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/stainless-code/codemap/teachnpx skills add stainless-code/codemap --skill teachgit clone --depth 1 https://github.com/stainless-code/codemapWhat 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 | $0.00014 | $0.01218 |
| Opus 5 | $0.00007 | $0.00609 |
| Sonnet 5 | $0.00003 | $0.00244 |
| Haiku 4.5 | $0.00001 | $0.00122 |
Grade A, and why
teach 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Teach
The user wants to learn something over multiple sessions. This is stateful — progress lives in files in a teaching workspace, not in chat memory alone.
Teaching workspace
Default location: teach/<topic-slug>/ at the repo root (never inside src/ package code). User may choose another directory — treat that directory as the workspace root.
Git: workspaces are personal by default — do not commit teach/** unless the user explicitly wants shared onboarding material in the repo (add it to .gitignore otherwise).
State files at workspace root:
| File / folder | Purpose |
|---|---|
MISSION.md |
Why they're learning — grounds every lesson. MISSION-FORMAT.md |
GLOSSARY.md |
Canonical terms once understood. GLOSSARY-FORMAT.md |
RESOURCES.md |
High-trust knowledge + community sources. RESOURCES-FORMAT.md |
NOTES.md |
User teaching preferences, scratchpad |
./learning-records/*.md |
Decision-grade insights (ADR-shaped). LEARNING-RECORD-FORMAT.md |
./lessons/*.html |
Primary teaching unit — one scoped win per file (0001-slug.html, increment) |
./reference/*.html |
Compressed cheat sheets for revisit (syntax, flows, glossaries) |
./assets/* |
Reusable lesson components (stylesheet first) |
Create directories lazily when first needed.
Codemap repo topics
When the mission is this codebase (index, recipes, schema, parsers, adapters, agents init, rules/skills):
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 85 lines · 14 tokens per session scan A 81147930bd8b
teach is a skill published in the GitHub repository stainless-code/codemap (8 stars, last pushed 6d ago), licensed MIT. It adds 14 tokens to every session and 1,218 once invoked, about $0.0001 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 2026-08-31.
Other skills, from other repositories
roam
Codebase comprehension via roam-code CLI. Use when exploring codebases, planning modifications, debugging failures, assessing PR risk, or checking architecture health. Triggers on: understanding project structure, pre-change safety checks, finding symbols/files, blast radius analysis, affected tests, health scoring…
map-learn
Capture reusable lessons after a completed MAP workflow. Use when a MAP run has finished and you want rules written to .claude/rules/learned/ from a workflow summary or handoff. Do NOT use during active implementation.
source-reading
带 AI 精读大型开源仓库，产出每句话都能回溯到源码具体行的书稿、课程或技术文档。核心是零幻觉：每一处引用逐字节核实、行号实读、静默删行由脚本抓出。覆盖锁版本锚点、写逐章大纲、八段结构写章节、编成带封面封底的 HTML 书、机器校验、并行子 Agent 生产六件事。当用户要读懂一个陌生的大型仓库、精读某个开源项目源码、把源码整理成一本书、整理成课程或系列文章、做源码解读、写架构分析、或者要派多个 Agent 并行写技术内容时使用。触发词：精读源码、读源码、源码解读、源码分析、拆解这个项目、这个仓库怎么读、把源码写成课、把源码写成书、写源码精读、架构分析、code walkthrough、带我读代码。.
levelup-specify
Extract Context Directive Records (CDRs) from the current session after completing work. Identifies reusable patterns (rules, personas, examples, evals) and captures directive compliance cases for team-ai-directives.
lane-memory
SMA-style project fact corpus under .agents/memory/. Opt-in via adoc stages.memory.enabled. Use when user says память, lane-memory, corpus, CORE, почему бот забыл, or an agent needs durable non-code facts. Not PROGRESS/LESSONS dumps.
t-doc
Generate project tutorial documentation that reads like a human wrote it, not like AI output. Scans the codebase, extracts architecture/APIs/config/deployment details, and writes structured tutorial docs in the project's documentation target, preferring an existing docs-web site over docs/tutorials/. Use this skill…