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render-podcast-skit

Render a two-host podcast ad from a brand config and dialogue script. Includes the working planner, full-frame and split-screen assembly, captions from measured character timings, brand end card, approved paid-step adapters, and 65 quality-check falsification cases. Use for short conversational ads with two stable hosts in one room.

70

Quality

88%

Does it follow best practices?

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SecuritybySnyk

Passed

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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.

A disciplined, token-efficient overview of a complex paid-generation pipeline with strong sequencing and validation checkpoints at every irreversible step. The main gaps are minor: a duplicated human-facing summary, and role-based references to bundled scripts that omit explicit file names or links.

DimensionReasoningScore

Conciseness

The body is lean and imperative with no padding or explanation of known concepts, but the 'Human version' section largely duplicates the agent section's summary ('Voice, image and lip-sync generation use the separately installed provider capabilities' appears in both). Anchor 4 fits: minor duplicated content that could be trimmed, but otherwise every line earns its place.

4 / 5

Actionability

The workflow gives concrete, executable direction ('Use the bundled driver for a free preview', 'Lock one wide two-host plate and crop both singles from it', 'Both confirmation and execution flags are required') and points to scripts/README.md which contains copy-paste commands. It is not a 5 because the body itself never names the driver script or the exact flags — an agent must open the README to execute anything.

4 / 5

Workflow Clarity

The six-step sequence is clearly ordered with explicit validation checkpoints at every paid step: 'Review the script, caption layout, cut pace... before generation', 'Review the plate before spending on clips', 'watch it end to end', and 'Run the bundled self-test to prove the checks reject their known-bad inputs'. Resume/recovery guidance ('Preserve completed audio and timing responses when resuming') and a final review checklist ('identity, voice fit, room continuity, lip-sync, caption safety and the brand card') complete the anchor-5 pattern for a paid, irreversible workflow.

5 / 5

Progressive Disclosure

The body is a clean overview delegating commands to scripts/README.md (verified present, one level deep, no nested chaining beyond PIPELINE.md → README.md), and referenced artifacts (driver, self-test, examples, arc menu) all exist in the bundle. It is not a 5 because references are role-based rather than path-linked — 'the bundled driver', 'an arc from the bundled menu' never name their files (one_shot.py, arcs.json), leaving minor discovery work.

4 / 5

Total

17

/

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.

A dense, concrete description with an explicit and well-conditioned 'Use for' trigger clause. All four dimensions perform at or near the top of the scale; the only improvement space is broader natural-language synonyms for the ad/rendering domain.

DimensionReasoningScore

Specificity

The description lists multiple concrete components — 'working planner, full-frame and split-screen assembly, captions from measured character timings, brand end card, approved paid-step adapters, and 65 quality-check falsification cases' — comprehensively covering the package's capabilities. It is not a 4 because there are no meaningful gaps: planning, both edit modes, captioning, the end card, paid generation, and QC are all enumerated.

5 / 5

Completeness

The 'what' is explicit ('Render a two-host podcast ad from a brand config and dialogue script') and the 'when' is an explicit trigger clause ('Use for short conversational ads with two stable hosts in one room') with concrete conditions. It is not a 4 because the when-clause is already specific about ad type, host count, and staging.

5 / 5

Trigger Term Quality

Good natural keyword coverage: 'podcast ad', 'two-host', 'conversational ads', 'hosts', 'brand config', 'dialogue script'. It falls short of a 5 because common synonyms users might say — 'commercial', 'video ad', 'spot', or file extensions like .mp4/.json — are absent.

4 / 5

Distinctiveness Conflict Risk

The 'two-host podcast ad' niche with 'two stable hosts in one room' is highly distinctive and unlikely to fire for unrelated skills. Not a 4: the trigger conditions are narrow and specific to this exact use case, leaving minimal overlap risk.

5 / 5

Total

19

/

20

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.

Validation — 15 / 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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