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skillify

The meta skill. Turn any raw feature into a properly-skilled, tested, resolvable unit of agent capability. Idempotent: running on an existing skill improves it (bug fix, new input, quality pass) instead of starting from scratch. Every skill declares an EVAL CONTRACT (its goal + skill-specific dimensions + hard-fails) so the cross-modal eval judges THIS skill's real purpose, not generic slop. Cross-modal eval runs BEFORE tests: 3 frontier models from different providers critique the output against the contract, you iterate to quality, THEN write/update tests that lock in the proven-good behavior. NO-REGRESSION LAW: any edit to a skill must score >= the previous iteration's eval — forward only, never back. For skills that back a scheduled job, an edit MUST re-run a representative task and eval it before shipping.

Quality

74%

Does it follow best practices?

Run evals on this skill

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SecuritybySnyk

Passed

No findings from the security scan

Fix and improve this skill with Tessl

tessl review fix ./skills/skillify/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

77%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.

The body is an exceptionally actionable, well-sequenced operations manual with concrete commands, explicit validation checkpoints, and feedback loops throughout. Its weaknesses are a ~600-line monolithic footprint with significant redundancy (the no-regression law restated many times) and no use of reference files to move worked examples, provider/cost configuration, and slot-resolution details out of the main context.

Suggestions

Consolidate the no-regression law into a single authoritative section and have the checklist, Quality Gates, and Anti-Patterns reference it by number instead of restating it — it currently appears six or more times.

Move the two worked examples, the judge-slot resolution table, and the provider configuration / cost expectation sections into reference files (e.g. references/examples.md, references/eval-config.md) with clearly signaled links from the main body.

Trim the Phase 3 'cycles + cost guardrails' and repeated framing sentences ('THE QUALITY GATE', 'THE FORWARD-ONLY LAW' section headers) to cut token spend without losing any executable content.

DimensionReasoningScore

Conciseness

The body is operational rather than educational — commands, thresholds, and pass criteria rather than concepts Claude already knows — but it is roughly 600 lines with real redundancy: the no-regression law is restated in at least six places (Contract, checklist item 3b, Phase 3.5 heading and section, the hard-rule template, Quality Gates, and Anti-Patterns), and the two long worked examples plus Anti-Patterns repeat body content. This fits the 'mostly efficient but could be tightened' anchor better than the 'noticeably verbose' one, since almost every section carries executable specifics.

3 / 5

Actionability

Guidance is fully executable throughout: copy-paste commands ('gbrain eval cross-modal --task ... --dimensions ...', the jq receipt-reading recipe, the Phase 7 verify block), exact pass criteria (dimension mean >= 7, floor 5, regression threshold 0.5), copy-paste frontmatter and bug-fix templates, a judge-slot resolution table, and two concrete worked examples with real numbers covering both the new-skill and improve modes. Placeholders like <provider:model> and <slug> are appropriate parameterization, not pseudocode.

5 / 5

Workflow Clarity

The process is clearly sequenced Phase 0 through Phase 7 with mode selection up front, a 15-item checklist, an explicit eval→fix→re-eval feedback loop (up to 3 cycles with explicit IF pass/ELSE branches), a forward-only regression gate with concrete FORWARD/REGRESSION verdicts and revert instructions, and a Phase 7 verify block of concrete commands. This matches the anchor with explicit validation steps, feedback loops for error recovery, and a checklist for a complex process.

5 / 5

Progressive Disclosure

No bundle files exist (no references/, scripts/, or assets/), and the ~600-line body inlines substantial material that clearly belongs in separate reference files: the judge-slot resolution table, provider configuration, cost expectations and cycle guardrails, and two long worked examples. Section headers are clear and well-organized, and cross-skill pointers (e.g. skills/conventions/cron-via-minions.md) are one level deep, so this sits between 'minimal structure' and 'good structure' — the 'some structure but content that should be separate is inline' anchor is the best fit.

3 / 5

Total

16

/

20

Passed

Description

63%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.

A dense, highly concrete description that comprehensively covers what skillify does across both new-skill and improve modes, but it is written in internal jargon for an operator who already knows the system rather than in the natural language a user would use to invoke it. The missing explicit 'Use when' clause and thin natural trigger vocabulary are the main weaknesses.

Suggestions

Add an explicit 'Use when...' sentence in the description using natural user phrases, e.g. 'Use when the user wants to turn a feature or script into a skill, asks to add tests and evals to a skill, or wants to check whether a skill regressed.'

Replace or gloss internal jargon ('resolvable unit of agent capability', 'generic slop', 'NO-REGRESSION LAW') with plain-language equivalents so the trigger terms match what users actually say.

Include a few natural synonyms users would type ('make this a skill', 'skillify this', 'did this skill regress') directly in the description text, not only in the frontmatter triggers list.

DimensionReasoningScore

Specificity

The description lists multiple concrete, specific actions with comprehensive coverage of the pipeline: 'Turn any raw feature into a properly-skilled, tested, resolvable unit', 'running on an existing skill improves it (bug fix, new input, quality pass)', 'declares an EVAL CONTRACT (its goal + skill-specific dimensions + hard-fails)', '3 frontier models from different providers critique the output', 'any edit to a skill must score >= the previous iteration's eval', and 'an edit MUST re-run a representative task and eval it before shipping'. It matches the comprehensive-coverage anchor; only minor internal jargon ('resolvable unit of agent capability', 'generic slop') keeps it from being perfectly plain, which is not enough to drop a full anchor.

5 / 5

Completeness

The 'what' is clearly and thoroughly answered, but there is no explicit 'Use when...' clause or equivalent trigger guidance in the description — the only 'when' signal is the weakly implied mode distinction 'running on an existing skill improves it'. Per the rubric guideline, a missing explicit 'Use when' clause caps completeness at 3 even with a clear 'what'.

3 / 5

Trigger Term Quality

Relevant keywords are present — 'skill', 'skillify', 'tests', 'bug fix', 'quality pass', 'eval' — but the natural phrases a user would actually say ('make this a skill', 'turn this into a skill', 'add tests and evals for this') appear only in the frontmatter triggers, not in the description itself. The description leans on internal jargon ('EVAL CONTRACT', 'cross-modal eval', 'NO-REGRESSION LAW', 'frontier models') rather than user language, matching the 'some relevant keywords but missing common variations' anchor.

3 / 5

Distinctiveness Conflict Risk

The description carves a clear niche — 'The meta skill' for turning features into evaluated, tested, regression-proof skills — with distinctive mechanisms (eval contract, 3-provider cross-modal eval, no-regression law) that are unlikely to fire for unrelated skills. It is not a 5 because the distinctiveness rests on internal terminology rather than distinct user-facing trigger phrases, leaving minor overlap risk with any other skill-authoring/quality skill.

4 / 5

Total

15

/

20

Passed

Validation

75%

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

Validation — 12 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (661 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

relative_links

Relative link issues: 1 suspicious

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

12

/

16

Passed

Repository

garrytan/gbrain

Path

skills/skillify/SKILL.md

Reviewed

Table of Contents

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If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.