microsoft--skillopt/plugins/copilot/README.md

SkillOpt-Sleep — GitHub Copilot integration

Give Copilot (CLI or VS Code) a nightly sleep cycle via a tiny MCP server that exposes the skillopt_sleep engine as tools. MCP is GitHub's supported way to extend Copilot, so this works across Copilot CLI, VS Code, and other MCP clients with the same server.

What's here

File	Purpose
mcp_server.py	stdlib-only MCP (stdio) server exposing sleep_* tools
mcp-config.example.json	drop-in MCP server config
copilot-instructions.snippet.md	paste into .github/copilot-instructions.md

Install

Requires Python ≥ 3.10. No third-party packages — the server is pure stdlib.

1. Register the MCP server. Add the server to your Copilot MCP config (Copilot CLI: ~/.copilot/mcp-config.json; VS Code: your MCP settings). Use mcp-config.example.json as a template — set SKILLOPT_SLEEP_REPO to this repo's path:

   {
     "mcpServers": {
       "skillopt-sleep": {
         "command": "python3",
        "args": ["/abs/path/SkillOpt/plugins/copilot/mcp_server.py"],
        "env": { "SKILLOPT_SLEEP_REPO": "/abs/path/SkillOpt" }
       }
     }
   }
   

2. (Optional) Tell Copilot about it. Append copilot-instructions.snippet.md to your repo's .github/copilot-instructions.md so Copilot reaches for the tools when the user asks to "run the sleep cycle".

Use

Ask Copilot things like "run the sleep cycle", "what did the last sleep propose?", "adopt the staged sleep proposal". The server exposes seven MCP tools: sleep_status, sleep_dry_run, sleep_run, sleep_adopt, sleep_harvest, sleep_schedule, and sleep_unschedule.

Each tool takes optional project, backend (mock/claude/codex/copilot/handoff), and scope arguments. For sleep_adopt, first inspect sleep_status, then use the adoption controls that match the reviewed staging manifest:

• staging — exact staging directory to adopt instead of the latest night
• skills — array of skill names to adopt; each is passed as one repeated --skill argument without shell interpolation
• all_skills — adopt every staged per-skill proposal
• legacy — adopt only the legacy managed SKILL.md/CLAUDE.md pair

Choose one selection mode (skills, all_skills, or legacy) and do not combine them. A bare sleep_adopt remains compatible with legacy-only staging; fan-out staging requires an explicit selection.

Tool results preserve the engine's exit_code in structuredContent. Ordinary nonzero exits set isError: true; exit 3 is the expected handoff_pending state and is not an MCP tool error. With json: true, text content is the engine's parseable JSON stdout, while diagnostics remain separate in structuredContent.

Default backend is mock (no API spend). The copilot backend drives the GitHub Copilot CLI (copilot -p ... --output-format json) and requires the copilot CLI to be installed and authenticated.

Harvesting is local and read-only, and the default mock backend makes no provider calls. A real backend sends truncated transcript excerpts and derived tasks to the selected provider. Outbound prompts are not currently guaranteed to be secret-free; review sensitive data and provider policy first. See the shared data-boundary guidance.

For speed, the copilot backend runs each call against an isolated COPILOT_HOME with built-in MCP servers and custom instructions disabled, so your user MCP servers (including this project's own) are not spawned per call (~5x faster). Override with SKILLOPT_SLEEP_COPILOT_HOME=<dir>, pick a model with SKILLOPT_SLEEP_COPILOT_MODEL, or set SKILLOPT_SLEEP_COPILOT_FULL_ENV=1 to use your real Copilot environment instead.

Verify the server directly (no Copilot needed)

printf '%s\n' \
  '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{}}' \
  '{"jsonrpc":"2.0","id":2,"method":"tools/list"}' \
  | SKILLOPT_SLEEP_REPO="$(pwd)" python3 plugins/copilot/mcp_server.py

You should see the server info and all seven sleep_* tools.

Notes / status

• MCP is the stable, official Copilot extension surface, so this is the most portable shared-engine integration (one server → CLI + IDE).
• The MCP schema exposes the main CLI's implemented controls, including task and session caps, target-skill selection, scheduling, and staged adoption. It does not add experiment-only gate, rollout, token/time-budget, or optimizer/target split flags. See the shared CLI reference.