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skill-optimizer

Self-evolving skill optimization via SkillOpt-paper-grounded text-space optimizer.

Quality

57%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/skill-optimizer/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

70%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

A strong operational skill body: exact CLI invocations, a clearly sequenced and validation-gated pipeline, and honest failure handling. Its weaknesses are repetition of the same safety rules across five sections and reliance on dangling out-of-bundle references (D-numbered design IDs, conventions/ paths) that a reader cannot follow.

Suggestions

Resolve the dangling references: either bundle a short reference defining the D3-D17 design decisions or drop the IDs, and replace ../conventions/agent-operator-protocol.md and conventions/quality.md with paths that exist relative to the skill.

State the bundled-skill mutation rule once (e.g. in Iron Law) and cross-reference it from the decision tree, 'When it fails', and Anti-Patterns instead of restating it five times.

Move the judge-strengthening guidance (rule-check vocabulary, benchmark line shape, worked before/after examples) into a bundled references/ file and keep a one-line pointer in the body.

DimensionReasoningScore

Conciseness

The body is dense and imperative with no basic-concept explanations, but the bundled-mutation rule (--allow-mutate-bundled AND --held-out) is restated in five places (Iron Law, 'Where your skills live', decision tree, 'When it fails', Anti-Patterns) and the --split 1:1:1 / d_sel_too_small rationale is repeated twice, so it could be meaningfully tightened.

3 / 5

Actionability

Concrete copy-paste commands throughout ('gbrain skillopt X --bootstrap-from-skill --split 1:1:1', '--resume <run-id>'), exact error codes, and the JSONL benchmark line shape make this mostly executable. Minor gaps: the pipeline's D3/D5/D6/D7/D8/D9/D12/D14/D17 design IDs are cited but never defined, and no worked example of a strengthened judge check is given, only the vocabulary.

4 / 5

Workflow Clarity

The pipeline diagram gives an explicit sequence (pre-flight gates → baseline eval → gated epochs → final test eval), the bootstrap flow is a numbered 4-step checklist (generate → strengthen judges → delete sentinel → re-run), and validation is load-bearing throughout: median-of-3 + epsilon gate, hard-refusal codes, cost preflight with --dry-run, and --resume for crash recovery.

5 / 5

Progressive Disclosure

Section headers and a decision tree give real structure, but the body leans on references that do not resolve in the bundle (D3-D17 IDs with no defining document, ../conventions/agent-operator-protocol.md, conventions/quality.md, an external GitHub tutorial), and the ~50-line judge-strengthening material is inlined where a references file would carry it. No references/, scripts/, or assets/ directories exist.

3 / 5

Total

15

/

20

Passed

Description

28%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

The description names a real niche but communicates it in research jargon rather than user-facing language: no concrete actions, no use-when guidance, and the natural trigger phrases live in the frontmatter triggers list instead of the description. It reads like a paper title, which poor matches for routing and comprehension.

Suggestions

Add an explicit 'Use when...' clause covering the three user moments the body already names: improving a skill against a benchmark, bootstrapping a benchmark for a new skill, and re-tuning after switching target models.

Replace jargon ('self-evolving', 'SkillOpt-paper-grounded text-space optimizer') with concrete actions, e.g. 'Rewrites a skill's SKILL.md body to improve its benchmark score; generates starter benchmarks from an existing skill; validation-gates every edit.'

Fold the natural trigger phrases from the triggers list ('optimize this skill', 'make the skill better', 'tune the skill against the benchmark') into the description so routing works from the description alone.

DimensionReasoningScore

Specificity

'Self-evolving skill optimization' names the domain but the only action is the generic 'optimization'; the real capabilities (rewrite the SKILL.md body, generate a benchmark from the skill, validation-gate each edit) are absent, and 'SkillOpt-paper-grounded text-space optimizer' is abstract jargon. Not a 1 because a concrete domain and one action-like noun are present.

2 / 5

Completeness

The 'what' is vague (no concrete actions beyond 'optimization') and the 'when' is entirely absent — there is no 'Use when...' clause or equivalent trigger guidance in the description, which per the rubric caps completeness at 3 and the vague-what/no-when fit lands at the anchor-2 example level.

2 / 5

Trigger Term Quality

'skill' and 'optimization' are keywords a user would plausibly say ('optimize this skill'), matching the one-or-two-generic-keywords anchor, but the natural phrases users actually use ('make this skill better', 'improve skill X', 'tune the skill') appear only in the frontmatter triggers list, not the description, and are drowned out by 'SkillOpt-paper-grounded' and 'text-space optimizer' jargon.

2 / 5

Distinctiveness Conflict Risk

Skill optimization is a genuine niche distinct from document or code skills, but the description gives no trigger phrases to separate it from closely related skills (skill scaffolding, general 'make this better' requests), fitting the 'somewhat specific but could still overlap' anchor rather than the clearly-distinct anchors 4-5.

3 / 5

Total

9

/

20

Passed

Validation

87%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

relative_links

Relative link issues: 1 suspicious

Warning

Total

14

/

16

Passed

Repository

garrytan/gbrain

Path

skills/skill-optimizer/SKILL.md

Reviewed

Table of Contents

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