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AC tiktok-ai-model-generator

Generate AI model videos for TikTok livestreams using Pinterest, Claude, Nano Banana Pro, and Veo or Kling. Use for creating AI-generated fashion models wearing products, animating them into videos, or building automated TikTok content production workflows. This skill provides a complete 4-step workflow covering Pinterest reference selection, Claude JSON prompt generation, Nano Banana Pro image generation, and video animation. Perfect for e-commerce sellers, content creators, and TikTok marketers who need AI models to showcase products.

modbender/skill-library-mcp Agent Skills author: modbender MIT 4 files body ≈ 2 777 tokens Open the sourcegithub.com↗ analyzed 3 d ago

Generate AI model videos for TikTok livestreams using Pinterest, Claude, Nano Banana Pro, and Veo or Kling.

As a process C 61/100 · Has gaps — weak spots: result and completion, when it triggers, running it twice

GeneratorAI and agentsMarketingMedia and videotype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
C
61/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
When it triggers w 12
20
the three weakest of ten parameters · all ten

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: 4. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 61/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 5 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 133 steps, 3 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2777 tokens
    • low 11 top-level sections: this looks like several domains in one skill

    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
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 542: enough signal without eating the budget
    • +4Structure: 32 headings
    • +3Step-by-step instructions: 133 items
    • +4Has examples (7 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.