversation warmly instead.
If all gates pass, ask in the user's language: "want me to set up AGENTS.md for this project?"
This is opt-in. On yes, read the init-deep skill at its SKILL.md path and follow it. On no,
record the decline through the memory tools and finish the conversation gracefully: a short
send-off in their language, an invitation to come back with omo --onboard on OmO Native or
senpi --onboard on a plain senpi install, and nothing more.
After init-deep finishes, tell the user in their language that init-deep ran through mass-ulw's
DAG orchestration — the work was spread across parallel child agents in dependency-ordered
waves rather than done by one session. Then introduce mass-ulw: mass ulw is how you bring that
same multi-agent orchestration to any big job, and combined requests such as mass ulw research, mass ulw loop, and mass ulw plan load mass-ulw alongside each of those workflows.
How do I install onboarding?
Download packages/omo-senpi/skills/onboarding/SKILL.md from the publisher, or copy the path and open the repository. PlugMCP lists the skill and does not host the file.
Where does onboarding live?
The publisher keeps it at https://github.com/code-yeongyu/oh-my-openagent.
Which agents can use onboarding?
Claude, Cursor, and Codex can read a SKILL.md you add from the publisher's repository.