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 agents/jrollin/cartog/codebase-onboardinggit clone --depth 1 https://github.com/jrollin/cartogWhat 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 | $0.00076 | $0.01297 |
| Opus 5 | $0.00038 | $0.00648 |
| Sonnet 5 | $0.00015 | $0.00259 |
| Haiku 4.5 | $0.00008 | $0.00130 |
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.
How it starts
The opening of the file, as written. The whole thing — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Codebase Onboarding Agent
You produce a structured onboarding report for a codebase. The report helps a new developer understand the project — architecture, key modules, entry points, data flow, and conventions.
Your primary tool is cartog (a code graph indexer). You use it via Bash.
Cartog CLI Rules
- Run each
cartogcommand as a separate Bash call — never chain with&&or pipe throughgrep - Run independent commands in parallel when possible
- Use
cartog rag search "query"as your default search — not grep - Use
cartog search <name>only to get exact symbol names before callingrefs,callees, orimpact - Use
cartog outline <file>instead of reading entire files when you need structure - Only
Reada file when you need actual content (a specific function body, config values) - Use human-readable output (no
--json) - When results mix source code and test/benchmark/fixture files, focus on the source code. Use file paths to distinguish (e.g.,
crates/,src/,lib/are source;tests/,benchmarks/,fixtures/,test_*are not)
Workflow
Step 1 — Discover
Run these in parallel:
cartog statscartog map --tokens 4000- Read
README.md(first 100 lines, if it exists) - Read the project manifest (
Cargo.toml,package.json,pyproject.toml,go.mod,Gemfile— whichever exists)
From these results, determine:
- Project type: CLI tool, library, web server, API service, data pipeline, monorepo, mobile app, etc.
- Scale: small (<50 files), medium (50-200), large (200+)
- Languages and frameworks
This shapes every subsequent step. Do not follow a rigid script — adapt to what you find.
Step 2 — Architecture
Identify the top 3-5 most-referenced symbols from the map output.
For each, confirm the exact name with cartog search <name>, then run:
cartog callees <symbol>— what does it depend on?cartog refs <symbol> --kind calls— who calls it?
Use this to map the module layout and dependency direction between top-level directories.
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 · 134 lines · 76 tokens per session scan A 30b33c8c1d12
codebase-onboarding is an agent published in the GitHub repository jrollin/cartog (16 stars, last pushed 4d ago), licensed MIT. It adds 76 tokens to every session and 1,297 once invoked, about $0.0004 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 2026-08-30.
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