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generate-image

Generates or edits images with AI models through the OpenRouter Image API (Gemini, Seedream, Recraft, GPT-Image, Riverflow). Use for photos, illustrations, artwork, concept art, visual assets, logos, and image editing or compositing from reference images. For flowcharts, circuits, pathways, and other technical diagrams, use the scientific-schematics skill instead.

73

Quality

92%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

88%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.

A highly actionable, well-sequenced skill body built almost entirely of non-obvious, dated API facts, with strong validation checkpoints. Its weaknesses are mild: some inlined catalogue detail duplicates the models.md reference and the tail sections could be tightened.

Suggestions

Move the per-model capability-difference bullets and per-model cost caveats into references/models.md, keeping only one representative example in the body.

Trim the citation-procedure tail to the reference block plus one line, moving the fetch-and-verify steps into the reference or a linked file.

DimensionReasoningScore

Conciseness

The ~320-line body is dense with dated, model-specific facts Claude cannot know (per-model allowed values, reference counts, pricing) and explains almost no known concepts, but the per-model capability bullets and the citation-procedure tail partly duplicate or drift from the core task and could be trimmed.

4 / 5

Actionability

Every section carries copy-paste-ready commands: quick start, catalogue inspection, a real rejected-request transcript, seven executable worked examples, a direct curl request, and a complete flag table with defaults.

5 / 5

Workflow Clarity

The flow (key resolution -> model choice -> free pre-billing validation via --dry-run/preflight -> prompt recipe -> cheap iteration -> edit via reference) has explicit validation checkpoints, a 'look at the image' read-back step, and error-triage feedback (401/402/403, Retry-After).

5 / 5

Progressive Disclosure

One clearly signaled, one-level-deep reference (references/models.md, cited twice and present on disk) and a consistently path-invoked script, but a notable share of catalogue detail (capability-difference bullets, cost caveats) is inlined in the body where the reference already carries it.

4 / 5

Total

18

/

20

Passed

Description

95%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 strong description: concrete actions, an explicit use-when clause with natural trigger terms, and an explicit redirect boundary for adjacent skills. The only gap is that a few covered capabilities (vector output, transparency) are not named.

DimensionReasoningScore

Specificity

Names three concrete actions ('Generates or edits images', 'image editing or compositing from reference images') plus the backing API and model families, but omits covered capabilities like vector/SVG output and transparent backgrounds — a minor coverage gap rather than comprehensive.

4 / 5

Completeness

Explicitly answers what ('Generates or edits images with AI models through the OpenRouter Image API') and when ('Use for photos, illustrations, artwork, concept art, visual assets, logos...') with concrete trigger phrases, matching the top anchor.

5 / 5

Trigger Term Quality

'photos, illustrations, artwork, concept art, visual assets, logos, image editing or compositing' gives comprehensive natural-synonym coverage of the phrases users would actually say for this domain.

5 / 5

Distinctiveness Conflict Risk

Clear niche with an explicit negative boundary ('For flowcharts, circuits, pathways, and other technical diagrams, use the scientific-schematics skill instead'), actively disambiguating against the nearest competing skill.

5 / 5

Total

19

/

20

Passed

Validation

87%

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

Validation — 14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

metadata_field

'metadata' should map string keys to string values

Warning

referenced_paths_exist

Referenced path issues: 1 missing

Warning

Total

14

/

16

Passed

Repository

K-Dense-AI/scientific-agent-skills

Path

skills/generate-image/SKILL.md

Reviewed

Table of Contents

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