docs: move SkillOpt-Sleep into the guide; clean docs/sleep; fix guide link
Per maintainer request: - Remove the internal/scratch docs/sleep/ tree (reports, raw logs, blog run JSON, sweep.jsonl) — 23 files — and the root PUBLISHING.md. These were working notes, not reference docs. - Take the dedicated SkillOpt-Sleep content out of the main README (News bullet + section) and host it in the rendered guide instead: new section 9 in docs/guideline.html (deployment companion, the three plugins, opt-in experience replay / dream rollouts) with a sidebar entry. - Fix the README's opening reference so "Documentation & Reproduction Guide" links directly to the rendered GitHub Pages page, not the raw .html source. - Repoint the now-removed docs/sleep links in the plugin READMEs to the guide section. The plugin code (plugins/, skillopt_sleep/) is unchanged; only docs move. Co-Authored-By: Claude Opus 4 <noreply@anthropic.com>
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Yifan Yang
@@ -288,6 +288,12 @@
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<a href="#cli">CLI scripts</a>
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<a href="#webui">WebUI</a>
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</div>
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<div class="group">
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<div class="glabel"><span class="num">9</span> SkillOpt-Sleep</div>
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<a href="#sleep">Deployment companion</a>
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<a href="#sleep-plugins">Plugins (3 agents)</a>
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<a href="#sleep-replay">Experience replay (opt-in)</a>
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</div>
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</nav>
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<!-- ───────────── MAIN CONTENT ───────────── -->
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@@ -917,6 +923,57 @@ PY</code></pre>
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<tr><td><code>--share</code></td><td class="def">off</td><td>Create a public Gradio share link.</td></tr>
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</tbody>
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</table></div>
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</section>
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<section id="sleep">
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<h2>9.1 SkillOpt-Sleep — the deployment-time companion (preview) <a class="anchor" href="#sleep">#</a></h2>
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<p><strong>SkillOpt-Sleep</strong> applies SkillOpt's discipline to your own daily usage. It gives a
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local coding agent a nightly <em>sleep cycle</em> that reviews your past sessions, replays your
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recurring tasks on your own API budget, and consolidates what it learns into <strong>validated</strong>
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long-term memory and skills — behind a held-out gate, staged for your review. The agent gets better
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the more you use it, with no weight training and zero inference-time overhead. It is an early
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<strong>preview</strong> we are actively iterating on; interfaces and defaults may change.</p>
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<p>One "night":</p>
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<pre><code>harvest Claude Code / Codex transcripts → mine recurring tasks → replay offline
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→ consolidate (reflect → bounded edit → GATE on real held-out tasks)
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→ stage proposal → (you) adopt</code></pre>
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<p>The engine lives in the top-level <code>skillopt_sleep/</code> package with <strong>zero dependency</strong>
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on the paper's <code>skillopt/</code> experiment code (the validation gate is vendored). Deterministic
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proof, no API key required:
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<code>python -m skillopt_sleep.experiments.run_experiment --persona researcher --assert-improves</code>.</p>
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<h2 id="sleep-plugins">9.2 Plugins (three agents) <a class="anchor" href="#sleep-plugins">#</a></h2>
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<p>One engine, thin per-agent shells (see <a href="https://github.com/microsoft/SkillOpt/tree/main/plugins"><code>plugins/</code></a>):</p>
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<div class="table-wrap"><table>
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<thead><tr><th>Platform</th><th>Folder</th><th>Install</th></tr></thead>
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<tbody>
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<tr><td>Claude Code</td><td><code>plugins/claude-code</code></td><td><code>/plugin marketplace add ./plugins/claude-code</code> → <code>/skillopt-sleep</code></td></tr>
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<tr><td>Codex</td><td><code>plugins/codex</code></td><td><code>bash plugins/codex/install.sh</code> → <code>skillopt-sleep</code> skill</td></tr>
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<tr><td>Copilot</td><td><code>plugins/copilot</code></td><td>register <code>plugins/copilot/mcp_server.py</code> as an MCP server</td></tr>
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</tbody>
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</table></div>
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<p>Transcript source and replay backend are separate knobs: <code>--source claude</code> for Claude Code
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transcripts, <code>--source codex</code> for Codex Desktop archived sessions under
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<code>~/.codex/archived_sessions</code>, and <code>--backend codex</code> only when you want the
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replay/optimizer to spend Codex budget.</p>
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<h2 id="sleep-replay">9.3 Experience replay & dream rollouts (opt-in) <a class="anchor" href="#sleep-replay">#</a></h2>
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<p>Two consolidation mechanisms, both default <strong>off</strong> (so behavior is unchanged unless
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enabled). They strengthen the nightly update when your tasks have a clean correctness signal; the
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validation gate still governs what ships.</p>
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<div class="table-wrap"><table>
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<thead><tr><th>Config knob</th><th>Default</th><th>Effect</th></tr></thead>
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<tbody>
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<tr><td><code>dream_rollouts</code></td><td class="def">1</td><td>Run each task K times and learn from the good-vs-bad contrast (contrastive reflection).</td></tr>
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<tr><td><code>recall_k</code></td><td class="def">0</td><td>Associative recall — pull the K most-similar past tasks (from a persisted archive) into tonight's dream.</td></tr>
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<tr><td><code>dream_factor</code></td><td class="def">0</td><td>Add N lightweight synthetic variants of each task.</td></tr>
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</tbody>
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</table></div>
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<p>On a clean-signal benchmark the gain scales with recall depth (deployment protocol: 5 nights ×
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10 new real tasks/night, full held-out test, GPT-5.5, gated): <code>recall_k=10</code> → +3.1 pts,
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<code>recall_k=20</code> → +4.5 pts, full-history replay reference → +5.6 pts; a second benchmark
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(SpreadsheetBench, GPT-5.4-nano, gate-free) gives +3.6 pts. On saturated or noisy tasks the effect is
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flat within run-to-run noise (±1–2 pts). Keep the gate on; it bounds the downside.</p>
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<div class="footer-note">
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SkillOpt — Executive Strategy for Self-Evolving Agent Skills ·
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