The Lead Stack: Agent-Agnostic Workflows
intent-brief → spec → plan → diff → review-report → release
A high-performance repository of "Skills" and RTK-powered tools designed for Tech Leads. These workflows are Agent-Agnostic, allowing any LLM agent (Gemini, Claude, GPT) to assist with implementation planning, code review, and automated testing.
Live Web App: https://ai-tech-lead-stack.vercel.app
Setting this up with an AI assistant? There's a prompt written for that: Set this up with an AI assistant. It checks your computer, asks what you want help with, sets everything up, and answers your questions afterwards.
Contents
- How it fits together
- Commands Quick Reference
- Which tier am I on?
- Requirements
- Set this up with an AI assistant
- 🚀 Quick Start
- Supported Editors & Agents
- 🧹 Resetting a Project
- Documentation: setup guides, the skill and workflow catalogues, methodology and architecture
- Peripherals & Sibling Apps
- Branching Strategy
- Support, Contributing & Security
How it fits together
There are two ways in: the web dashboard and your IDE's coding agent. Both use
the same shared core (packages/core). Both can also change code:
- The web app mostly reads your linked GitHub repo, but a few routes commit
to its
feat/*anddiscovery/*branches or trigger its GitHub Actions (close-up 4). - In the IDE, your coding agent edits files, and the MCP server's
apply_patchtool can write them too (close-up 7).
The overview comes first. It is followed by the web app flow, then the IDE flow, and finally the telemetry both flows share. Colours are the same in every diagram: purple for people, blue for the dashboard, green for the shared core and MCP server, and pink for storage and outside services. Dashed lines are optional or conditional calls. Thick arrows are writes to code or to a Git repo.
flowchart LR
dev(("Developer<br/>in browser"))
agent(("IDE coding agent"))
dash["Dashboard app<br/>apps/dashboard"]
subgraph core["Shared core · packages/core"]
mcp["MCP server"]
libs["Reflexion engine · skills<br/>model resolver · telemetry"]
end
repo["Your project repo<br/>on GitHub"]
code[("Files on your machine<br/>(MCP working directory)")]
ext[("Postgres · AI providers<br/>E2B · ClickUp · Figma · Discord · Ollama")]
dev -->|"uses"| dash
agent -->|"calls MCP tools"| mcp
agent ==>|"edits"| code
mcp ==>|"reads · apply_patch writes"| code
dash -->|"uses"| libs
mcp -->|"uses"| libs
dash ==>|"reads · commits to feat/* and discovery/*<br/>branches · triggers Actions"| repo
dash -->|"app data · models · sandboxes · webhooks"| ext
libs -->|"app data · models · telemetry"| ext
mcp -->|"code_search embeddings"| ext
classDef actor fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef app fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef coreNode fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class dev,agent actor
class dash app
class mcp,libs coreNode
class repo,code,ext extNode
Web app flow
Everything the dashboard (apps/dashboard) does. You sign in with GitHub, and
routes that touch GitHub use your GitHub token on the project's linked repo.
Model calls use the API keys you saved in Settings, and fall back to the
server's env keys.
1. Chat and feature discovery
Chat streams replies from your chosen model and saves the conversation. When the
project has a linked repo, it reads code from that repo on GitHub. Otherwise its
tools read skills and files from the dashboard server's own toolbox checkout.
Slash-command workflows always come from the toolbox. The ClickUp and Figma
tools only read, and only work when the project has those keys saved. Feature
discovery streams a planning conversation; its one tool, write_to_sandbox,
runs in your browser (close-up 3). Both routes also read your user and project
records.
flowchart LR
discovery["Feature discovery<br/>api/orchestrator/discovery"]
chat["Chat<br/>api/chat"]
db[("Postgres")]
ai["AI model providers"]
tools["ClickUp · Figma<br/>read-only"]
github["Your project repo<br/>on GitHub"]
toolbox[("Toolbox skills<br/>and workflows")]
core["Telemetry<br/>(see close-up 9)"]
discovery -->|"streams replies"| ai
chat -->|"saves chats and messages"| db
chat -->|"streams replies"| ai
chat -.->|"reads tasks and designs"| tools
chat -.->|"reads code (repo linked)"| github
chat -->|"slash workflows · skills<br/>when no repo is linked"| toolbox
chat -->|"records events"| core
click chat "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/chat/route.ts"
click discovery "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/orchestrator/discovery/route.ts"
classDef actor fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef app fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef coreNode fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class chat,discovery app
class core coreNode
class tools,github,toolbox,ai,db extNode
2. Reflexion from the web app
The Reflexion route reads package.json and tsconfig.json from your linked
repo for context, then runs the reflexion engine. The engine drafts a plan,
critiques it and revises it, saving progress to the ReflexionRun table after
each step. Every step is also recorded as telemetry. You get back a plan and an
IDE prompt to apply yourself. The resume route continues a paused run the same
way, without reading GitHub.
flowchart LR
dev(("Developer<br/>in browser"))
reflexapi["Reflexion route<br/>api/orchestrator/reflexion"]
github["Your project repo<br/>on GitHub"]
engine["Reflexion engine<br/>engine.ts"]
runner["Model runner<br/>providers-user"]
ai["AI model providers"]
dbstore["DbStateStore"]
db[("Postgres<br/>ReflexionRun")]
telemetry["Telemetry<br/>(see close-up 9)"]
dev -->|"starts a run"| reflexapi
reflexapi -.->|"reads package.json<br/>and tsconfig.json"| github
reflexapi -->|"runs loop"| engine
engine -->|"drafts and critiques"| runner
runner -->|"your saved keys,<br/>else env keys"| ai
engine -->|"saves progress"| dbstore
dbstore --> db
reflexapi -->|"records each step"| telemetry
reflexapi -->|"plan + IDE prompt"| dev
click reflexapi "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/orchestrator/reflexion/route.ts"
click engine "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/lib/ai/reflexion/engine.ts"
click runner "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/lib/ai/reflexion/providers-user.ts"
click dbstore "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/lib/ai/reflexion/db-state-store.ts"
classDef actor fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef app fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef coreNode fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class dev actor
class reflexapi app
class engine,runner,dbstore,telemetry coreNode
class github,ai,db extNode
3. Sandbox preview
Feature discovery can preview changes in an E2B sandbox, using the E2B key you saved in Settings. Your browser first fetches a zip of the linked repo's default branch, together with the project's decrypted env vars. It then asks the server to boot a sandbox, which uploads the project, installs dependencies and starts a dev server. File writes from the discovery AI go into that sandbox only. Nothing is copied back to GitHub automatically.
flowchart LR
dev(("Developer<br/>in browser"))
bundle["Project bundle<br/>api/orchestrator/discovery/project-bundle"]
repo["Your project repo<br/>on GitHub"]
boot["Sandbox boot<br/>api/orchestrator/sandbox/boot"]
fileaction["Sandbox file action<br/>discovery/actions/e2b.ts"]
e2b["E2B sandbox<br/>(your E2B key)"]
dev -->|"1 · fetch project"| bundle
bundle -->|"downloads a zip of<br/>the default branch"| repo
dev -->|"2 · boot sandbox"| boot
boot -->|"uploads, installs,<br/>starts dev server"| e2b
dev -->|"3 · AI file writes"| fileaction
fileaction -->|"writes files"| e2b
click bundle "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/orchestrator/discovery/project-bundle/route.ts"
click boot "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/orchestrator/sandbox/boot/route.ts"
click fileaction "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/feature-development/discovery/actions/e2b.ts"
classDef actor fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef app fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class dev actor
class bundle,boot,fileaction app
class repo,e2b extNode
4. Writes to your GitHub repo
These routes change your linked repo on GitHub, using your GitHub token.
Feedback commits a screenshot to .github/assets/feedback/ on a branch,
comments on that branch's pull request and posts to Discord. Sync commits
whatever files it is sent; nothing in the dashboard calls it yet. Both refuse
any branch that isn't feat/* or discovery/*, and always block main,
master, prod, production and staging. Generate and Audit trigger the
ai-prompt-agent.yml and audit.yml GitHub Actions on your repo's main
branch. Those workflow files live in your repo, so what they change is defined
there, not here.
flowchart LR
feedback["Feedback<br/>api/feature-development/feedback"]
sync["Sync<br/>api/feature-development/sync"]
actions["Generate · Audit<br/>api/orchestrator/generate · audit"]
repo["Your project repo<br/>on GitHub"]
discord["Discord"]
feedback ==>|"commits snapshot PNG<br/>· comments on PR"| repo
feedback -->|"notifies"| discord
sync ==>|"commits files to<br/>feat/* or discovery/*"| repo
actions ==>|"triggers ai-prompt-agent.yml<br/>or audit.yml"| repo
click feedback "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/feature-development/feedback/route.ts"
click sync "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/feature-development/sync/route.ts"
click actions "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/orchestrator/generate/route.ts"
classDef app fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class feedback,sync,actions app
class repo,discord extNode
5. Settings, design review and the skill editor
Projects and Settings store app data in Postgres: projects, who can access them, your API keys (encrypted), and per-project model routing. Design review keeps review sessions in Postgres, reads Figma files through a proxy with the project's Figma key, and posts to the project's Discord webhook when a review is ready for the designer. Notify devs posts to a separate Discord webhook. The skill editor drafts new skills with your model. When you submit one, it opens a draft pull request on this toolbox's own repo, not on your project.
flowchart LR
settings["Projects · Settings<br/>api/projects · api/settings/*"]
design["Design review<br/>api/design-review"]
notify["Notify devs<br/>api/notify/devs"]
skilled["Skill editor<br/>api/skills/chat · skills/actions.ts"]
db[("Postgres")]
figma["Figma"]
discord["Discord"]
ai["AI model providers"]
tbrepo["Toolbox repo<br/>on GitHub"]
settings -->|"projects, access, encrypted<br/>API keys, model routing"| db
design -->|"review sessions"| db
design -.->|"figma-proxy reads files"| figma
design -->|"ready for designer"| discord
notify -->|"webhook"| discord
skilled -->|"drafts skills"| ai
skilled ==>|"opens a draft PR<br/>with the new skill"| tbrepo
click design "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/design-review/route.ts"
click notify "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/notify/devs/route.ts"
click skilled "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/api/skills/actions.ts"
classDef app fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class settings,design,notify,skilled app
class db,figma,discord,ai,tbrepo extNode
IDE flow
Everything your coding agent does through the MCP server, which runs on your
machine. Every project path the server uses is relative to the directory it
was started in. The default install starts it with npm --prefix <toolbox>,
so that directory is the toolbox checkout unless your editor or a proxy starts
it somewhere else.
6. Skills, knowledge items and pipelines
Skills, workflows, policies and the skill graph are read from the toolbox
checkout. If the server finds a client project above its working directory, it
also reads that project's .ai overrides. Before any get_* call, the server
checks the hook rules in .ai/hooks and can refuse the call. Knowledge Items
(KIs) are stored in your home directory, in ~/.gemini/antigravity/knowledge.
verify_mission_alignment writes .ai/.mission-alignment.json into the toolbox
checkout. get_* and plan_pipeline calls are recorded as telemetry.
flowchart LR
agent(("IDE coding agent"))
mcp["MCP server<br/>mcp-server/index.ts"]
hooks[(".ai/hooks<br/>(working directory)")]
toolbox[("Toolbox files<br/>.ai/skills · workflows · policies")]
project[("Project .ai overrides<br/>skills.graph.json · policies")]
kis[("Knowledge items<br/>~/.gemini/antigravity/knowledge")]
telemetry["Telemetry<br/>(see close-up 9)"]
agent -->|"skill, pipeline,<br/>KI and alignment tools"| mcp
mcp -->|"get_* calls checked first,<br/>can refuse"| hooks
mcp -->|"reads skills, graph, policies<br/>· writes .mission-alignment.json"| toolbox
mcp -.->|"reads, if a client project is found"| project
mcp -->|"reads and writes KIs"| kis
mcp -->|"get_* and plan_pipeline<br/>record events"| telemetry
click mcp "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/mcp-server/index.ts"
classDef actor fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef coreNode fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class agent actor
class mcp,telemetry coreNode
class hooks,toolbox,project,kis extNode
7. Code tools
repo_map and code_search read a prebuilt index in .tls-index, which you
create with npm run index:build in packages/core. code_search also calls a
local Ollama (OLLAMA_URL, default localhost:11434) to embed your query.
read_region reads a line range from a file. apply_patch writes a file after
checking .ai/hooks for protected paths. None of these tools restrict paths to
the working directory.
flowchart LR
agent(("IDE coding agent"))
mcp["MCP server<br/>handlers/codebase.ts"]
index[(".tls-index<br/>built by index:build")]
ollama["Ollama<br/>localhost:11434"]
hooks[(".ai/hooks<br/>protected paths")]
files[("Files in the MCP<br/>working directory")]
agent -->|"repo_map · code_search<br/>read_region · apply_patch"| mcp
mcp -->|"repo_map, code_search read"| index
mcp -->|"code_search embeds query"| ollama
mcp -->|"apply_patch checks first"| hooks
mcp -->|"read_region reads"| files
mcp ==>|"apply_patch writes"| files
agent ==>|"edits"| files
click mcp "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/mcp-server/handlers/codebase.ts"
classDef actor fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef coreNode fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class agent actor
class mcp coreNode
class index,ollama,hooks,files extNode
8. Reflexion from the IDE
reflexion_loop starts a run in the background and returns a run id straight
away. Its _sub_max and _sub_pro variants only change the tier check. Before
starting, it looks you up in Postgres by your GitHub CLI or git email, along
with the project, so it can use the API keys and model routing you saved in the
web app. Env keys are the fallback, and env model variables such as
MODEL_PLANNER override saved routing. Progress is saved to
.reflexion-out/state.json, or to your OS temp directory when the server runs
inside another project. Poll reflexion_status for the result, and use
reflexion_resume to continue a paused run; resume skips the Postgres lookup
and uses env keys only. Each step is recorded as telemetry, and your coding
agent applies the final plan.
flowchart LR
agent(("IDE coding agent"))
mcp["MCP server<br/>handlers/reflexion.ts"]
db[("Postgres<br/>saved keys · model routing")]
engine["Reflexion engine<br/>engine.ts"]
runner["Model runner<br/>providers-env"]
ai["AI model providers"]
filestore["FileStateStore"]
files[(".reflexion-out/state.json<br/>or OS temp dir")]
telemetry["Telemetry<br/>(see close-up 9)"]
agent -->|"reflexion_loop · status · resume"| mcp
mcp -.->|"reflexion_loop only: looks up<br/>you (gh/git email) and project"| db
mcp -->|"runs loop in background"| engine
engine -->|"drafts and critiques"| runner
runner -->|"saved keys, else env keys<br/>(resume: env keys only)"| ai
engine -->|"saves progress"| filestore
filestore --> files
mcp -->|"records each step"| telemetry
mcp -->|"run id, then status and plan"| agent
click mcp "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/mcp-server/handlers/reflexion.ts"
click engine "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/lib/ai/reflexion/engine.ts"
click runner "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/lib/ai/reflexion/providers-env.ts"
click filestore "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/lib/ai/reflexion/state-store.ts"
classDef actor fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81
classDef coreNode fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class agent actor
class mcp,engine,runner,filestore,telemetry coreNode
class db,ai,files extNode
Shared by both flows
9. Where telemetry goes
Web chat, the skill editor, web reflexion and several MCP tools record events
through one telemetry service. It writes each event to the AnalyticsEvent
table, the single source of truth. Each event has a kind; only
llm_generation events carry a cost. The Metrics dashboard reads those events
and reflexion runs with grouped queries. See
docs/methodology.md for what each kind means.
flowchart LR
webchat["Web chat · skill editor<br/>api/chat · api/skills/chat"]
webreflex["Web reflexion<br/>and resume routes"]
mcpcalls["MCP get_* · plan_pipeline<br/>reflexion_loop · reflexion_resume"]
telemetry["Telemetry service<br/>telemetry-service.ts"]
db[("Postgres")]
metrics["Metrics dashboard<br/>dashboard/page.tsx"]
webchat -->|"records events"| telemetry
webreflex -->|"records events"| telemetry
mcpcalls -->|"records events"| telemetry
telemetry -->|"writes AnalyticsEvent"| db
metrics -->|"reads AnalyticsEvent<br/>and ReflexionRun"| db
click telemetry "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/packages/core/src/lib/telemetry-service.ts"
click metrics "https://github.com/bronz3beard/ai.tech-lead-stack/blob/main/apps/dashboard/src/app/dashboard/page.tsx"
classDef app fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554
classDef coreNode fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d
classDef extNode fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337
class webchat,webreflex,metrics app
class mcpcalls,telemetry coreNode
class db extNode
[!NOTE] Related project — SML Gate (
small-language-model-gate, CLIslm-gate) — a local AI routing and pre-processing layer that uses a small, free local model via Ollama to intercept, compress, and answer easy or repetitive prompts before they reach your paid subscription or API cloud model, cutting token spend and protecting your monthly quota. Itsmcp-gatelayer can sit in front of this stack's MCP server (TLS_ADAPTER=on+DOWNSTREAM_MCPpointing atdist/mcp-server.mjs) to condense tool and skill payloads before they hit your editor's context window.Explore SML Gate on GitHub →
Commands Quick Reference
Which tier am I on?
How to decide, the current platform facts, and what the L0–L3 isolation levels mean: Choosing a tier.
Requirements
- RTK (Runtime Toolkit):
curl -fsSL https://raw.githubusercontent.com/rtk-ai/rtk/refs/heads/master/install.sh | sh - GitHub CLI (gh): Required for automated PR management.
- Browsers (Playwright):
npx playwright install chromium - Python Deps:
pip install python-dotenv playwright - System: Access to your local Chrome User Data Directory.
- Firecrawl API: (Optional) For the
planning-expertto read external links.
Set this up with an AI assistant
There's a prompt you can paste into any AI coding assistant (Claude Code, Cursor, Gemini, Codex, or a plain chat window). It checks your computer, asks how technical you are and what you want help with, sets everything up from the npm package, and ends with a short "start here" summary. If something you need is missing, it stops, tells you how to get it, and carries on when you're ready. Keys never go through the chat.
Get the prompt, paste it into your assistant, and
answer its questions. Afterwards, keep asking it about the skills, the dev team,
the reflexion loop, the web app and usage metrics. The same page lists five
things to check before you trust the result, and a block to keep in AGENTS.md.
Moving from a downloaded folder? The prompt handles that too: it removes the old setup, then does a fresh install. To do it step by step yourself, including keeping your old settings, follow Switch Tech-Lead Stack to npm.
🚀 Quick Start
1. Installation
Only need the MCP server? It is published on npm as
tech-lead-stack, with the
skills bundled. Point your MCP client at npx -y tech-lead-stack, for example
in Claude Code:
claude mcp add tech-lead-stack -- npx -y tech-lead-stack
Or let it set up every editor on this computer for you, including putting it behind a gateway such as slm-gate if you use one:
npx -y tech-lead-stack@latest init
Doing it by hand behind a gateway: Without a clone: start the stack with npx.
For the full stack (IDE commands, project linking, the dashboard), clone this repo and link it globally for easy access:
# Recommended: link the repo's own commands once, no hardcoded paths.
# Run this inside the tech-lead-stack checkout:
# npm link
# That gives you `lead-init`, `lead-clean` and `lead-run` everywhere.
# Alternative: shell aliases, if you would rather not link globally.
# Add these to your ~/.zshrc, replacing the path with your checkout.
alias lead-init='bash /path/to/tech-lead-stack/install.sh --link .'
# Cursor: register skills globally (~/.cursor/skills/) without touching your app repo
alias lead-init-cursor='bash /path/to/tech-lead-stack/install.sh --link . --ide cursor'
# Continue: register skills and MCP globally (~/.continue/config.yaml) without touching your app repo
alias lead-init-continue='bash /path/to/tech-lead-stack/install.sh --link . --ide continue'
# Claude Code: generate /tls:<name> slash commands + user-scope MCP, globally
alias lead-init-claude='bash /path/to/tech-lead-stack/install.sh --link . --ide claude-code'
# Add an IDE to a project you already linked, without re-running the full install
alias lead-ide-only='bash /path/to/tech-lead-stack/install.sh --link . --ide-only --ide'
2. Initialize a Project
Navigate to any repository you want to automate and run the new alias:
lead-init
Next steps
- Set up your editor: see Supported Editors & Agents below.
- Connect the MCP server directly, through
install.sh, or behind SLM Gate: Running the MCP server. - Choose models per role, or run fully offline: Configuration.
- Every install flag, and how to remove everything again: Install, Link & Uninstall.
Supported Editors & Agents
Every row above is removed by the same command: lead-clean --global --apply.
"Verified" means a real session invoked a skill through that client and the MCP
get_skill call succeeded. Anything marked unverified is wired up and expected
to work, but has not been confirmed by hand. Reports welcome.
🧹 Resetting a Project
Full detail lives in Install, Link & Uninstall. This is the short version.
Unlink one project. The default. Removes the .ai, .agents and
AGENTS.md symlinks and the two copied GitHub files. Your editor setup keeps
working for every other project you have linked.
lead-clean # the current directory
lead-clean ../other-project # somewhere else
lead-clean --dry-run # preview, delete nothing
Remove the editor setup from this machine. Opt-in, and previews unless you
add --apply. Covers every platform at once: Claude Code, Cursor, Continue,
Cline, Gemini and Claude Desktop.
lead-clean --global # show exactly what would go
lead-clean --global --apply # remove it
It deletes only what points at your checkout. An unrelated MCP server in the
same config file survives, as do your account and session state. Every JSON file
it edits is backed up to <file>.bak first.
Safety. Cleanup refuses to run against your home directory, the filesystem root, or the tech-lead-stack repository itself. A copied file you have since edited, such as a customised pull request template, is kept and reported rather than deleted.
Without npm link, call the script directly:
bash /path/to/tech-lead-stack/scripts/cleanup.sh .
Documentation
Getting set up
Editor setup: Antigravity · Claude Code · Cline · Continue · Cursor · Gemini CLI & Desktop
Reference
Guides, designs and decisions
Peripherals & Sibling Apps
These live in their own repositories:
- SLM Gate: A local pre-processing and routing layer that can sit in front of this stack's MCP server. See Running the MCP server.
- Voice Agent Relay: A
local node service that parses spoken transcripts and executes them via
keyless agent CLIs (
agy,claude,codex,cursor-agent). - Voice Assistant App (private repository, not publicly available): A mobile client (iOS/Android) that acts as a hands-free voice interface for the Tech Lead Stack. It connects to the local voice relay to execute codebase changes via voice commands.
Branching Strategy
This repository enforces Trunk Based Development with a rebase-first workflow and squash-and-merge PRs.
For detailed day-to-day workflow examples and guidelines for both developers and AI agents, please refer to the Branch Management Strategy document.
Support, Contributing & Security
- Need help? See SUPPORT.md.
- Found a bug or want to contribute? See CONTRIBUTING.md.
- Found a security vulnerability? Report it privately as described in SECURITY.md. Do not open a public issue.
- Licence: MIT.
Questions or feature requests? Open an issue or join the discussion on GitHub.
