product-reel-generator

Generates Instagram-ready product reels from any e-commerce product page URL. Scrapes product images, classifies by type, generates AI-animated clips via Higgsfield API, creates text overlays with style presets, and composes a 15-20 second reel with music. Supports model-based and product-only reels.

Quality

87%

Does it follow best practices?

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Securityby

High

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Quality

Content

92%

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 pipeline document with concrete commands, API specifics, and error-recovery guidance. The main weakness is progressive disclosure: most detail lives inline in a single long file rather than being split into clearly signaled reference files.

Suggestions

Move the Higgsfield API reference (base URL, auth header, model table, polling) and the style-preset table into separate reference files (e.g., references/higgsfield-api.md, references/style-presets.md) and link to them from SKILL.md to improve progressive disclosure.

Add the missing Pillow Python snippet that renders the transparent PNG text overlays so Step 5 is as copy-paste-ready as the other steps.

Add an explicit final verification checkpoint (e.g., probe the output with ffprobe to confirm 1080x1920, 25fps, AAC audio, 15-20s) before declaring the reel done.

Dimension	Reasoning	Score
Conciseness	The body is dense with actionable tables, FFmpeg commands, and concrete API specs, and avoids explaining basic concepts Claude already knows. It is not a 2 because there is little unnecessary padding — nearly every section earns its place.	3 / 3
Actionability	Most steps give copy-paste-ready commands and specific API details (base URL, auth header, model paths, duration, 15s polling, concat and audio-mix filters). It is not a 2 because the overwhelming majority is executable; the one minor gap (Pillow overlay rendering code is omitted, only the FFmpeg composite is shown) does not pull it to 'some concrete guidance but incomplete.'	3 / 3
Workflow Clarity	A clearly sequenced 7-step pipeline with explicit checkpoints: dependency verification up front, a poll-until-completed feedback loop, and bad-clip→Ken-Burns error recovery. It is not a 2 because checkpoints and feedback loops are explicit rather than implicit.	3 / 3
Progressive Disclosure	The ~190-line SKILL.md is well-organized and signals one real one-level reference (scripts/higgsfield_video.py, which exists), but API specs, style presets, and reel structures all live inline where they could be split into reference files. It is not a 3 because it is not a lean overview pointing to detailed materials, and not a 1 because organization is solid with no deep nesting.	2 / 3
	Total	11 / 12 Passed

Description

82%

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 specific, well-scoped description that names concrete actions and natural trigger terms with low conflict risk. Its main weakness is the absence of an explicit 'when to use this' trigger clause, which caps completeness at 2.

Suggestions

Append an explicit trigger clause such as 'Use when the user wants to turn a product page URL into an Instagram reel, product video, or short ad clip.' to raise completeness to 3.

Add a few natural user phrasings (e.g., 'product video', 'reel from a product link', 'Instagram ad') alongside 'product reels' to broaden trigger coverage.

Dimension	Reasoning	Score
Specificity	Lists multiple concrete actions ('Scrapes product images, classifies by type, generates AI-animated clips via Higgsfield API, creates text overlays with style presets, and composes a 15-20 second reel'), matching the anchor for several specific actions. It is not a 2 because it goes well beyond naming a domain to enumerate distinct operations.	3 / 3
Completeness	Answers 'what' thoroughly but provides no explicit 'Use when...' trigger clause, so 'when' is only implied. Per the judging guidelines, a missing explicit trigger clause caps completeness at 2, so it cannot reach 3.	2 / 3
Trigger Term Quality	Includes natural terms a user would say — 'Instagram-ready product reels', 'e-commerce product page URL', 'reel with music', 'text overlays' — giving good coverage. It is not a 2 because common variations are present rather than sparse.	3 / 3
Distinctiveness Conflict Risk	Targets a clear niche (Higgsfield-API-driven Instagram product reels from e-commerce URLs) with distinct triggers, making conflict with other skills unlikely. It is not a 2 because the scope is too specific to plausibly overlap with sibling skills.	3 / 3
	Total	11 / 12 Passed

Validation

87%

Warnings & errors only

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

Criteria	Description	Result
allowed_tools_field	'allowed-tools' contains unusual tool name(s)	Warning
frontmatter_unknown_keys	Unknown frontmatter key(s) found; consider removing or moving to metadata	Warning

	Total	14 / 16 Passed

Repository: gooseworks-ai/goose-skills
Path: skills/design/packs/video-production/product-reel-generator/SKILL.md
Commit: 8866b2a

Reviewed: 1 day ago

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