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AC narrated-handdrawn-story-video

Create polished Chinese story short videos from story text or ordered illustrations: story-specific multi-scene colored hand-drawn visuals, a text-led opening poster, sentence-synchronous local Qwen3-TTS narration with optional character voices, subtitles, and licensed BGM mixed beneath narration. Use for idiom stories, children's stories, history explainers, or any Chinese narrated hand-drawn short-video request where visual variety and audio-text synchronization matter.

ClawHub Agent Skills author: ToBeWin v0.1.0 MIT-0 46 files body ≈ 1 239 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

Create polished Chinese story short videos from story text or ordered illustrations: story-specific multi-scene colored hand-drawn visuals, a text-led opening…

As a process C 52/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

GeneratorGitHubMedia 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
91/100
safety, quality, tests
Safety 60%
95
Quality 40%
84
Run on models
none yet
Process rating
C
52/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
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 · 5

    ✓ No critical or high findings

    Medium and low: 5
    • low Secrets in code secret-high-entropy-token renderer/package-lock.json:37
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha5…55h/ff8EMaJ+cYhy…4pW+sXf9…fFg==",
      quoted
    • low Secrets in code secret-high-entropy-token renderer/package-lock.json:56
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha512-+j7a5…6jc+aLJg…vzS/ob7w==",
      quoted
    • low Secrets in code secret-high-entropy-token renderer/package-lock.json:69
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha5…IJ5+F21M…aFJ/0dsi…lOA==",
      quoted
    • low Secrets in code secret-high-entropy-token renderer/package-lock.json:142
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha5…S4F+im88…HRE/cYlx…2oE/ufM0p61IKng==",
      quoted
    • low Secrets in code secret-high-entropy-token renderer/package-lock.json:187
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "integrity": "sha5…rdY+ziCYCPMmtZjjIwOmXFjmyzEHn+UUxk5of+SYsj…5hY/rOAw==",
      quoted

    Files scanned: 41. 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 52/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 5 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
    • 100Steps. 21 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1239 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
    • -4Absolute local paths (C:\Users, /home/…): not portable
    • +2Single-language instructions
    • +3Description length 476: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 21 items
    • +4Has examples (2 code blocks)
    • +3All 3 scripts are documented
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: clean
    This skill is a disclosed video-production workflow whose command use, local file access, model download, and media rendering steps fit its stated purpose, though its optional Python ML dependencies should be installed cautiously.
    LLM: benign (high) · VirusTotal: · 26 Aug 2026