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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.

70

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

72%

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 and well-structured with clear progressive disclosure to supporting files. Its main weaknesses are deferring runnable commands to README.md and lacking validation feedback loops for batch generation.

Suggestions

Embed or inline the key runnable command(s) for generating and editing avatars instead of only pointing to README.md, so the guidance is copy-paste ready.

Add an explicit validation/verification step (e.g., check the manifest or failure JSON and retry failed images) after batch generation to establish a feedback loop.

Clarify how to detect and handle partial failures (failed-render retry rules) within the workflow so batch runs are self-correcting.

DimensionReasoningScore

Conciseness

The body is lean and assumes Claude's competence, with terse directives like 'Describe visible appearance only.' and no padding explaining what image generation or pixel art is.

3 / 3

Actionability

Guidance is concrete (specific file paths, the '--overwrite' flag, exact forbidden styles), but actual runnable commands are deferred to README.md rather than provided copy-paste ready, so it is not fully executable inline.

2 / 3

Workflow Clarity

The two-stage sequence and explicit anti-pattern guidance are clear, but batch avatar generation with manifests/failure JSON lacks any validation or validate-fix-retry feedback loop, capping the score at 2.

2 / 3

Progressive Disclosure

The body is a concise overview that signals one-level-deep references to README.md for commands and DESIGN.md for structural changes, with content appropriately split and easy to navigate.

3 / 3

Total

10

/

12

Passed

Description

100%

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, trigger-rich, and explicitly signals when to use it, with a clear niche that minimizes conflict risk. It is written in third person with no over-claims.

DimensionReasoningScore

Specificity

Lists multiple concrete actions including 'avatar image generation', 'OpenAI-compatible image API config', 'image-to-image realm edits', and 'white-background postprocessing' rather than vague language.

3 / 3

Completeness

An explicit 'Use when working on...' clause answers when, while the enumerated actions answer what, satisfying both required parts.

3 / 3

Trigger Term Quality

Natural domain terms such as 'image generation', 'portrait prompts', and 'image-to-image realm edits' give good coverage of what a target user would say, complemented by niche cultivation terms.

3 / 3

Distinctiveness Conflict Risk

The highly specific niche ('tools/img_gen avatar', 'yaoguai', 'qi-refining', 'cultivation realms') makes it clearly distinguishable and unlikely to trigger for unrelated skills.

3 / 3

Total

12

/

12

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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