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BD render-narrated-ugc-wardrobe-stitch

Assemble a narrated-UGC "stitch reply" ad from a config: one voiceover carries a verbatim testimonial while about 30 short clips (one creator across about 5 outfits and 3 settings, plus product B-roll) are trimmed to windows cut from the voiceover's word timings and joined. The voiceover sits over a ducked music bed, word-by-word captions run throughout, a landing-page scroll is animated from a screenshot, and the brand's real end card closes it (never AI-rendered text). Free, deterministic assembly using the shared stitch-videos-ffmpeg helper; the voiceover, creator stills and clips come from create-vo-elevenlabs, create-image-gpt-image-fal, create-image-fal and create-video-fal. Use for the narrated-ugc-wardrobe-stitch format.

athina-ai/goose-skills Agent Skills author: athina-ai 6 files body ≈ 1 474 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Assemble a narrated-UGC "stitch reply" ad from a config: one voiceover carries a verbatim testimonial while about 30 short clips (one creator across about 5…

As a process D 43/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorMedia and videoMarketingtype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
B
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
43/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: render-narrated-ugc-wardrobe-stitch (gooseworks-ai/goose-skills)

How to improve

    For the model run — optional
    • Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
    • A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.

    Guard findings · 0

    ✓ No critical or high findings

    Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "status"

    Process rating: all ten parameters 43/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 10 mutating operations with no state check
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 13 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1474 tokens

    Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.

    Quality signals

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +3Description length 738: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 13 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.