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AD wan-3-video-prompt-architect

Build and review production-ready prompts and short shot plans for WAN-30.video text-to-video or image-to-video work. Use when a user needs clearer subject motion, camera direction, scene continuity, audio direction, reference-image roles, or a pre-generation configuration check; do not use this Skill to claim official Alibaba or Wan affiliation, call a model provider, access an account, or spend credits.

ClawHub Agent Skills author: happyhorse v1.0.0 MIT-0 2 files body ≈ 1 161 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Build and review production-ready prompts and short shot plans for WAN-30.video text-to-video or image-to-video work. Use when a user needs clearer subject…

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

AnalyzerGitHubAI and agentsMedia 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
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
Run on models
none yet
Process rating
D
47/100
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

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: 2. 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 47/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 100Steps. 27 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1161 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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 408: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 27 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    The skill is mainly a prompt-writing guide, but its optional setup tells users to run unpinned remote GitHub code locally, so it should be reviewed before installation.
    LLM: suspicious (high) · 14 Sept 2026