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render-mosaic-grid-reveal

Render a 'mosaic-grid-reveal' video from a config — a real-DOM FULL-BLEED N×N mosaic of real product tiles that pops in one tile at a time (scatter order, ease-out-back overshoot), the grid clears, then the brand wordmark builds line-by-line followed by a sub-label, tagline, and CTA; frame-stepped via Playwright and encoded with FFmpeg — deterministic assembly, FREE (the music bed comes from create-music-elevenlabs), so the wordmark, tile captions, and CTA stay pixel-crisp. Use for the mosaic-grid-reveal format.

65

Quality

78%

Does it follow best practices?

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SecuritybySnyk

Passed

No findings from the security scan

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tessl review fix ./skills/ads/capabilities/render-mosaic-grid-reveal/SKILL.md
SKILL.md
Quality
Evals
Security

Quality

Content

72%

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

A lean, actionable skill body with executable commands and verified bundle references; main gaps are minor redundancy and the absence of an explicit verification checkpoint in the render pipeline.

Suggestions

State the 'never AI-render the wordmark/copy' rule once and reference it rather than repeating it in the intro and Rules.

Add an explicit verification step after encoding (e.g. probe output duration/resolution with ffprobe) before declaring the master done.

DimensionReasoningScore

Conciseness

Mostly efficient and operational, but the 'real wordmark / never AI-render' point is repeated across the intro and Rules, and the 'why pixel-crisp' justification is restated twice.

2 / 3

Actionability

Provides copy-paste-ready commands (build_html.py, render.py, and an ffmpeg mux line) plus a fully enumerated config field list, all executable.

3 / 3

Workflow Clarity

The build → render → mux sequence is clear and build_html writes duration_sec back, but there is no explicit validation/verification checkpoint for the batch encoding pipeline.

2 / 3

Progressive Disclosure

Well-organized into Inputs/Run/Rules with one-level-deep references to real bundle files (scripts/config.example.json, build_html.py, render.py) that all exist on disk.

3 / 3

Total

10

/

12

Passed

Description

85%

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 highly specific, third-person description that clearly states both capability and a 'Use for...' trigger, but its trigger vocabulary is limited to the format name itself with no natural-language variations.

Suggestions

Add natural-language trigger variants a user might actually say (e.g. 'product variant showcase video', '9-tile grid reveal ad') alongside the format name.

Consider condensing the visual-mechanic description so the trigger clause is not buried at the end of a very long sentence.

DimensionReasoningScore

Specificity

Lists multiple concrete actions: 'pops in one tile at a time', 'grid clears', 'wordmark builds line-by-line', 'frame-stepped via Playwright and encoded with FFmpeg'.

3 / 3

Completeness

Explicitly answers what it does (render a mosaic-grid-reveal video) and when to use it via the explicit 'Use for the mosaic-grid-reveal format' clause.

3 / 3

Trigger Term Quality

The only trigger is the format name ('Use for the mosaic-grid-reveal format'); it is a named niche jargon term with no natural user-language variations or common synonyms.

2 / 3

Distinctiveness Conflict Risk

The narrow 'mosaic-grid-reveal' niche and specific toolchain (Playwright/FFmpeg) make it unlikely to trigger for the wrong skill.

3 / 3

Total

11

/

12

Passed

Validation

93%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

15

/

16

Passed

Repository
gooseworks-ai/goose-skills
Reviewed

Table of Contents

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