SKILLEMALL.ai
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BC ai-recreator

AI video repurposing × digital human dubbing web tool. Paste a Douyin/TikTok link → wait 3 minutes → get a digital human video that looks like YOU said it. 5-step pipeline: download → transcribe → rewrite → TTS → digital human.

ClawHub Agent Skills author: ShyLamb-token v1.0.2 MIT-0 23 files body ≈ 2 127 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

AI video repurposing × digital human dubbing web tool.

As a process C 57/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

ProcedureMedia and videoInfrastructureMarketingtype 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
B
88/100
safety, quality, tests
Safety 60%
99
Quality 40%
72
Run on models
none yet
Process rating
C
57/100
Has gaps
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

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Exfiltration read-dotenv SKILL.md:46
    Reads a .env file
    cp backend/.env.example backend/.env  # then edit .env with your key

Files scanned: 23. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "trigger_terms"

Process rating: all ten parameters 57/100

  • 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
  • 50When it triggers. No condition that starts the skill
  • 50Failures and branches. 0 branches, has a failure section
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 46 steps, 1 vague phrases
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 2127 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
  • -2localhost URLs: will not work for another user
  • +1No license
  • +2Single-language instructions
  • +3Description length 227: enough signal without eating the budget
  • +4Structure: 25 headings
  • +3Step-by-step instructions: 46 items
  • +4Has examples (5 code blocks)
  • +4Reference files are cited in the instructions (4 of 4)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 72.

External checks

ClawHub: suspicious
This is a coherent media-repurposing web app, but it needs Review because it handles sensitive videos, cookies, external services, and persistent outputs with weak scoping and inconsistent disclosure.
LLM: suspicious (high) · 29 Jul 2026