Mines your Claude Code and Cursor sessions into evidence-based CLAUDE.md / AGENTS.md rules.
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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):
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.
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.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.ctxlayer apply walks you through each proposal. Accepted rules land in a managed block: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.
| Command | What it does |
|---|---|
ctxlayer projects | List projects with observable session history (Claude Code + Cursor) |
ctxlayer scan | Mine signals from this project's sessions |
ctxlayer distill | Distill signals into proposals (.ctxlayer/proposals.json) |
ctxlayer promote | Scan 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 apply | Review proposals interactively; write accepted ones |
ctxlayer check | Fast, model-free: how many new signals since the last distill? --hook prints a nudge only past --threshold (default 3), else stays silent |
ctxlayer stale | Find context-file references the repo has outgrown — missing files, removed npm scripts. Exits 1 on findings, so it drops straight into CI |
ctxlayer export | Export distilled entries as Agent Operating Procedure JSON |
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.
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).
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.
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.
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.
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.
Coding agents are chapter one. The engine is source-agnostic — it distills observations of work into Agent Operating Procedures (AOPs):
--global); staleness detection (ctxlayer stale)MIT © The Context Layer
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