Yifan Yang
86bad36ffe
Updates the SkillOpt-Sleep plugin on top of the current main. User-facing and engine improvements since the initial drop: * Command renamed /sleep -> /skillopt-sleep across Claude Code + Codex shells; refreshed plugin READMEs and install scripts. * Built-in scheduling (skillopt_sleep/scheduler.py + __main__): schedule / unschedule the nightly cycle without external cron wiring. * Backend robustness: bounded retry with backoff (no more silent empty-string on transient 429/timeout), content-filter-safe rollout prompt, an output-contract guardrail that rejects edits violating the task's required format, and a per-sample cache key so repeated dream rollouts are independent samples (fixes degenerate single-sample reflection). * consolidate / rollout / replay: parallel multi-rollout dreaming, gate-mode controls, TaskRecord.system framing field. Scope: this commit ships only the plugin engine + shells. Research/benchmark harnesses and their data are intentionally not included; the public package has no dependency on them (the one research-evaluator import is now guarded). Marked as an early preview in the README; we'll keep iterating. 99/99 unit tests pass. Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
50 lines
2.0 KiB
Markdown
50 lines
2.0 KiB
Markdown
---
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name: skillopt-sleep
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description: Nightly offline self-evolution for a Codex agent. Reviews past sessions, replays recurring tasks, and consolidates validated memory + skills behind a held-out gate. Use when the user wants Codex to learn from past usage, run a "sleep"/"dream" cycle, or schedule offline self-optimization.
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---
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# SkillOpt-Sleep (Codex skill)
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This skill drives the `skillopt_sleep` engine — an offline "sleep cycle" that
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makes a Codex agent better at the user's recurring work without retraining.
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## When to use
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Trigger when the user wants to: review past sessions, learn their preferences,
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consolidate feedback into long-term memory/skills, run a nightly/offline
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self-improvement cycle, or adopt a staged proposal.
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## How to run it
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Invoke the bundled runner via shell (Codex `exec` has shell access). The runner
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finds the engine and a Python ≥ 3.10 automatically:
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```bash
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# point at the repo if it isn't auto-detected from CWD:
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export SKILLOPT_SLEEP_REPO=/path/to/SkillOpt-Sleep
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bash "$SKILLOPT_SLEEP_REPO/plugins/run-sleep.sh" <action> --project "$(pwd)"
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```
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`<action>` ∈ `status | dry-run | run | adopt | harvest`. Use `--backend codex`
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for real improvement on the user's own Codex budget (default `mock` = no spend).
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## Steps
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1. Run the requested action; capture stdout.
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2. For `run`/`dry-run`: read the staged `report.md` it prints and show the user
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the held-out baseline → candidate score and the exact proposed edits.
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3. `run` only **stages** a proposal under `<project>/.skillopt-sleep/staging/`;
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nothing live changes until `adopt`. Offer `/skillopt-sleep adopt`.
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4. Never hand-edit the user's `AGENTS.md` / skills yourself — only `adopt` does,
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and it backs up first.
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## Validate
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```bash
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python -m skillopt_sleep.experiments.run_gbrain --backend codex \
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--seeds brief-writer --data-root /path/to/gbrain-evals/eval/data/skillopt-v1 \
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--nights 2 --limit-replay 3 --limit-holdout 3
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```
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A deficient skill goes 0.00 → 1.00 on a held-out set; the optimizer's edits are
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gated on real-task performance.
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