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rules-distill

Scan skills to extract cross-cutting principles and distill them into rules — append, revise, or create new rule files

68

Quality

83%

Does it follow best practices?

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SecuritybySnyk

Advisory

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SKILL.md
Quality
Evals
Security

Quality

Content

100%

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

The skill body is well-crafted: lean, fully actionable with concrete commands and a ready-to-use subagent prompt, clearly sequenced phases with mandatory user-approval validation, and shallow well-signaled references to real bundle scripts.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence — no padding with concepts Claude already knows; every section (verdict table, subagent prompt, JSON schema, example) earns its place, matching the 'lean and efficient' anchor.

3 / 3

Actionability

Provides concrete executable commands, a complete copy-paste subagent prompt, an exact JSON output schema, and a worked end-to-end example — fully actionable and copy-paste ready rather than vague or pseudocode.

3 / 3

Workflow Clarity

Three clearly sequenced phases with explicit validation checkpoints — the mandatory 'Never modify rules automatically. Always require user approval' gate and the user approve/modify/skip feedback loop, plus the cross-batch merge re-check, satisfy the clear-sequence-with-feedback-loops anchor for a batch operation.

3 / 3

Progressive Disclosure

References two real, verified bundle scripts (scan-skills.sh, scan-rules.sh) inline at their point of use, one level deep, with a well-organized overview body — matching clear one-level-deep references with easy navigation.

3 / 3

Total

12

/

12

Passed

Description

67%

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 is specific and distinctive about what it does, but it lacks an explicit 'when to use' trigger clause and natural trigger-term coverage, which cap completeness and trigger-term quality at 2.

Suggestions

Add an explicit 'Use when...' clause, e.g., 'Use when doing periodic rules maintenance, after installing new skills, or when rules feel incomplete relative to the skills in use.'

Broaden trigger terms with natural user phrasing such as 'update rules', 'maintain rules', or 'extract rules from skills' so the skill surfaces for common requests.

Keep the concrete append/revise/create actions but pair them with the new trigger guidance to raise completeness to a 3.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'Scan skills to extract cross-cutting principles', 'distill them into rules — append, revise, or create new rule files' — matching the anchor for multiple specific concrete actions rather than the level-2 partial list.

3 / 3

Completeness

Clearly states what the skill does but omits any 'when should Claude use it' guidance; per the rubric a missing 'Use when...' clause caps completeness at 2.

2 / 3

Trigger Term Quality

Contains relevant keywords ('skills', 'principles', 'rules', 'rule files') but lacks common natural variations a user would say (e.g., 'update my rules', 'maintain rules') and has no explicit trigger phrasing, placing it at 'some relevant keywords but missing common variations'.

2 / 3

Distinctiveness Conflict Risk

The niche of extracting cross-cutting principles from skills and distilling them into rules via append/revise/create is distinctive and unlikely to trigger for the wrong skill.

3 / 3

Total

10

/

12

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.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_version

'metadata.version' is missing

Warning

Total

15

/

16

Passed

Repository
affaan-m/ECC
Reviewed

Table of Contents

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