Skill Eval Improve

Skill eval & improve

Improve skills measurably: baseline → measure → bounded edit → re-validate. Combine local tooling, Codex plugin-eval (when installed), and research-backed loops (SkillOpt).

When to use

• Skill triggers wrong or never loads (description routing)
• Bloated SKILL.md, high token cost, weak outcomes
• After adding a new procedure—need regression checks
• Porting patterns from product MCP / plugin-eval research into Skill Steward skills

When not to use

• Bulk repo validation — e.g. “validate every skill in this repo” → pnpm run validate only (skill-authoring-lifecycle for audit); do not start benchmark or SkillOpt loops.
• Automated SkillOpt / cluster training — Skill Steward documents a manual bounded-edit loop; no overnight optimizer pipeline.
• Creating a new skill — use skill-authoring-lifecycle first; eval-improve applies after a skill exists.

Cursor scope (optional): activate when editing under skills/** or scripts/validate-skills.mjs.

Mixture of experts (evaluation stack)

Use the cheapest layer that answers the question. Do not skip layer 0.

Layer 0 — Skill Steward validator (always)

pnpm run validate
pnpm run validate:json   # CI / automation

Fix all error: lines. Treat warn: (missing sources.md, long SKILL.md) seriously.

T-named skill quality gates (ADR 0011, ADR 0027)

T1 behavior-critical currently includes harness-engineering-lifecycle, mcp-harness-repo-maintainer, mixture-of-experts, multi-agent-handoff, plugin-marketplace-setup, repo-quality-system-lifecycle, repository-governance-lifecycle, skill-authoring-lifecycle, skill-eval-improve, steward-continuity-boundary-lifecycle, and vision-alignment-foresight. Each requires evals/cases/*.yaml (≥2) + references/evals.md. Schema: eval-case-schema.md.

Layer 0b — Rule-based cases (T1 behavior-critical CI)

pnpm run eval
pnpm run eval -- --skill mcp-harness-repo-maintainer
pnpm run eval:json

Chrome eval design (failure modes, rubrics, objective vs judge): references/chrome-eval-design.md.

CI does not run LLM judges. Subjective quality stays in references/evals.md (layer 2+).

Layer 1 — Codex plugin-eval (local)

When Codex plugin-eval is installed (~/.codex/plugins/.../plugin-eval):

# Chat-first router
plugin-eval start skills/<name> --request "Evaluate this skill." --format markdown

# Static report
plugin-eval analyze skills/<name> --format markdown

# Token budget explanation
plugin-eval explain-budget skills/<name> --format markdown

# Starter benchmark config
plugin-eval init-benchmark skills/<name>
plugin-eval benchmark skills/<name> --dry-run

Hand off rewrite plans to plugin-eval’s improve-skill skill after analyze --brief-out.

Details: references/plugin-eval.md.

Layer 2 — Human prompt suite (required for material edits)

1. Write 3–5 representative user prompts (should trigger + should not trigger).
2. Run agent without skill → record failures.
3. Run with skill → record improvements and new failures.
4. Mirror prompts in evals/cases/*.yaml (CI rules) and references/evals.md (behavior log).

Split ~60% train (edit against) / 40% held-out (gate acceptance)—mirrors SkillOpt selection gate.

Layer 3 — SkillOpt-inspired improve loop (research)

SkillOpt treats SKILL.md as trainable text with a frozen agent:

Rollout (tasks + current skill) → Reflect (failures vs successes)
  → Bounded edit (add/delete/replace under budget) → Held-out gate (keep only if better)

Skill Steward manual adaptation (no GPU cluster required):

Paper: https://arxiv.org/abs/2605.23904 · Site: https://microsoft.github.io/SkillOpt/

Related: SkillLens (model-generated skills study).

Layer 4 — Ecosystem benchmarks (2026+)

Layer 5 — Runtime Dogfood Benchmarks

At 10,000x scale, NLP prompt evaluation fails because LLMs suffer Cognitive Overload navigating massive toolsets. Runtime dogfood should objectively assert their logical trajectory using deterministic traces.

1. Capture compact traces: Store action IDs, tool counts, artifact digests, and redacted excerpts, not raw product traces.
2. Define assertions: Expected action trajectory, declared surfaces used first, maximum tool calls, maximum repair/setup attempts, maximum unrelated tool calls, required return_to_goal_step, required artifacts, and negative checks for unrelated actions.
3. Run dogfood benchmarks: Use steward benchmark --scenario <id> --json for runtime dogfood scenarios. Do not put product runtime scenarios under T1 behavior-critical skill evals.

The current steward eval --name registered-eval path is legacy/experimental. Skill quality remains pnpm run eval; runtime dogfood belongs to steward benchmark, where durability_blocked is valid blocked evidence when contract inputs are modified or untracked, not proof of runtime behavior.

Improve workflow (checklist)

- [ ] sources.md cites plugin-eval + SkillOpt if used
- [ ] pnpm run validate
- [ ] T1 behavior-critical: `pnpm run eval` + cases updated
- [ ] plugin-eval analyze (optional)
- [ ] 3+ prompt evals documented in references/evals.md
- [ ] Bounded edit applied; held-out improved
- [ ] skill-authoring-lifecycle checklist
- [ ] PR mentions eval delta

What to fix first (typical order)

1. name / description (routing)—must include what + when
2. Broken links / missing references/sources.md
3. Delete or replace duplicated rules before adding a new section or eval case
4. Move bulk to references/ (SKILL.md < 500 lines)
5. Add error-handling / validation steps agents skip
6. Token cost (description length, always-loaded content)

Anti-patterns

• Rewriting entire SKILL.md from one failure (destroy working rules)
• Self-editing without held-out prompts (overfit)
• Adding skill rules, evals, or tools from one observed run when a smaller FAQ, error message, native command, observed-effect check, or deletion would solve the problem
• Adding a new eval for duplicated guidance before trying to compress, delete, or replace the overlapping rule
• Claims without references/sources.md rows
• Evaluating only with static analyze—never running real prompts
• LLM judge in CI (flake, cost) — offline only per ADR 0011
• Passing pnpm run eval and claiming agent behavior is proven

Related skills

Sources

See references/sources.md.

Install

npx skills add arenukvern/skill_steward --skill skill-eval-improve