Works with the AI Agents you already use
Claude Code
Cursor
Codex CLI
DeepSeek
Muse+22 moreClaude CodeCursorCodex CLIDeepSeekMuse
Dots
Manus
Grok
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.
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.
Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Agents & Orchestration.
Partial code yields a partial pack; no invented architecture
• Inferred business rules are labeled assumptions
• Not a substitute for team knowledge or formal security audit
• Not for writing features, full code review, or single-bug RCA deep-dives
• Refresh when critical flows, auth, or module boundaries change
How to use
- 1
Connect your agent to the Agensi MCP
Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup
- 2
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 3
Ask for what you need
Your agent loads this skill and uses it on your task. It always asks before buying a skill.
Reviews
No reviews yet
Be one of the first to try it. Every listed skill goes through the trust checks below.
Security scanned
Checked by our 8-point scan before listing
Fresh listing
Recently published to Agensi
Money-back guarantee
Doesn't do what its page promises? You get your money back.
Trust & safety
Security scan
Security scan passed 21 days ago
Permissions
This skill only reads the codebase context you open or paste. It drafts an onboarding pack and agent context. It does not change source files, print secret values, call the network, or deploy. Grant Read only; leave Shell, Write, Browser, Network, and Env off.