SkillOpt/plugins/devin

SkillOpt-Sleep — Devin integration

Give Devin (Cognition) a nightly sleep cycle via a tiny MCP server that exposes the skillopt_sleep engine as tools. MCP is Devin's supported way to add custom tooling, so this works in Devin's CLI and IDE.

Devin doesn't write transcripts in the format the engine consumes, so this plugin adds a Devin-specific harvester that converts every locally available source into the Claude Code-compatible JSONL the engine reads.

What's here

File	Purpose
mcp_server.py	stdlib-only MCP (stdio) server exposing sleep_* tools
harvest_devin.py	converts Devin ATIF-v1.7 transcripts + agentmemory + .devin/skills into JSONL, with taskKey + outcome envelopes
judge.py	reference judge for the deferred/judge branch of the validation gate
mcp-config.example.json	drop-in MCP server config
devin-rules.snippet.md	paste into .devin/rules/skillopt-sleep.md

What it harvests

Source	Where
Devin transcripts (ATIF-v1.7)	~/.local/share/devin/cli/transcripts/*.json
agentmemory	~/.agentmemory/standalone.json
Skill files	.devin/skills/*/SKILL.md

Workspaces are auto-detected from ~/.config/Devin/User/workspaceStorage/*/workspace.json. After sleep_adopt, the evolved skill is synced to .devin/skills/skillopt-sleep-learned/SKILL.md.

Install

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

1. Register the MCP server. Use mcp-config.example.json as a template; set args to the absolute path of this mcp_server.py. The engine is found automatically (this plugin lives inside the SkillOpt repo). Or via the Devin CLI:

   devin mcp add skillopt-sleep \
     --env "SKILLOPT_DEVIN_CLAUDE_HOME=$HOME/.skillopt-sleep-devin" \
     -- python3 /abs/path/to/SkillOpt/plugins/devin/mcp_server.py
   

2. (Optional) copy devin-rules.snippet.md to .devin/rules/skillopt-sleep.md so Devin proactively offers the tools.

3. Ask Devin: "run the sleep cycle", "what did the last sleep propose?", "adopt it".

Tools

Tool	What it does
sleep_status	nights run so far + latest staged proposal
sleep_dry_run	preview cycle — no staging; a real backend still makes provider calls
sleep_run	full cycle; stages a proposal for review
sleep_adopt	apply the staged proposal; syncs skill to the workspace
sleep_harvest	debug: list the recurring tasks mined
sleep_schedule	install a nightly cron entry (--hour / --minute)
sleep_unschedule	remove the nightly cron entry

Default backend is mock (no API spend); the claude, codex, and copilot backends use the corresponding authenticated CLI and budget. The handoff backend runs the cycle with no model subprocess or API key — the engine writes pending model calls to .skillopt-sleep-handoff/PROMPTS.md + pending.json (exit code 3) and resumes after answers are placed in answers/<id>.md; re-run sleep_run with the same arguments to resume. The seven tools call the same python -m skillopt_sleep actions as the other shared-engine integrations.

Data boundary

The Devin harvester reads local ATIF transcripts, agentmemory, and skill files and converts them into the engine's session format. The mock backend keeps that workflow local. A real backend sends truncated excerpts and derived tasks to the selected provider for mining, replay, judging, and reflection. The conversion step is not a guarantee that outbound prompts contain no secrets; review sensitive sources and provider policy before enabling a real backend. See the shared data-boundary guidance and implemented CLI reference.