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BC render-vo-anchored-motion-listicle

Assemble a narrated motion-graphic listicle video ad from a config: a voiceover (cloned or cast, in the chosen tone) reads a numbered list while N animated web-graphic beats in one branded design system (alternating tiles, big numerals, callouts) are rendered frame by frame and timed to the voiceover's words. Optional B-roll windows (a brand clip, an AI clip or a motion graphic) give the eye a break, and captions appear only inside those windows, with the voiceover mixed over a low music bed. Free, deterministic assembly; the voiceover, music and any AI clips come from create-vo-elevenlabs, create-music-elevenlabs and create-video-fal. Use for the vo-anchored-motion-listicle format.

gooseworks-ai/goose-skills Agent Skills author: gooseworks-ai 6 files body ≈ 1 365 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Assemble a narrated motion-graphic listicle video ad from a config: a voiceover (cloned or cast, in the chosen tone) reads a numbered list while N animated…

As a process C 51/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

GeneratorPlaywrightMarketingMedia and videoDesigntype 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
92/100
safety, quality, tests
Safety 60%
100
Quality 40%
79
Run on models
none yet
Process rating
C
51/100
Has gaps
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

The same skill appears in 1 more place: 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 51/100

    • 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. 4 mutating operations with no state check
    • 100Tools and files. No external tools needed
    • 100Steps. 14 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1365 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
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 691: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 14 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.