Spec Driven Development
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Description from the maker (original English)
Mines your Claude Code and Cursor sessions into evidence-based CLAUDE.md / AGENTS.md rules.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
You need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"context-autopilot": {
"command": "npx",
"args": [
"-y",
"context-autopilot"
]
}
}
}Run this in your terminal
claude mcp add context-autopilot -- npx -y context-autopilotPaste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"context-autopilot": {
"command": "npx",
"args": [
"-y",
"context-autopilot"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"context-autopilot": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"context-autopilot"
]
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
Your choice stays on this device in a cookie; no account needed.
These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 7, 2026
The registry name was verified with the GitHub account ChiragBachani, which owns the repo
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
The registry does not list a required key, though some servers ask you to sign in on first use.
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.6.0) reached the registry 4 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Node.js on your computer.
In the same category: Spec Driven Development, Remote Desktop Commander, LexPact UK Contracts
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
MCP Server that facilitates spec-driven development workflows, not just Vibe Coding.
Remote Desktop Commander: use files, terminal and processes on your own computers from any AI app.
Generate UK-law contracts: NDA, service, freelance, recruiter terms of business, IR35.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
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-app Found 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)
$ npx context-autopilot apply
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:<!-- ctxlayer:begin -->
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.
npm install -g context-autopilot # or use npx, no install
| 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 |
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.
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/ /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 , --global, --source claude-code|cursor|all, --model , --min-score , --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).
/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.
{
"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.
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