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create-image-gpt-image-fal

Generate a single photoreal or designed image with OpenAI gpt-image via fal.ai. Supports gpt-image-1 (default, fixed sizes — the FAL fallback for Higgsfield's `gpt_image_2`) and gpt-image-2 (`openai/gpt-image-2`, custom output sizes up to 3840px). Routes to text-to-image or the edit variant depending on whether a reference image is provided. Use for photoreal character anchors, scene keyframes, and designed sheets (e.g. storyboards) where precise layout and legible text matter.

73

Quality

90%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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.

The body is highly actionable with executable examples, validation checkpoints, and strong failure-recovery guidance. Its main weakness is repeated emphasis of the public-URL constraint that could be consolidated for tighter token efficiency.

Suggestions

Consolidate the 'ref images must be PUBLIC URLs / proxy does not upload local files' guidance into a single authoritative spot (e.g. Inputs) and reference it briefly elsewhere rather than restating it four times.

Move the per-model pricing table into a references file or a clearly labeled 'as-of 2026-05' note so time-sensitive numbers don't clutter the core workflow.

DimensionReasoningScore

Conciseness

Mostly lean and assumes competence with no concept padding, but the 'ref images must be PUBLIC URLs / proxy does not upload local files' point is repeated across Inputs, Workflow, Failure Modes, and References.

4 / 5

Actionability

Copy-paste-ready bash commands cover the three main cases (text-to-image, edit-from-reference, gpt-image-2 custom size), with a concrete preflight block and a detailed failure-mode table with fixes.

5 / 5

Workflow Clarity

A clear Preflight → Workflow → numbered script-behavior sequence with an explicit preflight validation gate and a failure-mode table providing feedback loops for error recovery.

5 / 5

Progressive Disclosure

Well-organized sections with real, one-level-deep bundle references (media_proxy.py, generate.py, fal_helpers.py all exist) clearly listed; most detail is appropriately inline for a skill of this size.

4 / 5

Total

18

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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.

The description is strong: third-person, concrete, with explicit what/when guidance and a clearly delineated niche. Its only gap is slightly thin natural-language trigger coverage compared to the synonyms/extensions a user might say.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — generate, route to text-to-image or /edit, custom output sizes up to 3840px — across two named model variants, giving comprehensive coverage for a generation tool.

5 / 5

Completeness

Explicitly answers both 'what' (generate image, route to T2I/edit) and 'when' ('Use for photoreal character anchors, scene keyframes, and designed sheets').

5 / 5

Trigger Term Quality

Includes natural triggers like 'photoreal character anchors, scene keyframes, and designed sheets (e.g. storyboards)', but lacks common synonyms/extensions (e.g. 'picture', '.png').

4 / 5

Distinctiveness Conflict Risk

A clear niche (gpt-image via fal.ai with the Higgsfield fallback distinction) and specialized triggers keep conflict risk minimal.

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
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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