AuC is a Python framework for running a single AI agent with an asynchronous, pluggable reasoning loop, language-model adapters, permission levels, and observable events. It is used to build coding and conversational agents with tools, security checks, web interfaces, background jobs, evaluations, and isolated execution. The catalogue entries are skills for extending its agent workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/ufy2024/auc/codebase-onboardingnpx skills add ufy2024/AuC --skill codebase-onboardinggit clone --depth 1 https://github.com/ufy2024/AuCWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ufy2024/auc/codebase-onboarding)<a href="https://agentmods.dev/skills/ufy2024/auc/codebase-onboarding"><img src="https://agentmods.dev/badge/skills/ufy2024/auc/codebase-onboarding.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00053 | $0.02058 |
| Opus 5 | $0.00026 | $0.01029 |
| Sonnet 5 | $0.00011 | $0.00412 |
| Haiku 4.5 | $0.00005 | $0.00206 |
Grade A, and why
codebase-onboarding scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
8 near-identical copies found in the catalogue:
- codebase-onboarding — 94% identical, 26 lines differ
- codebase-onboarding — 94% identical, 26 lines differ
- codebase-onboarding — 94% identical, 26 lines differ
- codebase-onboarding — 94% identical, 26 lines differ
- codebase-onboarding — 92% identical, 34 lines differ
- codebase-onboarding — 92% identical, 63 lines differ
- codebase-onboarding — 92% identical, 25 lines differ
- codebase-onboarding — 92% identical, 25 lines differ
How it starts
The opening of the file, as written. The whole thing — 253 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Onboarding
Systematically analyze an unfamiliar codebase and produce a structured onboarding guide. Designed for developers joining a new project or setting up Claude Code in an existing repo for the first time.
When to Use
- First time opening a project with Claude Code
- Joining a new team or repository
- User asks "help me understand this codebase"
- User asks to generate a CLAUDE.md for a project
- User says "onboard me" or "walk me through this repo"
How It Works
Phase 1: Reconnaissance
Gather raw signals about the project without reading every file. Run these checks in parallel:
1. Package manifest detection
→ package.json, go.mod, Cargo.toml, pyproject.toml, pom.xml, build.gradle,
Gemfile, composer.json, mix.exs, pubspec.yaml
2. Framework fingerprinting
→ next.config.*, nuxt.config.*, angular.json, vite.config.*,
django settings, flask app factory, fastapi main, rails config
3. Entry point identification
→ main.*, index.*, app.*, server.*, cmd/, src/main/
4. Directory structure snapshot
→ Top 2 levels of the directory tree, ignoring node_modules, vendor,
.git, dist, build, __pycache__, .next
5. Config and tooling detection
→ .eslintrc*, .prettierrc*, tsconfig.json, Makefile, Dockerfile,
docker-compose*, .github/workflows/, .env.example, CI configs
6. Test structure detection
→ tests/, test/, __tests__/, *_test.go, *.spec.ts, *.test.js,
pytest.ini, jest.config.*, vitest.config.*
Phase 2: Architecture Mapping
From the reconnaissance data, identify:
Tech Stack
- Language(s) and version constraints
- Framework(s) and major libraries
- Database(s) and ORMs
- Build tools and bundlers
- CI/CD platform
Architecture Pattern
- Monolith, monorepo, microservices, or serverless
- Frontend/backend split or full-stack
- API style: REST, GraphQL, gRPC, tRPC
Key Directories Map the top-level directories to their purpose:
src/components/ → React UI components
src/api/ → API route handlers
src/lib/ → Shared utilities
src/db/ → Database models and migrations
tests/ → Test suites
scripts/ → Build and deployment scripts
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 253 lines · 53 tokens per session scan A 8d9a48a742ab
codebase-onboarding is a skill published in the GitHub repository ufy2024/AuC (1,091 stars, last pushed 1mo ago), licensed MIT. It adds 53 tokens to every session and 2,058 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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