khashayar
bec23ed020
Wires the skillopt_sleep engine into Devin (Cognition) via an MCP server, following the same thin-shell pattern as plugins/copilot. - mcp_server.py: stdlib-only stdio MCP server exposing the standard sleep_* tools (status, dry-run, run, adopt, harvest). REPO_ROOT defaults to ../.. so it finds skillopt_sleep automatically when run from plugins/devin/. - harvest_devin.py: converts Devin ATIF-v1.7 transcripts, agentmemory, and .devin/skills/*/SKILL.md into the Claude Code-compatible JSONL the engine consumes; enriches with taskKey + outcome envelopes (hard test/build signal or judge rubric). Workspace auto-detection; cross-platform paths. - judge.py, mcp-config.example.json, devin-rules.snippet.md, README.md. - plugins/README.md: add Devin to the platform + install tables. No changes to skillopt_sleep; shells out to `python -m skillopt_sleep` like the other plugins. Pure stdlib; default backend mock (no API spend). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
19 lines
944 B
Markdown
19 lines
944 B
Markdown
# SkillOpt-Sleep (Devin)
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You have access to a nightly self-evolution cycle via the `skillopt-sleep` MCP
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server. Use these tools to improve your long-term skills over time:
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- **`sleep_status`** — how many nights have run + the latest staged proposal
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- **`sleep_dry_run`** — preview a cycle without changing anything
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- **`sleep_run`** — run a full cycle; stages a proposal for review
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- **`sleep_adopt`** — apply the staged proposal to `.devin/skills/skillopt-sleep-learned/SKILL.md`
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- **`sleep_harvest`** — debug: list the recurring tasks mined from recent sessions
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When a user asks about the sleep cycle, skill evolution, or improving your
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long-term memory, prefer calling these tools over explaining the concept.
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Default backend is `mock` (no API spend). Pass `backend: "claude"` or
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`backend: "codex"` with your own API key for real LLM-driven optimization.
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Place this file at `.devin/rules/skillopt-sleep.md` in your workspace.
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