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beat-sync-reel

Generates Instagram Reels where product image cuts are synced to audio beats. Accepts audio as a local file, URL, or search query. Uses librosa for beat detection, FFmpeg Ken Burns for scene animation, and Pillow for text overlays. No AI video generation — fully free, fast, and scalable.

56

Quality

66%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

Fix and improve this skill with Tessl

tessl review fix ./skills/design/packs/video-production/beat-sync-reel/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

62%

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

Highly actionable with executable, copy-paste-ready code across a clear seven-step pipeline, but it lacks explicit validation checkpoints for its batch operations and is a monolithic file with no progressive disclosure to offload detail.

Suggestions

Add explicit validation/feedback checkpoints in the pipeline (e.g. after beat detection confirm beat_times is non-empty; after scene generation verify each scene.mp4 exists and has correct duration before concat) to support error recovery.

Move the Style Presets table and image-classification heuristics into separate reference files (e.g. STYLE_PRESETS.md, IMAGE_FILTERING.md) and signal them from the main body, reducing the monolithic length.

Trim restated context and consolidate the Cost/Known Limitations sections to tighten token efficiency.

DimensionReasoningScore

Conciseness

Mostly efficient with lean code blocks and tables, but the pipeline re-states some context Claude already knows (e.g. classifying images, the heuristic tables) and the Known Limitations/Cost/Example sections add length that could be trimmed.

2 / 3

Actionability

Provides fully executable FFmpeg/Python snippets — beat_track, zoompan filters, concat, audio mix — with concrete parameters, plus copy-paste-ready yt-dlp and ffprobe commands throughout each step.

3 / 3

Workflow Clarity

The seven-step pipeline is clearly sequenced, but for a batch/generative operation there are no explicit validation checkpoints (e.g. verify beats detected, verify scenes rendered before concat) — the only checks are soft 'agent should' notes in Known Limitations, which caps workflow clarity at 2.

2 / 3

Progressive Disclosure

Well-organized into sections, but it is a single ~250-line monolithic file with no bundle files or one-level-deep references; substantial detail (Style Presets table, classification heuristics) that could live in separate files is inline.

2 / 3

Total

9

/

12

Passed

Description

70%

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, tool-grounded description with a clear niche, but it omits an explicit 'Use when...' trigger clause, capping completeness and weakening trigger-term quality.

Suggestions

Add an explicit trigger clause, e.g. 'Use when the user wants an Instagram Reel where product image cuts land on audio beats, or asks to sync a product video to music/beats.'

Surface more natural user phrasings ('reel', 'beat-synced video', 'sync photos to a song') so the description matches what users actually say.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'product image cuts are synced to audio beats', beat detection via librosa, FFmpeg Ken Burns scene animation, and Pillow text overlays — naming both the actions and the specific tools.

3 / 3

Completeness

Clearly answers 'what does this do', but there is no 'Use when...' clause or equivalent explicit trigger guidance, so the 'when to use it' half is only implied — capping completeness at 2 per the rubric.

2 / 3

Trigger Term Quality

Includes relevant natural terms like 'Instagram Reels' and 'product image', but lacks common variations a user would say (e.g. 'reel', 'beats', 'sync video to music', 'product video') and the trigger phrasing is not framed as user language.

2 / 3

Distinctiveness Conflict Risk

The narrow niche of beat-synced product Reels with named tools and the explicit 'No AI video generation' boundary make it clearly distinguishable and unlikely to trigger for the wrong skill.

3 / 3

Total

10

/

12

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.

Validation14 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

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
Reviewed

Table of Contents

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