skill-optimizer

name: skill-optimizer version: 0.1.0 description: Self-evolving skill optimization via SkillOpt-paper-grounded text-space optimizer. triggers: - "optimize this skill" - "tune the skill against the benchmark" - "make the skill better" - "run skillopt" - "skillopt for" mutating: true brain_first: exempt

Skill Optimizer

Self-evolving skill optimization. Treats SKILL.md as the trainable parameters of a frozen agent. Validation-gated, budget-capped, atomic-versioned.

Based on SkillOpt (arXiv 2605.23904, Microsoft Research, May 2026).

When to invoke this skill

The user wants to:

• Improve an existing skill's execution quality against a benchmark
• Bootstrap a benchmark file for a new skill
• Re-tune a skill after switching target models

Iron Law

• Validation gating is MANDATORY. Every candidate must clear median-of-3
  • epsilon=0.05 margin against the sel-set before SKILL.md gets rewritten.
• Frontmatter mutation is FORBIDDEN. The optimizer only edits the body. Routing surface (triggers:, brain_first:) stays invariant.
• Bundled skills require explicit opt-in AND an independent held-out set. Skills shipping with gbrain cannot be auto-mutated. To rewrite one in place the user passes BOTH --allow-mutate-bundled AND --held-out <path> with at least 5 benchmark-disjoint tasks; without the held-out set the run hard-refuses (exit 2). Drop --allow-mutate-bundled (or pass --no-mutate, the default for the dream-cycle phase) to write proposed.md for review instead — no held-out needed for review-only output.
• Bootstrap output requires human review. Both --bootstrap-from-skill and --bootstrap-from-routing write a sentinel; you must review + STRENGTHEN the generated judges, delete the sentinel, and re-run with --bootstrap-reviewed before optimization can use the file.

The pipeline

gbrain skillopt <skill-name> [flags]
  │
  ├── Pre-flight gates
  │     ├── working tree clean (or --force)
  │     ├── benchmark valid + D_sel >= 5 (D17)
  │     ├── cost preflight (D3) — refuses over --max-cost-usd
  │     └── per-skill DB lock (D14)
  │
  ├── Baseline eval on D_sel (sets best_sel_score)
  │
  ├── for epoch in 1..N:
  │     for step in 1..steps_per_epoch:
  │       ├── forward pass: rollouts on D_train batch
  │       ├── backward pass: reflect × 2 (failures + successes per D7)
  │       ├── rank + clip via LR cosine schedule
  │       ├── apply edits (body-only per D5, tagged result per D9)
  │       ├── validation gate: median-of-3 + epsilon=0.05 (D12)
  │       └── if accept: commit via D8 history-intent-first
  │     │
  │     └── slow update (D6) if no improvement this epoch
  │
  └── Final test eval on D_test → run receipt

Starting a benchmark from the skill itself (the common case)

The user will NOT hand-write a benchmark, and you shouldn't start from a blank file either. When the user says "make skill X better" and skills/X/skillopt-benchmark.jsonl doesn't exist, generate a starter from the SKILL.md directly:

1. Generate the starter. Run:

   gbrain skillopt X --bootstrap-from-skill
   

   One LLM call reads skills/X/SKILL.md, infers what the skill produces and what "good" looks like, and writes ~15 tasks (each with rule judges) to skills/X/skillopt-benchmark.jsonl plus a # BOOTSTRAP_PENDING_REVIEW sentinel. No routing-eval.jsonl is needed. Tune the count with --bootstrap-tasks N (max 50).
2. Review AND STRENGTHEN the judges. This is YOUR job and it is load-bearing. The generated rule checks are weak drafts — the model tends to emit generic contains, loose max_chars, or invented headings. Read each task, fix soft checks, add the must-haves the skill actually requires (real section names, real length ceilings, min_citations where sources are expected, tool_called/tool_not_called for tools the skill genuinely uses). A thin benchmark optimizes for a thin definition of quality — do not rubber-stamp.
3. Delete the sentinel line (# BOOTSTRAP_PENDING_REVIEW, the last line).
4. Run the optimizer with --split 1:1:1:

   gbrain skillopt X --bootstrap-reviewed --split 1:1:1
   

   The 1:1:1 split is REQUIRED for a 15-task starter — the default 4:1:5 makes the validation set floor(15/10)=1, below the D_sel >= 5 floor, and the optimizer refuses with d_sel_too_small. (4:1:5 needs ~50 tasks.) Add --dry-run first to preview cost.

Benchmark line shape (what the generator writes, one per line):

{"task_id":"x-001","task":"<user prompt>","judge":{"kind":"rule","checks":[{"op":"max_chars","arg":1800},{"op":"contains","arg":"agenda"}]}}

Rule-check vocabulary you'll strengthen with: contains, regex, section_present, max_chars, min_citations, tool_called, tool_not_called. Rule judges are deterministic and free, but shallow for skills whose quality is sequencing, privacy, refusal boundaries, or file placement — for those, hand-add richer checks (or an llm judge) during review.

Fallback — author freehand. If the generated starter is poor (rare, but possible for very behavior-shaped skills), discard it and write the JSONL yourself: read the SKILL.md, write ~15 realistic tasks covering the boring middle, attach >=2 rule checks each, save to skills/X/skillopt-benchmark.jsonl, run with --split 1:1:1. The human walkthrough lives at docs/tutorials/improving-skills-with-skillopt.md.

Decision tree

Output Format

When invoked, this skill produces:

• Updated skills/<name>/SKILL.md (when mutation is allowed)
• skills/<name>/skillopt/best.md — pointer copy of current best
• skills/<name>/skillopt/versions/vNNNN_eN_sN.md — per-step snapshots
• skills/<name>/skillopt/history.json — append-only run record
• skills/<name>/skillopt/rejected.json — bounded LRU of rejected edits
• ~/.gbrain/audit/skillopt-YYYY-Www.jsonl — ISO-week-rotated audit trail

Anti-Patterns

• Don't bypass the validation gate. The median-of-3 + epsilon=0.05 is load-bearing; without it, the optimizer accepts noise as improvement.
• Don't optimize bundled skills without --allow-mutate-bundled AND --held-out. They ship with gbrain and are load-bearing for downstream agents. In-place mutation requires both flags (held-out >=5 benchmark-disjoint tasks); without the held-out set the run hard-refuses and points you at proposed.md.
• Don't use bootstrap output without strengthening it. Both --bootstrap-from-skill and --bootstrap-from-routing have the optimizer model invent success criteria — generic and weak by default. Review and tighten the judges before SkillOpt optimizes against them, or it trains the skill toward benchmark artifacts instead of real quality.
• Don't skip --split 1:1:1 on a ~15-task starter. The default 4:1:5 split drops the validation set below the D_sel >= 5 floor and the run aborts with d_sel_too_small.

Contract

runSkillOpt(opts) returns:

{
  outcome: 'accepted' | 'no_improvement' | 'aborted' | 'errored',
  receipt: {
    run_id, skill_sha8, benchmark_sha8, models, cost,
    baseline_sel_score, best_sel_score,   // real measured baseline (no longer hardcoded 0)
    baseline_test_score, test_score,      // final held-out test-split eval
  },
  finalText: string,
  mutatedSkillFile: boolean,
  proposedPath?: string
}

Related skills

• skillify — scaffolds a new skill (use BEFORE skillopt)
• skillpack-check — audits skill conformance (item 13 surfaces skillopt status)
• conventions/quality.md — output quality standards skillopt enforces via judges