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skill-improver

Iteratively improve a Claude Code skill using the skill-reviewer agent until it meets quality standards. Use when improving a skill with multiple quality issues, iterating on a new skill until it meets standards, or automated fix-review cycles instead of manual editing.

56

Quality

63%

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 ./plugins/AI-Agents-Safe-Coding-Skills/skills/skill-improver/SKILL.md

The canonical home for this skill is skill-improver in administrakt0r/AI-Agents-Safe-Coding-Skills

SKILL.md
Quality
Evals
Security

Quality

Content

56%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 skill provides a reasonable framework for iterative skill improvement with good workflow structure and a clever completion marker mechanism. However, it suffers from vague actionability—the actual invocation of skill-reviewer is described in natural language rather than concrete commands—and includes some unnecessary explanatory content (rationalizations, concepts Claude already understands). The issue categorization is the strongest section, providing genuinely useful domain-specific classification guidance.

Suggestions

Replace the natural-language invocation instructions with the exact tool call or command syntax for invoking the skill-reviewer agent (e.g., the actual CLI command or agent call pattern).

Remove or significantly trim the 'Rationalizations to Reject' section—Claude doesn't need motivational guardrails phrased this way; convert to a single-line constraint if needed.

Trim the 'When to Use' section since it duplicates the skill description, or remove it entirely and keep only 'When NOT to Use' as the differentiating guidance.

DimensionReasoningScore

Conciseness

The skill contains some unnecessary padding, particularly the 'Rationalizations to Reject' section and the 'When to Use' list which largely restates the description. The minor issue evaluation criteria explain concepts Claude already understands (cost-benefit analysis, false positives). However, the issue categorization tables provide genuinely useful domain-specific knowledge.

3 / 5

Actionability

The invocation method is described in natural language rather than providing an exact command or tool call syntax. The example fix cycle is helpful but the actual mechanism for calling skill-reviewer is vague ('asking Claude to' with a quoted prompt). There are no executable commands—just descriptions of what to do.

3 / 5

Workflow Clarity

The core loop is clearly sequenced (Review → Categorize → Fix → Evaluate → Repeat) with explicit completion criteria and a concrete example showing two iterations. The completion marker mechanism serves as a validation checkpoint. Minor gap: no explicit instruction on what to do if fixes introduce new critical/major issues beyond just 'repeat'.

4 / 5

Progressive Disclosure

All content is inlined in a single file with no references to supporting files. The issue categorization tables and minor issue evaluation criteria could potentially be split out, but the skill is around 100 lines so it's borderline. The structure within the file is reasonable with clear section headers, but the content is somewhat long for a single file with no bundle support.

3 / 5

Total

13

/

20

Passed

Description

71%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 has strong completeness with clear 'what' and 'when' clauses, and reasonable distinctiveness. However, it lacks specificity about the concrete actions performed during the improvement process and could include more natural trigger terms that users would actually say when needing this skill.

Suggestions

Add specific concrete actions performed during improvement, e.g., 'validates frontmatter, checks content structure, fixes formatting issues, ensures proper examples'

Include more natural trigger terms and synonyms like 'polish skill', 'SKILL.md', 'review loop', 'skill quality check', 'refine skill'

DimensionReasoningScore

Specificity

Names the domain (skill improvement) and describes the core action (iteratively improve a skill using a reviewer agent), but lacks concrete details about what specific actions are performed (e.g., linting, formatting, validating frontmatter, checking content structure).

3 / 5

Completeness

Clearly answers both 'what' (iteratively improve a Claude Code skill using the skill-reviewer agent until it meets quality standards) and 'when' (improving a skill with multiple quality issues, iterating on a new skill, automated fix-review cycles instead of manual editing) with explicit trigger scenarios.

5 / 5

Trigger Term Quality

Includes some relevant terms like 'skill', 'skill-reviewer', 'quality standards', 'fix-review cycles', but misses natural user phrases like 'polish skill', 'validate skill', 'skill quality', 'SKILL.md', or 'review loop'. The term 'skill-reviewer agent' is somewhat jargon-like.

3 / 5

Distinctiveness Conflict Risk

Fairly distinct with its focus on iterative skill improvement using a specific reviewer agent, which narrows the scope well. Minor overlap risk with a general 'skill creation' or 'skill review' skill, but the emphasis on iteration and automated cycles helps differentiate.

4 / 5

Total

15

/

20

Passed

Validation

90%

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

Validation10 / 11 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

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

Warning

Total

10

/

11

Passed

Repository
administrakt0r/AI-Agents-Safe-Coding-Skills
Reviewed

Table of Contents

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