microsoft-SkillOpt/plugins/copilot/copilot-instructions.snippet.md at refactor/reflect-default-base

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Yifan Yang f9db99853b feat(plugins): ship SkillOpt-Sleep for Claude Code, Codex, and Copilot

Restructure into plugins/{claude-code,codex,copilot}/ — one engine, three thin
shells, all calling the shared plugins/run-sleep.sh -> python -m skillopt_sleep.

  - claude-code/: existing plugin moved here; runner delegates to the shared
    launcher (fixes repo-root resolution after the move).
  - codex/: ~/.codex/prompts/sleep.md custom prompt + ~/.agents/skills SKILL.md +
    install.sh + AGENTS.md hint — Codex's documented, stable extension surfaces.
  - copilot/: a stdlib-only MCP server (mcp_server.py) exposing sleep_* tools,
    plus mcp-config.example.json and a copilot-instructions snippet. Verified end
    to end (initialize -> tools/list -> tools/call returns real engine output).
  - plugins/README.md overview table; main README News + a dedicated SkillOpt-Sleep
    section; pyproject lists skillopt_sleep as a first-class package.

Decoupling emphasized throughout: open-source tool (skillopt_sleep/) with zero
dependency on the research package. 29 tests pass; all three shells resolve.

Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>

2026-06-08 14:31:52 +00:00

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SkillOpt-Sleep (offline self-evolution)

This project has SkillOpt-Sleep available via an MCP server (skillopt-sleep). It gives the agent a nightly "sleep cycle": it reviews past sessions, replays recurring tasks offline, and consolidates validated memory + skills behind a held-out gate.

When the user asks to "run the sleep cycle", "review my past sessions", "learn my preferences", or "make the agent improve from past usage", use the MCP tools:

sleep_status — what's happened + the latest staged proposal
sleep_dry_run — safe preview, stages nothing
sleep_run — full cycle, stages a reviewed proposal (nothing live changes)
sleep_adopt — apply the staged proposal (backs up first)
sleep_harvest — list mined recurring tasks

Always show the user the held-out baseline → candidate score and the proposed edits before suggesting sleep_adopt. Never hand-edit the user's memory/skill files; only sleep_adopt does that, with a backup.

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SkillOpt-Sleep (offline self-evolution)

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