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remix-graphic-ad-from-reference

Recreate a static graphic ad (Pinterest pin, IG/FB feed image, poster) from a reference image, swapping in a new brand's product and new copy while keeping the reference's layout, composition, and visual energy. ALWAYS generated with GPT Image 2 in edit-the-reference mode (fal-ai/gpt-image-1/edit-image, a billed FAL generation); the HTML/goose-graphics overlay is only an optional text-finishing step, never the generator. The static-graphics counterpart to the video remix-ad skill; this is what the app calls when a user picks a reference ad and wants it for their own product.

69

Quality

87%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

SKILL.md
Quality
Evals
Security

Quality

Content

81%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.

An exceptionally actionable and well-validated workflow document — exact commands, flags, paths, a hard fidelity gate, and per-failure-mode fixes. Its main weakness is token efficiency: the engine rule and several safety rules are each repeated many times, inflating context cost without adding information, and it carries one dangling `tests/` reference.

Suggestions

State the 'ALWAYS GPT Image 2, HTML is only a finishing step' rule once in Decision Rules and trim its re-statements from the Purpose, Inputs (`route_hint`), Phase 1, Phase 2A heading, and Spend Reference — this alone would cut significant length without losing information.

Consolidate the repeated colour discipline ('never invent a colour') and text-stacking rules into single entries in Decision Rules / Failure Modes instead of restating them in Brand grounding, Quality Checks, and Failure Modes.

Remove or fix the `tests/` reference (the directory is absent from the bundle), and consider moving the Quality Checks detail and Spend Reference into a reference file so SKILL.md stays a lean overview.

DimensionReasoningScore

Conciseness

The body is dense with genuinely non-obvious operational detail, but the same rules are repeated many times: "ALWAYS GPT Image 2 / never HTML as the generator" appears in the Purpose, Inputs (`route_hint`), Phase 1, the Phase 2A heading, Decision Rules, Failure Modes, and Spend Reference; "never invent a colour" and "never stack two text layers" are each stated 3+ times. This is beyond the "minor instances" of anchor 4 — the document could lose a third of its length by stating each rule once in Decision Rules.

3 / 5

Actionability

Guidance is copy-paste concrete throughout: the exact renderer command (`node <goose-graphics>/screenshot/screenshot.js --format <canvas> --input index.html --output render.png --font-delay 1500`), exact FAL slug and flags (`--aspect_ratio 3:4 --quality high --resolution 2k`, upscale via `fal-ai/esrgan`), concrete file paths (`scripts/cutout_product.py`, `assets/overlay-template.html`), aspect→canvas mappings, and a per-slot copy-authoring procedure. It fully specifies what to do for both generation paths and the common failure cases.

5 / 5

Workflow Clarity

The phases (0 → 0.5 → 1 → 2A/2B → 3) are clearly sequenced with an explicit validation checkpoint — the "fidelity gate (3 checks, all must pass)" — plus a Quality Checks section with concrete pass/fail criteria and feedback loops (re-roll on the original reference, overlay text for text-only failures, reject for non-remixable references) and a Failure Modes section pairing each cause with a fix.

5 / 5

Progressive Disclosure

Structure is good: operational detail sits in clearly-signaled bundle files that actually exist (`scripts/cutout_product.py`, `assets/overlay-template.html`), external capabilities are referenced one level deep by path, and sections are well-organized with headers. Not anchor 5: the body is a ~230-line monolith where QC detail, failure modes, and spend tables are all inline, and the referenced `tests/` directory does not exist in the bundle — a dangling reference.

4 / 5

Total

17

/

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: concrete capabilities, natural format keywords, an explicit when-clause tied to the app flow, and deliberate disambiguation from the video remix sibling. The only drag is that a third of its length is spent on engine/billing internals that add specificity noise rather than user-facing capability coverage.

DimensionReasoningScore

Specificity

"Recreate a static graphic ad... swapping in a new brand's product and new copy while keeping the reference's layout, composition, and visual energy" names several concrete actions (recreate, swap product, swap copy, preserve layout, optional text-overlay finishing). It stops short of anchor 5 because the second sentence digresses into engine plumbing (FAL slug, billing) rather than enumerating the full capability surface (e.g., copy auto-authoring, SaaS/app UI mode).

4 / 5

Completeness

The "what" is explicit (recreate a reference ad with a new brand's product and copy, generated via GPT Image 2 edit mode with HTML overlay as optional finishing), and the "when" is explicit and concrete: "this is what the app calls when a user picks a reference ad and wants it for their own product". Both are answered with concrete trigger phrasing, matching the anchor-5 example structure.

5 / 5

Trigger Term Quality

Natural trigger terms are present — "Pinterest pin", "IG/FB feed image", "poster", "reference ad", "static graphic ad", "remix" — phrased the way a user would say them. Not anchor 5 because common variants like "Instagram", "image ad", "banner", or "graphic" are missing while the slug "fal-ai/gpt-image-1/edit-image" is technical jargon a user would never say.

4 / 5

Distinctiveness Conflict Risk

It carves out a clear niche (static graphic ad remix from a reference image) and explicitly disambiguates from the closest neighbor: "The static-graphics counterpart to the video remix-ad skill". Medium/formats are enumerated (Pinterest pin, IG/FB feed, poster), so it is unlikely to trigger for the wrong skill.

5 / 5

Total

18

/

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.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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