gbrain/docs/guides/skillopt.md

gbrain skillopt — Self-evolving skills

Treat your SKILL.md files as the trainable parameters of an agent that itself never changes. Write a benchmark of realistic tasks; SkillOpt watches the agent run them, proposes specific edits, re-tests, and only keeps changes that measurably improve the score.

Based on SkillOpt (Microsoft Research, May 2026).

The 30-second pitch

# 1. Bootstrap a benchmark from existing routing fixtures
gbrain skillopt my-skill --bootstrap-from-routing

# 2. Review the generated benchmark
#    Delete the trailing `# BOOTSTRAP_PENDING_REVIEW` line

# 3. Run the optimizer
gbrain skillopt my-skill --bootstrap-reviewed

That's the entire workflow.

What's in the box

skills/my-skill/
  SKILL.md                          ← what gets optimized (body only; D5)
  skillopt-benchmark.jsonl          ← what success looks like
  skillopt/
    best.md                         ← current best version
    versions/
      v0001_e1_s1.md                ← per-step snapshots
      v0002_e1_s2.md
      ...
    history.json                    ← append-only run record (D8)
    rejected.json                   ← bounded LRU of rejected edits

The audit trail lives at ~/.gbrain/audit/skillopt-YYYY-Www.jsonl (ISO-week rotated; honors GBRAIN_AUDIT_DIR).

How the loop works

For each step:

1. Forward pass. Run the candidate skill against a batch from D_train.
2. Backward pass. Two reflect calls (failures + successes per D7) propose edits to address what worked / didn't work.
3. Rank + clip. Top-N edits within the LR budget (cosine schedule by default; D10 has the ASCII curve in orchestrator.ts).
4. Apply. D9 tagged-result patches the body (frontmatter forbidden per D5; ambiguous anchors rejected to the rejected-buffer).
5. Validation gate. D12 median-of-3 + epsilon=0.05: every sel-task runs the judge 3 times, takes the median; only accepts if median > best by more than 0.05.
6. Commit. D8 history-intent-first 5-step atomic write — crash-safe.

After each epoch with no improvement: D6 slow-update fires one meta-edit proposal (this lives in v0.42 follow-up; v1 emits the audit event).

Flags

Flag	Default	Purpose
--benchmark <path>	skills/<n>/skillopt-benchmark.jsonl	Path to benchmark JSONL
--bootstrap-from-routing	off	Auto-build benchmark from routing-eval.jsonl
--bootstrap-reviewed	off	Required after human-reviewing bootstrap output
--epochs N	4	Outer-loop iterations
--batch-size N	8	Tasks per inner step
--lr N	4	Max edits per step
--lr-schedule cosine|linear|constant	cosine	Edit-budget decay
--split TRAIN:SEL:TEST	4:1:5	Ratio; refuses if D_sel < 5
--optimizer-model MODEL	tier.deep	Reflects + proposes
--target-model MODEL	tier.subagent	Executes the skill
--judge-model MODEL	tier.reasoning	Scores rollouts
--patch | --rewrite	patch	Edit ops only vs. full rewrites
--dry-run	off	Cost preview, no LLM calls
--no-mutate	off	Write proposed.md, don't replace SKILL.md
--allow-mutate-bundled	off	Required to mutate gbrain-bundled skills
--max-cost-usd N	5.00	Hard cap; preflight refuses if exceeded
--max-runtime-min N	30	Wall-clock cap
--force	off	Bypass dirty-working-tree refusal
--resume <run-id>	off	Resume a prior interrupted run
--json	off	Machine-readable stdout

Exit codes

Code	Meaning
0	Improved + accepted (or --no-mutate proposed.md written)
1	No improvement; best skill unchanged
2	Aborted by gate (dirty tree, over budget, bench validation, etc.)

Cost model

A typical 20-task benchmark with defaults costs ~$0.90 per run:

• 32 rollouts × Sonnet ($0.009 each) ≈ $0.29
• 8 reflect calls × Opus (cached) ≈ $0.25
• 24 sel-judges × Sonnet (cached) ≈ $0.10
• Final test eval ≈ $0.07
• Total ≈ $0.71

For a 100-task benchmark: ~$5.00 (right at the default cap). Preflight refuses to start when the estimate exceeds --max-cost-usd.

Safety guards (the cathedral)

Guard	Decision	What it prevents
Validation gate is mandatory	D12 (paper)	Accepting LLM judge noise as improvement
Frontmatter mutation forbidden	D5	Routing surface drift (check-resolvable regression)
Per-skill DB lock	D14	Two concurrent runs corrupting history/versions
Bundled-skill gate	D16	Auto-mutating skills shipped with gbrain
Bootstrap review sentinel	D15	Self-referential benchmark gaming
Read-only tool sandbox in rollouts	D13	Optimization runs writing junk pages to your brain
History-intent-first atomic commit	D8	Half-written SKILL.md on crash
Cost preflight	D3	Surprise mid-run budget exhaustion
Dirty-tree refusal	dry-fix pattern	Overwriting your uncommitted changes

When NOT to use SkillOpt

• No benchmark. Optimizing against guesses is worse than not optimizing.
• Write-flavored skills. Skills whose job is to put_page heavily can't use the v1 read-only sandbox; mocked-write capture is a v0.42 follow-up.
• Tiny benchmarks (<10 tasks). D_sel < 5 refuses by default; meaningful validation needs ≥20 tasks total per the paper.

Related skills

• gbrain skillify scaffold <name> — create a new skill (use BEFORE skillopt)
• gbrain skillpack-check <name> — audit conformance + skillopt status
• gbrain check-resolvable — routing MECE validation (NOT mutated by skillopt)