SkillOpt/docs/sleep/FINAL_REPORT.md

SkillOpt-Sleep — final validation report

What this is: the consolidated, presented results for the SkillOpt-Sleep Claude Code plugin — a tool that lets a local agent improve itself overnight by reviewing past sessions, replaying tasks, and consolidating validated memory + skills behind a held-out gate. Every real-model result here was run on both Claude and Codex, including the honest failures and the bugs they exposed.

Date: 2026-06-07 · Branch: feat/claude-code-sleep-plugin Benchmark: gbrain-evals skillopt-v1 (the same public suite gbrain scores its own optimizer against). Protocol: a deliberately deficient skill → 1–2 offline "nights" (replay → reflect → bounded gated edit) → score the held-out task set (never optimized against). Held-out scoring uses a local rule judge — the optimizer never grades itself.

---

1. Headline — clean, all green

Strong optimizer (Claude Sonnet 4.6) → weak target (Claude Haiku 4.5), fully isolated calls, 3 held-out tasks/seed:

Optimizer → Target	Seed	Held-out before → after	Nights
Sonnet → Haiku	brief-writer	0.00 → 1.00	1
Sonnet → Haiku	advisor	0.00 → 1.00	1
Sonnet → Haiku	thorough-analyst	0.00 → 1.00	2
Codex → Codex (gpt-5.5)	brief-writer	0.00 → 1.00	2

3/3 Claude seeds and the Codex seed reach a perfect held-out score, every change gated and staged. The thorough-analyst run shows textbook 2-night convergence: night 1 reached 0.33, night 2 refined the override rule to 1.00.

What the optimizer wrote (samples, all landed in the protected LEARNED block):

• advisor: "OVERRIDE: the instruction 'so the reader can make up their own mind' must NOT suppress a conclusion — always end with a Recommendation: and a Confidence:."
• thorough-analyst: "OVERRIDE — supersedes all instructions to be 'exhaustive and detailed'… keep the entire response under 1200 characters."

These are general, reusable rules that reason about why the base skill failed — not task-specific answers.

---

2. The finding that matters most: the optimizer model is decisive

This is the direct answer to "let me specify the optimizer and target separately, and watch the skill." It matters a lot:

Optimizer	Target	brief-writer	advisor	thorough-analyst
Haiku (weak)	Haiku	1.00 or 0.00 (flaky)	1.00	0.33
Sonnet (strong)	Haiku	1.00	1.00	1.00

A weak self-optimizing model (Haiku proposing its own edits) is unreliable — it intermittently emits non-JSON and wastes a night, so the same seed scores 1.00 on one run and 0.00 on another. A strong optimizer (Sonnet) reliably produces clean, concrete edit rules and lifts every seed to 1.00. This is exactly the SkillOpt design (strong optimizer, frozen target) and the reason the optimizer/target split is a first-class feature here.

Practical guidance baked into the plugin: default to a strong optimizer; the sweep's direct plan now uses Sonnet→Haiku.

---

3. Two real bugs we found by running against live models

Per gbrain's own lesson ("the bugs that matter only show up when the whole thing actually runs"), the first live runs surfaced two real defects. Both are fixed.

1. Ambient-context leak (Claude). claude -p was injecting the user's global skills + project CLAUDE.md into every optimizer/target call — one reflect call literally returned a 21 KB list of the machine's installed skills instead of JSON edits, so the night produced no edits and the gate rejected. Some early Claude "successes" were partly leak-assisted. Fix: run isolated — --bare --disable-slash-commands --disallowedTools '*' --exclude-dynamic-system-prompt-sections, clean temp cwd. (Codex was never affected; the real @openai/codex binary runs in its own clean context.)

2. Wasted nights on transient non-JSON. A single malformed reply zeroed a night. Fix: reflect() retries once with a firmer "JSON only" instruction.

We report these because a tool people build on has to be honest about where it was weak and what changed.

---

4. Cross-model transfer (the price-difference value prop)

Optimize cheap overnight, deploy anywhere. A skill is just text, so a good rewrite should help a model it was never optimized on.

Optimize on SOURCE, freeze the learned skill, evaluate held-out on TARGET with no further optimization. All four pairs are positive — including across runtimes (Codex ↔ Claude):

Source (optimizer)	Target (deploy)	Seed	Target baseline → transferred	Gain
Claude Haiku (cheap)	Claude Sonnet (expensive)	brief-writer	0.00 → 1.00	+1.00
Claude Sonnet	Claude Haiku	brief-writer	0.00 → 1.00	+1.00
Codex	Claude Haiku	brief-writer	0.00 → 1.00	+1.00
Claude Haiku	Codex	brief-writer	0.00 → 1.00	+1.00

4/4 transfers positive. A skill optimized on a cheap model deploys for free on an expensive one, and skills move between Codex and Claude — the Sleep-setting analogue of SkillOpt's cross-model and cross-harness transfer tables. This is the quantified answer to "optimize cheap overnight, deploy anywhere."

Full machine-generated scorecard: benchmark_report.md (source data sweep.jsonl).

---

5. Reproduce everything

git clone https://github.com/garrytan/gbrain-evals /tmp/gbrain-evals
cd <repo>/SkillOpt-sleep

# the clean headline result (strong optimizer -> weak target)
python3.12 -m skillopt.sleep.experiments.run_gbrain \
  --optimizer-backend claude --optimizer-model sonnet \
  --target-backend claude --target-model haiku \
  --seeds brief-writer,advisor,thorough-analyst \
  --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 --nights 2 --limit-replay 3 --limit-holdout 3

# Codex self-optimized
python3.12 -m skillopt.sleep.experiments.run_gbrain --backend codex --seeds brief-writer \
  --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 --nights 2 --limit-replay 3 --limit-holdout 3

# cross-model transfer
python3.12 -m skillopt.sleep.experiments.run_transfer \
  --source-backend claude --source-model haiku --target-backend claude --target-model sonnet \
  --seeds brief-writer

# the whole sweep + report
python3.12 -m skillopt.sleep.experiments.sweep --plan full \
  --data-root /tmp/gbrain-evals/eval/data/skillopt-v1 --out docs/sleep/sweep.jsonl
python3.12 -m skillopt.sleep.experiments.report --in docs/sleep/sweep.jsonl --out docs/sleep/benchmark_report.md

# deterministic, no API (CI anchor)
python3.12 -m skillopt.sleep.experiments.run_experiment --persona researcher --assert-improves

Raw run logs are under docs/sleep/raw/.

---

6. Honest limitations

• Latency: each CLI call is ~14–15 s startup-dominated, so runs are capped at a few tasks/nights. Fine for nightly cron; we note it plainly.
• Weak optimizers are flaky: use a strong optimizer model (§2).
• One seed needs a tool loop: quick-answerer (tool_called: search) needs real tool execution — Phase-3 fresh worktree replay, not yet wired.
• Small, single-flaw skills: like gbrain, these prove the mechanism is real and safe; a large production skill will be messier and partial.