| name | onboard |
| description | Use when user asks to "onboard to project", "what does this project do", "summarize codebase", "get oriented", "new to this repo", "quick overview", "project summary", "codebase tour", "help me understand this code". Collects project data automatically then guides interactively. |
| argument-hint | [path] [--depth=quick|normal|deep] |
Onboard Skill
Automated data collection + LLM synthesis + interactive guidance.
Architecture
/onboard
│
├─ Phase 1: collector.js (pure JS, zero LLM)
│ ├─ scanManifest() → package.json/Cargo.toml/go.mod
│ ├─ scanStructure() → directory tree with file counts
│ ├─ readFileIfExists()→ README.md, CLAUDE.md
│ ├─ scanCI() → .github/workflows, Dockerfile
│ ├─ getGitInfo() → branch, commit count, remote
│ ├─ getRepoIntel() → onboard + hotspots + conventions +
│ │ projectInfo + entry-points + slop
│ └─ getRepoMap() → symbols, imports, exports (if available)
│
├─ Phase 2: onboard-agent (Sonnet)
│ ├─ Synthesize collected data into readable summary
│ ├─ Read key source files for architecture understanding
│ └─ Present summary to user
│
└─ Phase 3: Interactive (conversational)
├─ "What do you want to do?"
├─ Guide to files using coupling + ownership + symbols
└─ Answer follow-up questions with full context
Depth Levels
| Level | What's collected | Time |
|---|
quick | Manifest + README + structure + git info | ~2s |
normal | + CLAUDE.md + CI + repo-intel (auto-generates if missing) | ~5s |
deep | + repo-map AST symbols | ~15s |
Data Flow
The collector produces a single JSON object:
{
"manifest": { "type": "npm", "name": "...", "language": "typescript", "scripts": [...], "dependencies": {...} },
"readme": "# Project\n...",
"claudeMd": "# Rules\n...",
"structure": [{ "path": "src/", "depth": 1, "files": 23, "dirs": 5 }, ...],
"ci": { "github": true, "workflows": ["ci.yml", "release.yml"], "dockerfile": false },
"gitInfo": { "branch": "main", "commitCount": 232, "lastCommit": "2026-03-15", "remoteUrl": "..." },
"repoIntel": {
"onboard": { "language": "typescript", "structure": "single package", "health": "active", ... },
"hotspots": [...],
"conventions": { "commit": "conventional", ... },
"projectInfo": { "readme": {...}, "manifest": {...} },
"entryPoints": [{ "path": "src/main.rs", "name": "main", "kind": "main" }, ...],
"slop": {
"orphanExports": [...], "passthroughWrappers": [...],
"alwaysTrueConditions": [...], "commentedOutCode": [...],
"counts": { "orphanExports": 3, "passthroughWrappers": 0, "alwaysTrueConditions": 0, "commentedOutCode": 1 }
}
},
"repoMap": { "totalFiles": 45, "totalSymbols": 312, "keyExports": { "src/index.ts": ["Queue", "Worker", ...] } }
}
This is passed as the agent's prompt context. The agent reads key files to fill gaps, then presents a synthesized summary. No data collection happens in the LLM - only synthesis and guidance.
Repo-Intel Data
Expected: the orchestrator (the command that spawned this agent) has already checked <stateDir>/repo-intel.json and either pre-fetched the data into your context or skipped (user declined to generate). Do not call AskUserQuestion here - subagents cannot interact with the user.
If the pre-fetched data is empty, proceed with the available context. The orchestrator has already made the decision on the user's behalf.
Binary: agent-analyzer auto-downloads to ~/.agent-sh/bin/ from agent-sh/agent-analyzer GitHub releases (~10 MB) on first use. The lib/agentsys resolver locates the agentsys install (CC marketplace clone, npm global, or sibling repo).
