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elzlxx 553446575a feat(plugins): add OpenClaw shell for SkillOpt-Sleep

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.

2026-06-14 23:27:54 +08:00

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name, description

name	description
skillopt-sleep	Validate and refine agent skills through nightly sleep cycles with held-out gates. Wraps Microsoft's SkillOpt-Sleep engine for the OpenClaw/DeepSeek stack.

skillopt-sleep — OpenClaw Adaptation of Microsoft SkillOpt-Sleep

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.

When To Use

After Hermes's Weekly Skill Review (or as its replacement)
When a skill is being used 10+ times/week and could be tighter
Before promoting a new skill from skill-proposals/ to skills/
When a skill regresses in observed quality

What It Does (One Cycle)

harvest session transcripts  ->  mine recurring task patterns
                              ->  replay each pattern (current skill vs proposed)
                              ->  GATE: must improve held-out score
                              ->  stage proposal
                              ->  Ethan adopts (manual)

Nothing live changes until Ethan adopts. Every adopt backs up first.

Architecture

skills/skillopt-sleep/
├── SKILL.md                          # this file
├── config.json                       # engine config (backend, budgets, etc.)
├── run_sleep.py                      # entry point
└── skillopt_sleep_openclaw.py        # DeepSeek/Ollama backend

The engine itself is at ~/.openclaw/workspace/SkillOpt/skillopt_sleep/ (cloned from microsoft/SkillOpt).

Usage

# Run one cycle with current config
cd ~/.openclaw/workspace/skills/skillopt-sleep
python3 run_sleep.py

# Dry run (report only, no staging)
python3 run_sleep.py --dry-run

# Use a pre-built task set (recommended for testing)
python3 run_sleep.py --tasks tests/research-cron-tasks.json

Config (config.json)

Key knobs:

backend: "openclaw-deepseek" — our custom backend
model: "deepseek-v4-pro" — optimizer model
edit_budget: 3 — max bounded edits per night
gate_mode: "on" — validation-gated (rejects regressions)
auto_adopt: false — require Ethan to adopt manually
max_tasks_per_night: 12 — cap to control cost

Cost Estimate

Per night: 12 tasks × (1 attempt + 1 judge + 1 reflect) × ~$0.005/1K tokens × ~3K tokens/call ≈ $0.50-2.00/night.

Outputs

Report: ~/.skillopt-sleep/state.json (running totals)
Staging: ~/.skillopt-sleep/staging/<night>/
- report.md — readable summary
- best_skill.md — proposed skill
- edits.json — bounded edit list
- before.md / after.md — diffs

Held-Out Test Sets (Phase 2)

Located at tests/<category>-tasks.json. Each task has:

prompt — the recurring task
reference — exact-match gold answer
rubric — soft score rubric (0-1)
domain — research/devops/wiki/etc.

Currently building for 3 categories:

research-cron-output
devops-infrastructure-check
wiki-canonical-guide

When NOT To Use

For a one-off workflow (not a recurring pattern)
During a crisis/incident (humans must lead)
When session transcripts are < 24h old (not enough signal)
For skills < 300 tokens (over-optimization risk)

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