/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

Where your skills live

Plugin-bundled skills ship as a read-only snapshot; you cannot write a benchmark or optimization artifacts next to them. Run skillopt against your own writable skills directory: pass `--skills-dir <path>` (`gbrain skillopt X --skills-dir ~/my-project/skills`), or run from a workspace that contains a `skills/` tree and the CLI auto-detects it. Every `skills/X/...` path below refers to that writable directory (your own authored skills, skillify-scaffolded skills, or a brain-resident skillpack), not the plugin snapshot.

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 full walkthrough lives at https://git