de3be75bac
Adds docs/sleep/README.md — a concise intro to the SkillOpt-Sleep plugin (what it is, how to use it across the three agents, the opt-in experience-replay / dream-rollout knobs, and headline results), linking to the full guide section. Adds a News bullet pointing to it. No code changes.
SkillOpt-Sleep 😴 — deployment-time companion (preview)
SkillOpt-Sleep applies SkillOpt's discipline to your own daily usage. It gives a local coding agent a nightly sleep cycle that reviews your past sessions, replays your recurring tasks on your own API budget, and consolidates what it learns into validated long-term memory and skills — behind a held-out gate, staged for your review. The agent gets better the more you use it, with no weight training and zero inference-time overhead.
Preview. This is an early preview we are actively iterating on; interfaces and defaults may change. The engine lives in the top-level
skillopt_sleep/package with zero dependency on the paper'sskillopt/code (the validation gate is vendored).
How it works
One "night":
harvest Claude Code / Codex transcripts → mine recurring tasks → replay offline
→ consolidate (reflect → bounded edit → GATE on real held-out tasks)
→ stage proposal → (you) adopt
It synthesizes SkillOpt (validation-gated bounded text edits), Claude Dreams (offline consolidation; review-then-adopt), and the agent-sleep idea (short-term experience → long-term competence).
How to use it
One engine, thin per-agent shells (see plugins/):
| Platform | Folder | Install |
|---|---|---|
| Claude Code | plugins/claude-code |
/plugin marketplace add ./plugins/claude-code → /skillopt-sleep |
| Codex | plugins/codex |
bash plugins/codex/install.sh → skillopt-sleep skill |
| Copilot | plugins/copilot |
register plugins/copilot/mcp_server.py as an MCP server |
Deterministic proof (no API key):
python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves.
Opt-in: experience replay & dream rollouts
Two consolidation mechanisms, both default off (behavior is unchanged unless you enable them). They strengthen the nightly update when your tasks have a clean correctness signal; the validation gate still governs what ships.
| Config knob | Default | Effect |
|---|---|---|
dream_rollouts |
1 |
Run each task K times → learn from the good-vs-bad contrast (contrastive reflection). |
recall_k |
0 |
Associative recall — pull the K most-similar past tasks (from a persisted archive) into tonight's dream. |
dream_factor |
0 |
Add N lightweight synthetic variants of each task. |
Results
-
End-to-end on real agents. On the public gbrain-evals
skillopt-v1benchmark, deficient seed skills go 0.00 → 1.00 on held-out sets with both Claude and Codex (all 4 seeds, including a real tool-use loop). -
Experience replay scales the gain on a clean signal (deployment protocol: 5 nights × 10 new real tasks/night, full held-out test, GPT-5.5, gated):
Config Δ vs baseline recall_k=10, dream_rollouts=5+3.1 pts recall_k=20, dream_rollouts=5+4.5 pts full-history replay (reference) +5.6 pts A second benchmark (SpreadsheetBench, GPT-5.4-nano, gate-free) gives +3.6 pts.
-
Honest scope. Gains are real where tasks recur and have a checkable correctness signal; on saturated or noisy tasks the effect is flat within run-to-run noise (±1–2 pts, single seed). The validation gate keeps the downside bounded — keep it on.
Learn more
Full reference (pipeline, the three plugins, the experience-replay knobs) is in the Documentation & Reproduction Guide.