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Grok Bot+18 agentsCodebase Onboarding Context Pack — Adaptive Ramp + AGENTS.md for AI
Security-sensitive flags, missing tests, and stale runbooks create high-risk onboarding if nobody names them.
$15
Codebase Onboarding Context Pack — Adaptive Ramp + AGENTS.md for AI
Example session with this skill installed
Repository: signaldeck-ingest (IoT cold-chain sensor ingest + alert fan-out)
Target: contractor · Goal: fix-bug (alert emails delayed 5–15 min under load)
Scope: FastAPI API + Celery workers (ops UI out of scope)
Tree: apps/api (ingest, alert_evaluator, settings), workers/tasks/deliver_alert.py,
Redis broker, Postgres, tests only for pure evaluator math.
Notes: queue depth grows; ALERT_SYNC_FALLBACK can send email inline on API in staging;
no README; stale runbook; never print secret values.
Ask: full onboarding context pack for this contractor engagement.
- Read your context and instructions
- Compiled the codebase onboarding context
Codebase Onboarding Context Pack
1. Onboarding Summary
| Field | Value |
|---|---|
| Repository / scope | signaldeck-ingest — API + workers |
| Stack | Python · FastAPI · SQLAlchemy · Celery · Redis · Postgres |
| Target & goal | contractor / fix-bug |
| Onboarding difficulty | HARD |
| Context completeness | HIGH-RISK |
2. Difficulty factors
No README · stale runbook · async API/worker split · missing worker tests ·
ALERT_SYNC_FALLBACK tribal knowledge · security-sensitive env secrets (names only)
3–4. Mental model & architecture
Devices POST readings (HMAC) → reading_service → DB → alert_evaluator →
deliver_alert.delay → Celery worker → email/SMS provider.
Bug locus: fan-out / queue path (API can stay fast while emails lag).
5–7. Domain + learning order
Entities: Reading, AlertRule, AlertDelivery
Learn: (1) ingest → (2) evaluate → (3) enqueue/deliver → (4) staff rule CRUD later
8. Top risks
| Risk | Type | Mitigation |
|---|---|---|
| ALERT_SYNC_FALLBACK in staging | tribal / security | Document; align staging to async |
| No worker tests | missing-tests | Add delay-mock + retry tests |
| Secrets in settings | security-sensitive | Flag names only; never print values |
9–10. Path for this contractor
Orient (compose up) → happy-path ingest → async boundary / queue depth → fix-ready hypothesis
Checkpoints: name api/worker/redis roles; point to delivery intent; explain fast API vs late email
11. First contribution
Test that deliver_alert.delay is used on breach and provider is NOT called inline when
ALERT_SYNC_FALLBACK is false. Safe, on the bug path, no real secrets.
12–13. Tribal prompts + AGENTS.md quick pack
Questions on fallback ownership, queue layout, retry semantics, HMAC rotation.
Quick agent pack: key paths, gotchas, do-not-log secrets.
14. Gate
HIGH-RISK until docs/tests/fallback are cleaned up.
Refresh when Celery routing, auth, or fallback flag changes.
15. JSON
Full Context Pack JSON matching schema (difficulty, flows, first_contribution,
agent_context, assumptions).
Connects securely to your tools. The creator never sees your data.
What you get
About this skill
The problem
- New engineers waste days guessing which files matter and which flows are “tribal knowledge.”
- Generic “summarize this repo” prompts produce flat file lists without a learning order or a safe first task.
- AI coding agents need size-appropriate context (quick vs full vs per-module), not a dump of the whole tree.
- Security-sensitive flags, missing tests, and stale runbooks create high-risk onboarding if nobody names them.
What it does
- Rates onboarding difficulty (TRIVIAL → FORMIDABLE) from named factors (docs, tests, coupling, secrets, tribal knowledge).
- Maps architecture, domain model, conventions, and data flow from the actual code.
- Orders critical flows for learning by dependency (foundational path first).
- Builds an adaptive path for target + goal (e.g. contractor + fix-bug vs junior + add-feature).
- Recommends a first contribution that is small, safe, and on a core flow, with acceptance criteria.
- Adds comprehension checkpoints, tribal-knowledge interview prompts, and layered AI agent context packs.
- Issues a completeness verdict: ONBOARDING-READY, NEEDS-DOCUMENTATION, or HIGH-RISK.
Frameworks & tools
- Language- and stack-agnostic (works on Node, Python, Go, monorepos, services, workers).
- Optimized for Claude Code, Cursor, Codex CLI, OpenClaw, and other SKILL.md hosts with file access.
- Produces human onboarding docs plus AGENTS.md-style quick/full/per-module packs for agents.
- No external documentation SaaS required—output is markdown + JSON you keep in-repo.
Why this beats prompting it yourself
- Adaptive to who is onboarding and why, not a one-size summary.
- Dependency-aware learning order instead of alphabetical file lists.
- First task is concrete and verifiable, not “explore the codebase.”
- Risk register for onboarding anti-patterns (missing tests, magic flags, stale docs).
- Honest confidence map; secrets flagged by location only, never printed.
- Layered agent context so small fixes do not overload the model window.
How to install
Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.
- 1
Download the ZIP
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- 2
Unzip into your skills folder
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- 3
Ask your agent to use it
Restart the agent if it was already running. It picks the skill up automatically - no config needed.
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Click the path to copy it. Create the folder if it does not exist yet.
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