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AB video-generator

Automated text-to-video pipeline with multi-provider TTS/ASR support - OpenAI, Azure, Aliyun, Tencent | 多厂商 TTS/ASR 支持的自动化文本转视频系统

ClawHub Agent Skills author: Justin Liu v1.0.42 MIT-0 5 files body ≈ 1 769 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 69/100 · Nearly there — weak spots: result and completion, running it twice, progress reporting

GeneratorAzureInfrastructureMedia 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
90/100
safety, quality, tests
Safety 60%
98
Quality 40%
79
Run on models
none yet
Process rating
B
69/100
Nearly there
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 2

    ✓ No critical or high findings

    Medium and low: 2
    • low Exfiltration read-dotenv SKILL.md:176
      Reads a .env file
      cp .env.example .env
    • low Exfiltration read-dotenv SKILL.md:335
      Reads a .env file
      cat ~/openclaw-video-generator/.env

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

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "repository"
    • note frontmatter-key unknown frontmatter key "homepage"
    • note frontmatter-key unknown frontmatter key "requires"
    • note frontmatter-key unknown frontmatter key "install"

    Process rating: all ten parameters 69/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 51 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1769 tokens
    • low 14 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
    • -215 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 129: enough signal without eating the budget
    • +4Structure: 33 headings
    • +3Step-by-step instructions: 51 items
    • +4Has examples (15 code blocks)

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

    External checks

    ClawHub: suspicious
    This appears to be a legitimate video-generation skill, but it needs review because its instructions can expose API keys and use cloud services or installs under overly broad conditions.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026