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ai-md

Convert human-written CLAUDE.md into AI-native structured-label format. Battle-tested across 4 models. Same rules, fewer tokens, higher compliance.

57

Quality

67%

Does it follow best practices?

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Adds up to 20 points to the overall score

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SecuritybySnyk

High

Do not use without reviewing

Fix and improve this skill with Tessl

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

Quality

Content

77%

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

The content is highly actionable with a clear, validated multi-phase workflow, but it is weighed down by motivational padding and concept-explanation Claude already knows, and it is monolithic with no progressive disclosure into separate reference files.

Suggestions

Trim rhetorical/marketing prose ("The paradox we proved", "Real proof", "The uncomfortable truth: ...Always.") and the LLM-attention concept explanations, keeping only the novel methodology and concrete examples.

Move the deep-dive material (detailed 6-phase walkthroughs, real-world results tables, full example set) into reference files under references/ and keep SKILL.md as a concise overview with one-level-deep links.

Reduce first-person narrative framing ("the exact mental model I use") to neutral instructional voice to save tokens and improve clarity.

DimensionReasoningScore

Conciseness

The body carries genuinely novel methodology, but it is padded with motivational prose ("The paradox we proved", "The uncomfortable truth") and explains LLM attention mechanisms Claude already knows, so it could be tightened significantly despite being mostly useful.

2 / 3

Actionability

It provides concrete worked conversions, a 12-label vocabulary table, a copy-paste XML template, an executable bash token-count script, and an 8-question validation protocol — specific and ready to apply.

3 / 3

Workflow Clarity

The six-phase process is clearly sequenced with Phase 6 as an explicit multi-model validation checkpoint and a revert-on-regression feedback loop, plus backup and before/after reporting for the destructive CLAUDE.md edit.

3 / 3

Progressive Disclosure

No bundle files exist, and everything — full methodology, worked examples, results tables, template — lives inline in one ~520-line file; it is well-sectioned but content that should be offloaded to references is not split out.

2 / 3

Total

10

/

12

Passed

Description

57%

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 conveys a clear, distinctive niche and the core transformation, but it omits explicit "when to use" guidance and leans on buzzword-laden marketing phrasing rather than natural trigger terms.

Suggestions

Add an explicit "Use when..." clause naming concrete triggers (e.g., when CLAUDE.md is long and rules get ignored, when token usage from system instructions is too high, when migrating rules between Claude/Codex/Gemini/Grok).

Replace marketing slogans ("Battle-tested across 4 models", "Same rules, fewer tokens, higher compliance") with natural user-facing keywords a person would actually say when they need this skill.

List multiple concrete actions (convert, decompose compound rules, label, structure, validate cross-model) to lift specificity from a single transformation to a fuller action list.

DimensionReasoningScore

Specificity

Names the concrete conversion action ("Convert human-written CLAUDE.md into AI-native structured-label format") and its domain, but describes a single transformation rather than listing multiple specific actions, matching the level-2 anchor.

2 / 3

Completeness

It clearly states what the skill does but provides no "Use when..." trigger clause, so per the rubric guideline a missing explicit trigger caps completeness at 2.

2 / 3

Trigger Term Quality

"CLAUDE.md" and "token usage" are somewhat natural, but the description leans on jargon ("AI-native structured-label format") and marketing slogans ("Battle-tested across 4 models") rather than the terms a user would actually say, with common variations missing.

2 / 3

Distinctiveness Conflict Risk

Converting CLAUDE.md/system prompts into a structured-label format is a clear niche with distinct triggers unlikely to conflict with unrelated skills.

3 / 3

Total

9

/

12

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.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (524 lines); consider splitting into references/ and linking

Warning

frontmatter_unknown_keys

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

Warning

Total

14

/

16

Passed

Repository
sickn33/antigravity-awesome-skills
Reviewed

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