Context Autopilot

io.github.ChiragBachani由 GitHub 帳號 ChiragBachani 發布v0.6.0更新於 Oct 25, 2026

Mines your Claude Code and Cursor sessions into evidence-based CLAUDE.md / AGENTS.md rules.

已驗證STDIO僅桌面知識與記憶開發者工具

概覽

AI 產生的概覽

一個本機 MCP 伺服器,把 Claude Code 與 Cursor 工作階段歷史挖掘成有證據支撐、由你批准的 CLAUDE.md 與 AGENTS.md 規則。

可以做什麼
列出有可觀察 Claude Code 或 Cursor 工作階段歷史的專案,並掃描其中重複的指示、糾正和被拒絕的工具呼叫,附上原文引用作為證據。把這些訊號提煉成專案層級或跨所有專案的全球規則提案。把已保存在專案記憶或上下文檔案中的規則提升到全球規則檔案。審閱提案,只把你接受的規則寫入 CLAUDE.md 和 AGENTS.md 的受管區塊。找出過時的上下文檔案引用,例如遺失的檔案或已移除的 npm 指令碼,並以非零結束碼供 CI 使用。
適用情境
當你反覆向編碼助理重複同樣的慣例,希望根據自己過去的工作階段而非一般性的儲存庫掃描來建立持久上下文檔案時使用。適合同時使用 Claude Code 和 Cursor、並希望在寫入任何內容前明確批准或拒絕的開發者。
執行需求
透過 npx 以 stdio 在本機執行,需要 Node.js;挖掘 Cursor 工作階段需要 Node 22 或更新版本,較舊版本會略過該來源。提煉透過你現有的 claude CLI 完成,不需要 API 金鑰。它會讀取本機 Claude Code 記錄和 Cursor 的本機 SQLite 儲存。
安裝前請注意
被接受的規則會寫入 CLAUDE.md 和 AGENTS.md,全球模式或 promote 會寫入你個人的 ~/.claude/CLAUDE.md;promote 只讀取專案檔案。從工作階段擷取的訊號引用會傳送給你已在使用的編碼模型,除此之外不會上傳其他內容。套用前請審閱提案,因為它們會改變後續助理工作階段讀取的檔案。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Context Autopilot,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Context Autopilot

Automated context collection for coding agents. Mines your real agent sessions — every instruction you repeated, every correction you made, every tool call you rejected — and distills them into CLAUDE.md / AGENTS.md rules you approve.

Part of The Context Layer.

Run in your terminal (it's a plain CLI — don't paste this into a chat):

$ npx context-autopilot scan
Scanned 3 session(s) for ~/projects/my-appFound 26 signal(s):
  [CORRECTION] ×2 across 2 session(s)  (score 9)      "There are still so many buttons that dont work, like the publish…"  [REPEATED] ×4 across 3 session(s)  (score 10)      "Do not reference the legacy directory. Only work within…"
$ npx context-autopilot distill
[1/8] Perform click-and-type tests before reporting UI work complete  (confidence: high)    + Before declaring any screen done, click every button and verify it works.    evidence:      · 2026-06-27 — "There are still so many buttons that dont work…"
$ npx context-autopilot apply

Why

Every session starts blank, so you re-teach your agent the same conventions — and when you forget, it repeats the same mistakes. Hand-writing context files works but nobody keeps them current. And naive auto-generation is worse: research on LLM-generated context files found they reduce task success and raise cost, because repo scans produce generic filler.

Context Autopilot takes a third path: evidence. Your session history is a literal record of what the agent got wrong and what you said to fix it. Autopilot mines that record and only proposes rules your own words support — each one shipped with the quotes that justify it.

How it works

  1. Observe — ctxlayer scan parses your local Claude Code transcripts (~/.claude/projects) and Cursor sessions and extracts three signal types: instructions repeated across sessions, corrections after the agent went wrong, and rejected tool calls. Runs 100% locally.
  2. Distill — ctxlayer distill sends the signals (not your history) through Claude — via your existing claude CLI, no API key needed — and gets back imperative, project-specific rules with evidence and confidence ratings.
  3. Approve — ctxlayer apply walks you through each proposal. Accepted rules land in a managed block:
markdown
<!-- ctxlayer:begin -->## Learned conventions (Context Autopilot)
- **Staff login cannot access admin view** — When authenticated as staff, the admin role toggle must be hidden or disabled.<!-- ctxlayer:end -->

Hand-written content is never touched; re-runs update the block idempotently. Rules are written to both CLAUDE.md and AGENTS.md, so Claude Code, Cursor, Copilot, Codex, and every AGENTS.md-aware agent benefits.

Install

bash
npm install -g context-autopilot   # or use npx, no install

Commands

CommandWhat it does
ctxlayer projectsList projects with observable session history (Claude Code + Cursor)
ctxlayer scanMine signals from this project's sessions
ctxlayer distillDistill signals into proposals (.ctxlayer/proposals.json)
ctxlayer promoteScan every project's saved memory (auto-memory + CLAUDE.md) for rules that belong in your global ~/.claude/CLAUDE.md; --dry-run lists candidates without calling a model
ctxlayer applyReview proposals interactively; write accepted ones
ctxlayer checkFast, model-free: how many new signals since the last distill? --hook prints a nudge only past --threshold (default 3), else stays silent
ctxlayer staleFind context-file references the repo has outgrown — missing files, removed npm scripts. Exits 1 on findings, so it drops straight into CI
ctxlayer exportExport distilled entries as Agent Operating Procedure JSON

Global mode

bash
ctxlayer distill --global

Project context files hold repo conventions — but some feedback is about you: "explain things in plain English", "don't build while I'm brainstorming", "run independent work in parallel". Global mode mines all your projects across all your tools for exactly that, and maintains a managed block in your personal ~/.claude/CLAUDE.md, so every future session in every project starts already knowing how you like to work. Rules that mention a specific project are excluded by design — those belong in the project's own context file.

Promote saved memory to global

bash
ctxlayer promote

Where global mode mines your session transcripts, promote mines the memory your agents have already written down: each project's auto-memory files (~/.claude/projects/<name>/memory/) and each repo's CLAUDE.md/AGENTS.md. Rules about how you work — or rules duplicated in two or more projects — are proposed for your global ~/.claude/CLAUDE.md, generalized and with the source files as evidence. Additive only: project files are read, never edited. Anything already covered globally is filtered before the model is even called.

Options: --project <path>, --global, --source claude-code|cursor|all, --model <model>, --min-score <n>, --yes, --json.

Cursor session mining reads Cursor's local SQLite storage via Node's built-in node:sqlite (Node 22+; on older Node the Cursor source is skipped gracefully).

Claude Code plugin

/plugin marketplace add chiragbachani/context-autopilot/plugin install context-autopilot@the-context-layer

Then ask Claude to "update project context from my session history" — or don't ask at all: the plugin ships a SessionStart hook that runs ctxlayer check (fast, no model call) when a session begins. If enough new signals have accumulated since the last distillation, Claude gets a nudge to offer distillation at a natural pause. No new signals → complete silence.

MCP server

json
{  "mcpServers": {    "context-autopilot": {      "command": "npx",      "args": ["-y", "-p", "context-autopilot", "ctxlayer-mcp"]    }  }}

Exposes list_observable_projects, scan_context_signals, distill_context_proposals, distill_global_context, promote_to_global, apply_context_proposals, and find_stale_context.

The approval loop closes entirely inside chat: distill tools return each proposal with its evidence and instruct the agent to ask you which to accept; apply_context_proposals then writes exactly the titles you approved, remembers the ones you rejected (never re-proposed), and leaves the rest pending. No tool ever touches a context file without your explicit decision.

FAQ

How is this different from Claude Code's /insights? /insights is the same core observation — instructions you repeat belong in CLAUDE.md — shipped as a personal usage report: an HTML page with suggestions you copy-paste by hand, Claude Code only. Context Autopilot is the pipeline version: it also mines Cursor history, attaches your verbatim quotes as evidence to every rule, runs an explicit approve/reject flow, writes accepted rules into managed blocks in both CLAUDE.md and AGENTS.md (so Codex/Copilot/Cursor benefit), maintains a global cross-project rules file, and adds a CI staleness check. Fully open source and local.

How is this different from Claude Code's auto-memory? Auto-memory captures what the model notices live, in the moment, in one harness. Autopilot is retroactive and systematic: it mines months of existing history across tools, and finds cross-session patterns (you said it 6× in 4 sessions) that no single live session can see.

Troubleshooting

  • "Skill not found" / agent can't see the tools — MCP servers load at session start. After installing, start a new session (resumed/old sessions won't have the tools), and say "MCP tools" rather than a slash command: "Using the context-autopilot MCP tools, distill this project's context proposals."
  • Everything else (evidence presentation, approval flow, error hints) is built into the server itself — the tool results tell the agent exactly what to show and when to ask you.

Privacy

Everything runs on your machine. Transcripts are parsed locally; only the extracted signals (short quotes of your own instructions) are sent to the model you already use for coding. Nothing is uploaded anywhere else, ever.

Roadmap

Coding agents are chapter one. The engine is source-agnostic — it distills observations of work into Agent Operating Procedures (AOPs):

  • Now: Claude Code + Cursor sessions → CLAUDE.md / AGENTS.md; global cross-project rules (--global); staleness detection (ctxlayer stale)
  • Next: team-shared context; a GitHub Action for context linting in CI
  • Later: browser-workflow observation → AOPs for web tasks; ambient capture — until agents absorb the work you repeat, without you ever "building an agent"

License

MIT © The Context Layer

來源:README.md,提交 9ece859

工具

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版本歷史

1
  1. v0.6.0最新Sep 16, 2026

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