Yif-Yang
85 lines
3.5 KiB
Markdown
85 lines
3.5 KiB
Markdown
# SkillOpt-Sleep — GitHub Copilot integration
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Give **Copilot** (CLI or VS Code) a nightly **sleep cycle** via a tiny **MCP
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server** that exposes the `skillopt_sleep` engine as tools. MCP is GitHub's
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supported way to extend Copilot, so this works across Copilot CLI, VS Code, and
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other MCP clients with the same server.
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## What's here
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| File | Purpose |
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| `mcp_server.py` | stdlib-only MCP (stdio) server exposing `sleep_*` tools |
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| `mcp-config.example.json` | drop-in MCP server config |
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| `copilot-instructions.snippet.md` | paste into `.github/copilot-instructions.md` |
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## Install
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Requires Python ≥ 3.10. No third-party packages — the server is pure stdlib.
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1. **Register the MCP server.** Add the server to your Copilot MCP config
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(Copilot CLI: `~/.copilot/mcp-config.json`; VS Code: your MCP settings).
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Use `mcp-config.example.json` as a template — set `SKILLOPT_SLEEP_REPO` to
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this repo's path:
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```json
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{
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"mcpServers": {
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"skillopt-sleep": {
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"command": "python3",
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"args": ["/abs/path/SkillOpt/plugins/copilot/mcp_server.py"],
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"env": { "SKILLOPT_SLEEP_REPO": "/abs/path/SkillOpt" }
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}
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}
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}
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```
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2. **(Optional) Tell Copilot about it.** Append
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`copilot-instructions.snippet.md` to your repo's
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`.github/copilot-instructions.md` so Copilot reaches for the tools when the
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user asks to "run the sleep cycle".
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## Use
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Ask Copilot things like *"run the sleep cycle"*, *"what did the last sleep
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propose?"*, *"adopt the staged sleep proposal"*. The server exposes seven MCP
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tools: `sleep_status`, `sleep_dry_run`, `sleep_run`, `sleep_adopt`,
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`sleep_harvest`, `sleep_schedule`, and `sleep_unschedule`.
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Each tool takes optional `project`, `backend` (`mock`/`claude`/`codex`/`copilot`), and
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`scope` arguments. Default backend is `mock` (no API spend). The `copilot`
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backend drives the GitHub Copilot CLI (`copilot -p ... --output-format json`)
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and requires the `copilot` CLI to be installed and authenticated.
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Harvesting is local and read-only, and the default `mock` backend makes no
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provider calls. A real backend sends truncated transcript excerpts and derived
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tasks to the selected provider. Outbound prompts are not currently guaranteed
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to be secret-free; review sensitive data and provider policy first. See the
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[shared data-boundary guidance](../README.md#data-boundary).
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For speed, the `copilot` backend runs each call against an isolated
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`COPILOT_HOME` with built-in MCP servers and custom instructions disabled, so
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your user MCP servers (including this project's own) are not spawned per call
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(~5x faster). Override with `SKILLOPT_SLEEP_COPILOT_HOME=<dir>`, pick a model
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with `SKILLOPT_SLEEP_COPILOT_MODEL`, or set `SKILLOPT_SLEEP_COPILOT_FULL_ENV=1`
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to use your real Copilot environment instead.
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## Verify the server directly (no Copilot needed)
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```bash
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printf '%s\n' \
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'{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' \
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'{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
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| SKILLOPT_SLEEP_REPO="$(pwd)" python3 plugins/copilot/mcp_server.py
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```
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You should see the server info and all seven `sleep_*` tools.
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## Notes / status
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- MCP is the stable, official Copilot extension surface, so this is the most
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portable shared-engine integration (one server → CLI + IDE).
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- The MCP schema exposes the main CLI's implemented controls, including task and
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session caps, target-skill selection, scheduling, and staged adoption. It does
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not add experiment-only gate, rollout, token/time-budget, or optimizer/target
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split flags. See the [shared CLI reference](../README.md#supported-cli-surface).
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