Codebase Onboarding Context Pack — Adaptive Ramp + AGENTS.md for AI

Name: Codebase Onboarding Context Pack — Adaptive Ramp + AGENTS.md for AI
Price: 15 USD
Availability: InStock
Author: Agensi

Security-sensitive flags, missing tests, and stale runbooks create high-risk onboarding if nobody names them.

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You say

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.

Your agent does

# 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).

What you get

Ramp a junior or senior engineer into an unfamiliar repositoryBrief a contractor on a scoped bug with a safe first taskGenerate AGENTS.md-style context for Cursor / Claude CodeMap architecture, domain model, and critical flows for an auditorReplace “ask around” onboarding with a documented learning pathFlag tribal-knowledge and security-sensitive onboarding risks

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.

ai-agents

documentation

onboarding

software-architecture