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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.

52

Quality

59%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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

Quality

Content

65%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 a model of token-efficient progressive disclosure: a lean overview delegating cleanly to one verified, one-level-deep reference with explicit loading instructions. Its weakness is actionability — SKILL.md itself contains no example of the structured-label format or any concrete conversion step, so a reader (or Claude) must always load the 512-line guide before doing anything.

Suggestions

Inline a minimal quick-start example in SKILL.md — a small before/after pair showing prose CLAUDE.md rules converted to structured labels — so the core transformation is actionable without loading the full guide.

Add a 3-5 step top-level outline of the conversion procedure (e.g. parse rules → classify → convert to labels → validate compliance) in the body, with the detailed guide expanding each step.

State at least one concrete validation checkpoint in the body itself (e.g. how to verify the converted file preserves all original rules) rather than only referencing the guide's validation requirements by category.

DimensionReasoningScore

Conciseness

The ~25-line body contains zero concept explanations, zero padding, and no material Claude already knows — sections are "Detailed Guide", "When to Use This Skill", and "Limitations", each earning its tokens. This matches anchor 5 (lean and efficient; every token earns its place) and is clearly above anchor 4's "minor instances of over-explanation".

5 / 5

Actionability

The only executable guidance in the body is "Read [the detailed guide](references/detailed-guide.md) before executing this skill" plus deferral language ("Treat its safety, prerequisites, and validation requirements as mandatory"); no conversion steps, format examples, or commands appear in SKILL.md itself. This matches anchor 2 (high-level hints, missing the specific steps to execute) — better than anchor 1 because the pointer is concrete and unambiguous, but short of anchor 3, which requires some concrete how-to content in the evaluated text.

2 / 5

Workflow Clarity

The body's sequence is a clear two-step delegation — read the guide fully or by section, then execute treating safety/prerequisites/validation as mandatory — with the "When to Use" and "Limitations" sections framing preconditions. However, the actual conversion sequence and its validation checkpoints are implicit (they live entirely in detailed-guide.md), matching anchor 3 (sequence present but checkpoints implicit) rather than anchor 4's mostly-explicit checkpoints.

3 / 5

Progressive Disclosure

Scored against the actual bundle: the body is a lean overview whose single reference, references/detailed-guide.md, exists, is clearly signaled with a markdown link, and is exactly one level deep (the guide contains no nested references). The loading guidance ("For focused work, load the relevant sections; for end-to-end work, read the guide completely") plus well-organized sections matches anchor 5.

5 / 5

Total

15

/

20

Passed

Description

53%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 states a clear, single concrete capability in third person without padding, but it omits any activation guidance ("when to use") and lacks the natural synonym coverage (system prompt, instructions, rules) that would let users trigger it reliably. It is a mid-tier description: specific enough to distinguish, incomplete enough to under-trigger.

Suggestions

Append a 'Use when...' clause with concrete triggers, e.g. "Use when your CLAUDE.md is long or ignored, token usage from system instructions is too high, or when migrating rules between AI tools (Claude, Codex, Gemini, Grok)."

Add natural synonyms users actually say — "system prompt", "project instructions", "rules file", "memory file" — alongside "CLAUDE.md" to improve trigger-term coverage.

Consider naming one more concrete action (e.g. "migrate rules between AI tools") in the description itself to lift specificity from one action to several.

DimensionReasoningScore

Specificity

"Convert human-written CLAUDE.md into AI-native structured-label format" names the domain and one concrete action, but the rest ("Battle-tested across 4 models", "Same rules, fewer tokens, higher compliance") states claims rather than additional actions. It matches anchor 3 (domain plus 1-2 concrete actions) and falls short of anchor 4, which expects several listed specific actions.

3 / 5

Completeness

The "what" is clear (convert CLAUDE.md into a structured-label format), but there is no "Use when..." clause or equivalent trigger guidance anywhere in the description, capping completeness at 3 per the judging guidelines. It is not a 4 because the "when" is not merely implicit-and-improvable; it is entirely absent.

3 / 5

Trigger Term Quality

"CLAUDE.md", "tokens", and "compliance" are relevant keywords a user might say, but common natural variations such as "system prompt", "instructions", "rules file", or "project memory" are missing. This matches anchor 3 (some relevant keywords, missing common variations) rather than anchor 4's good-but-incomplete coverage.

3 / 5

Distinctiveness Conflict Risk

"Convert human-written CLAUDE.md into AI-native structured-label format" carves out a distinct niche (CLAUDE.md/system-prompt restructuring) with only minor overlap risk against general prompt-engineering or documentation skills. It is not a 5 because the description does not enumerate distinct trigger phrases that would fully separate it from adjacent skills.

4 / 5

Total

13

/

20

Passed

Validation

93%

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

Validation — 15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

15

/

16

Passed

Repository
sickn33/agentic-awesome-skills
Reviewed

Table of Contents

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