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553446575a
Adds a thin OpenClaw shell wrapping the SkillOpt-Sleep engine. Enables nightly validation-gated skill improvement cycles for OpenClaw agents. Components: - skillopt_sleep_openclaw.py: DeepSeek V4 Pro + Ollama nomic-embed-text backend, mirroring the Claude/Codex/Copilot backend pattern. - run_sleep.py: CLI entry point supporting dry-run and pre-built task files. - run_sleep_cron.sh: bash wrapper for nightly cron invocation. - slash_sleep.py: /sleep command (status / run / adopt / reject / cost). - config.json: engine config tuned for our stack. - SKILL.md: OpenClaw skill manifest. - tests/: 14 held-out tasks across 3 categories (research-cron, devops, wiki). OpenClaw is the 4th ecosystem in which SkillOpt-Sleep can be deployed, joining Claude Code, Codex, and Copilot. The shell follows the same single-engine / thin-shell pattern as the existing three plugins. End-to-end tested: pipeline runs against real OpenClaw session transcripts, gate correctly rejects non-improvements, staging artifacts land in ~/.skillopt-sleep/staging/<night>/. Cost: ~$0.02/night on DeepSeek V4 Pro.
97 lines
3.2 KiB
Markdown
97 lines
3.2 KiB
Markdown
---
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name: skillopt-sleep
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description: Validate and refine agent skills through nightly sleep cycles with held-out gates. Wraps Microsoft's SkillOpt-Sleep engine for the OpenClaw/DeepSeek stack.
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---
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# skillopt-sleep — OpenClaw Adaptation of Microsoft SkillOpt-Sleep
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A nightly self-improvement loop that reads our session transcripts, mines recurring workflow patterns, replays them with proposed skill edits, and gates the proposals against a held-out test set. Only improvements that beat baseline are staged for human adoption.
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## When To Use
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- After Hermes's Weekly Skill Review (or as its replacement)
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- When a skill is being used 10+ times/week and could be tighter
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- Before promoting a new skill from `skill-proposals/` to `skills/`
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- When a skill regresses in observed quality
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## What It Does (One Cycle)
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```
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harvest session transcripts -> mine recurring task patterns
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-> replay each pattern (current skill vs proposed)
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-> GATE: must improve held-out score
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-> stage proposal
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-> Ethan adopts (manual)
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```
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Nothing live changes until Ethan adopts. Every adopt backs up first.
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## Architecture
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```
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skills/skillopt-sleep/
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├── SKILL.md # this file
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├── config.json # engine config (backend, budgets, etc.)
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├── run_sleep.py # entry point
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└── skillopt_sleep_openclaw.py # DeepSeek/Ollama backend
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```
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The engine itself is at `~/.openclaw/workspace/SkillOpt/skillopt_sleep/` (cloned from microsoft/SkillOpt).
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## Usage
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```bash
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# Run one cycle with current config
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cd ~/.openclaw/workspace/skills/skillopt-sleep
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python3 run_sleep.py
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# Dry run (report only, no staging)
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python3 run_sleep.py --dry-run
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# Use a pre-built task set (recommended for testing)
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python3 run_sleep.py --tasks tests/research-cron-tasks.json
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```
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## Config (config.json)
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Key knobs:
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- `backend: "openclaw-deepseek"` — our custom backend
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- `model: "deepseek-v4-pro"` — optimizer model
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- `edit_budget: 3` — max bounded edits per night
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- `gate_mode: "on"` — validation-gated (rejects regressions)
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- `auto_adopt: false` — require Ethan to adopt manually
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- `max_tasks_per_night: 12` — cap to control cost
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## Cost Estimate
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Per night: 12 tasks × (1 attempt + 1 judge + 1 reflect) × ~$0.005/1K tokens × ~3K tokens/call ≈ **$0.50-2.00/night**.
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## Outputs
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- Report: `~/.skillopt-sleep/state.json` (running totals)
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- Staging: `~/.skillopt-sleep/staging/<night>/`
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- `report.md` — readable summary
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- `best_skill.md` — proposed skill
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- `edits.json` — bounded edit list
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- `before.md` / `after.md` — diffs
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## Held-Out Test Sets (Phase 2)
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Located at `tests/<category>-tasks.json`. Each task has:
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- `prompt` — the recurring task
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- `reference` — exact-match gold answer
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- `rubric` — soft score rubric (0-1)
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- `domain` — research/devops/wiki/etc.
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Currently building for 3 categories:
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- research-cron-output
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- devops-infrastructure-check
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- wiki-canonical-guide
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## When NOT To Use
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- For a one-off workflow (not a recurring pattern)
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- During a crisis/incident (humans must lead)
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- When session transcripts are < 24h old (not enough signal)
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- For skills < 300 tokens (over-optimization risk)
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