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generate-image

Generate or edit 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.

72

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

The canonical home for this skill is generate-image in K-Dense-AI/scientific-agent-skills

SKILL.md
Quality
Evals
Security

Quality

Content

92%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-structured skill body: executable commands throughout, explicit pre-flight and post-generation validation checkpoints, and a clean single-reference split of the model catalogue. The only trimmable fat is minor duplication between the parameter bullet guide and the flag table.

DimensionReasoningScore

Conciseness

Largely efficient and free of padding — no explanations of concepts Claude already knows, and dense operational specifics (model table, parameter enums, measured costs). It falls short of 5 due to minor over-explanation and redundancy: the per-model parameter bullet list partially duplicates the flag table below it, and the rough guide is repeated twice with the same 'let the check be the authority' caveat. Not noticeably verbose enough for 3 — this matches 'efficient; minor instances that could be trimmed'.

4 / 5

Actionability

Fully executable throughout: copy-paste-ready quick start, seven worked example commands with realistic prompts, a complete flag table with defaults and enums, a raw curl example with a real response body, and concrete prompt-writing structure. Matches the 'fully executable; specific examples cover the common cases' anchor.

5 / 5

Workflow Clarity

Clear sequence with explicit validation checkpoints and feedback loops: a free preflight check that fails locally before billing ('a bad parameter fails locally in under a second with the legal values printed'), '--dry-run' to validate without cost, an explicit post-run verification step ('**Then look at the image.** Read the file back and check it'), and error-recovery guidance for refusals and retries. Matches the top anchor.

5 / 5

Progressive Disclosure

The body is a well-organized overview with a single one-level-deep reference — 'references/models.md carries the full catalogue with per-model parameters, allowed values, and prices' — which exists in the bundle and is clearly signaled, plus the bundled script. Model detail is appropriately split out (the body keeps only a selection table), navigation is easy, and the free live-listing commands are shown instead of the full catalogue. Matches the top anchor.

5 / 5

Total

19

/

20

Passed

Description

87%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: third person, concrete, with an explicit 'Use for' trigger clause and explicit boundary routing to a sibling skill. The only weaknesses are a few missing natural synonyms and a slightly thin action list.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — 'Generate or edit images', 'compositing from reference images', plus artifact types like logos and concept art — but coverage is not fully comprehensive (e.g., model selection or variations is not surfaced). It sits between the 'several specific actions with minor gaps' (4) and comprehensive (5) anchors, closer to 4 since only 2-3 distinct actions are named.

4 / 5

Completeness

Clearly answers both: what it does ('Generate or edit images with AI models through the OpenRouter Image API') and when to use it ('Use for photos, illustrations, artwork, concept art, visual assets, logos, and image editing or compositing from reference images'). Explicit trigger guidance is present with concrete trigger phrases, matching the top anchor exactly.

5 / 5

Trigger Term Quality

Good natural-keyword coverage ('photos, illustrations, artwork, concept art, visual assets, logos, image editing or compositing'), but common variations users would say — 'draw', 'picture', 'make an image', 'AI art' — are absent. Matches the 'good keyword coverage; a few natural terms missing' anchor rather than the comprehensive synonym coverage of 5.

4 / 5

Distinctiveness Conflict Risk

Clear niche with distinct triggers and an explicit boundary clause — 'For flowcharts, circuits, pathways, and other technical diagrams, use the scientific-schematics skill instead' — which actively routes away near-miss cases. Minimal conflict risk, matching the top anchor.

5 / 5

Total

18

/

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/claude-scientific-writer
Reviewed

Table of Contents

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