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img-gen-avatar

Use when working on tools/img_gen avatar image generation, OpenAI-compatible image API config, human or yaoguai portrait prompts, qi-refining base generation, image-to-image realm edits, white-background postprocessing, manifests, or prompt rules that preserve pixel-art identity while changing cultivation realms.

71

Quality

86%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

80%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 concise, well-structured, and offers specific prescriptive rules, but it lacks executable examples and an explicit validation/feedback loop for its batch generation workflow, which limits actionability and workflow clarity.

Suggestions

Add an explicit validation checkpoint and feedback loop for the batch workflow, e.g. 'After generation, verify the manifest/failure JSON; re-run failed entries with --overwrite before proceeding.'

Include at least one concrete executable example (e.g. a sample invocation of a default script with --overwrite) so guidance is copy-paste ready.

Show a minimal text-to-image base prompt and a realm-edit prompt example to make the prompt rules immediately actionable.

DimensionReasoningScore

Conciseness

Lean, dense bulleted content with no padding and no over-explanation of concepts Claude already knows; every section earns its tokens.

5 / 5

Actionability

Provides concrete, specific guidance (preserve lists, forbidden styles, serial/--overwrite/tqdm/manifest rules) but includes no executable code or commands, leaving minor gaps for an instruction-only skill.

4 / 5

Workflow Clarity

The two-stage workflow is clearly sequenced and batch behavior is specified, but there is no explicit validate->fix->retry feedback loop for a batch/destructive operation, which caps the score at 3 per the rubric.

3 / 5

Progressive Disclosure

Well-organized into clear sections with one-level-deep pointers to README.md and DESIGN.md; at under 50 lines with no bundle files, the simple-skill guidance permits a top score.

5 / 5

Total

17

/

20

Passed

Description

92%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 highly specific, well-structured description that clearly conveys both capability and trigger conditions with minimal conflict risk. Its only weakness is reliance on domain jargon that may miss a few everyday phrasings a user would naturally invoke.

DimensionReasoningScore

Specificity

Lists multiple concrete capabilities (OpenAI-compatible API config, human/yaoguai portrait prompts, qi-refining base generation, image-to-image realm edits, white-background postprocessing, manifests, prompt rules) with comprehensive coverage of the avatar-generation domain.

5 / 5

Completeness

Explicitly answers both what it does (config, generation, edits, postprocessing, manifests, prompt rules) and when to use it via a concrete 'Use when...' clause enumerating trigger scenarios.

5 / 5

Trigger Term Quality

Includes natural trigger phrasing ('Use when working on tools/img_gen avatar image generation') and several feature-level terms, but is heavily domain-jargon laden and omits some plainer synonyms a user might naturally say.

4 / 5

Distinctiveness Conflict Risk

Occupies a very narrow niche (cultivation avatar image generation under tools/img_gen) with distinct, specific triggers and minimal risk of conflicting with unrelated skills.

5 / 5

Total

19

/

20

Passed

Validation

100%

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

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
4thfever/cultivation-world-simulator
Reviewed

Table of Contents

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