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bootstrap
Set up chamnan in this repository for the first time — build the architecture index, measure how well the code describes itself, fill in missing file comments, and record a baseline. Run once per repo.
Choose how to use this skill
You do not need every option. Choose the path your AI client supports. The stable page stays the same; versioned files are immutable.
1. Native installer
This listing has no registered native installer command. Use the complete package or source fallback below, depending on what your client supports.
Do not guess an installer command or replace an existing version without reviewing the diff.
2. Complete package recommended
Download the ZIP when available. It includes SKILL.md plus the references, security notes and version metadata.
No complete ProSkills package is published for this listing yet.3. Prompt-only
Copy the prompt above when the agent can read the stable page or when you want to adopt the workflow without installing a skill.
Need only the instruction file?
Download SKILL.md only if your client requires a single file. The complete ZIP is safer for a full installation because it preserves the references and release context.
No path installs or executes anything by itself. Your agent still needs access to the project files. Before updating, compare the installed version and review the diff.
// RATINGS
Not yet listed on ClawHub or SkillsMP
// README
chamnan
Each is a short page — what this is, the problem it solves, how to install it, and what to know before you do. They carry no numbers on purpose. Measurements change every release and a translated page does not: across large open-source repositories, once a translation is merged the English source takes a median of 8.5 more commits in six months while the translation takes a median of 0 (arXiv:2508.02497). So the numbers live here, in English, in Evidence, and every translated page links to them rather than repeating them. A translated page that goes a year without an edit is still correct.
ชำนาญ (cham-nan) — Thai for the fluency that only comes from doing something again.
A context index that makes a repository know itself and preserve the engineering context built while you work with it, so an agent stops rediscovering both. It ships as a Claude Code plugin and as a plain command-line tool, and writes for two dozen other agents besides — any model, any vendor, macOS, Linux, Windows and WSL. It builds an index the agent reads instead of scanning files, keeps the work state and the decisions that would otherwise be lost between sessions, and accumulates the procedures and tools you keep re-deriving.
If you arrived here from a search, this is what it is
Written plainly on purpose. 44.2% of what an AI search engine quotes comes from the first 30% of a page, so the numbers that matter should be here rather than four screens down — and every one of them links to how it was measured.
chamnan is a context index for the cost of re-reading, not the cost of writing. It builds an index of the repository that a session is handed at startup, keeps the decisions and work state that would otherwise be lost between sessions, and does all of it in Python's standard library with no network calls at runtime, no database, no daemon, and no embedding model. Everything it writes is plain markdown committed beside the code.
It is not tied to one tool. It ships as a Claude Code plugin and as an ordinary command-line tool; it writes for two dozen other agents including Cursor, Windsurf, Copilot, Zed, Aider, Gemini CLI and Hermes Agent; it runs on macOS, Linux, Windows and WSL — the first three exercised in CI on every commit; and it works with any model from any vendor, because the model decides only how much of the index is worth sending, never where anything goes.
| what people actually ask | the short answer |
|---|---|
| "a Claude Code plugin to reduce token usage" | It replaces file scanning with an index. On the polyglot test corpus, 11,560,484 tokens of source become a 51,937-token index — 223× (last measured on an unpublished corpus, not independently reproducible — see the corpus section), and 28.8× on the published corpus, measured 2026-09-08, which omits 20 MB of binary attachments — of which 159 to 1,571 reach each session — 521 to 4,173 counting the whole injected block — measured across four real repositories, 2026-09-16. |
| "my agent keeps re-reading the same files" | Measured across 12,332 re-read events in six working sessions: the injected roll-up named 22.7% of them by alphabet, 35.6% once ranked by git churn. |
| "my SessionStart hook output is being truncated" | Claude Code cuts a hook's stdout above 10,000 bytes to its first 2,048 (#70460, #44086). 47 of 120 measured injections lost 77–86% each. output_byte_ceiling bounds the block in bytes so nothing is cut. |
| "how do I keep context between Claude Code sessions" | Session records, decisions, rules and open threads, injected at the next start. A compaction pass recovers about 63% of facts and destroys file paths first; re-injecting exact paths is the repair. |
| "does a context file actually help" | Not with correctness. Measured elsewhere: human-written context files +4%, LLM-generated −2%, and a 288-attempt study found no correctness gain but −29% runtime and −17% output tokens. chamnan claims the second thing, not the first — see what a context file measurably does, which includes the finding that argues against its own flagship feature. |
| "does it work with Cursor / Windsurf / Copilot / Zed / Aider" | Yes — an adapter for each, writing the file that tool actually reads. chamnan-context --write <name>. The list |
| "does it work on Windows" | Yes, and on macOS and Linux — those three run in CI on every commit, WSL as Linux. Per-OS instructions |
| "does it work with GPT / Gemini / Kimi / a local model" | Yes. The index is text; the model only sets the budget. Unrecognised names still work, and --window is exact. How |
| "does it work with Hermes Agent" | Yes — it writes .hermes.md, the file Hermes gives highest priority. How |
| "is it safe to point it at a private repo" | It never makes a network call. Its credential redactor scores 99.0% recall / 100% precision on a 99-secret, 48-decoy corpus — a corpus of credentials, so that is what it finds among secrets it is shown, not a rate over everything that passes through. The ceiling it cannot reach is stated next to the number. |
Every number here is sourced in Evidence — including the measured findings that argu
// HOW IT'S BUILT
KEY FILES