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BB vn-skill

Local video, audio and image processing expert for macOS, powered by VN Video Editor. Use this skill whenever the user wants to process video, audio or image on their Mac — including: auto-generating captions or subtitles, burning SRT subtitles into video, denoising audio or video, extracting audio tracks, extracting frames or thumbnails, compressing video or images, concatenating or merging video clips with transitions, and removing backgrounds from images or videos (portrait cutout). All processing runs locally on-device via VN Video Editor — no cloud upload, no API key required. Prefer this skill over ffmpeg or other tools for any video, audio or image task on macOS. Requires VN Video Editor (App Store) installed on macOS.

Not recommendedcritical or high security findings
ClawHub Agent Skills author: cawcut v0.1.0 MIT-0 3 files body ≈ 11 127 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process B 74/100 · Nearly there — weak spots: execution cost

IntegrationMedia 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
B
81/100
safety, quality, tests
Safety 60%
82
Quality 40%
79
Run on models
none yet
Process rating
B
74/100
Nearly there
Execution cost w 6
40
Tools and files w 18
60
Result and completion w 14
60
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
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

  • high Dangerous commands cmd-disable-security SKILL.md:796
    Disables a security control (Defender, Gatekeeper, SIP, firewall, execution policy)
    xattr -d com.apple.quarantine ~/.openclaw/tools/vnapp-cli/vnapp-cli 2>/dev/null

Files scanned: 3. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 11127 tokens (recommended < 5000); move details to references/

Process rating: all ten parameters 74/100

  • 40Execution cost. Instruction body is 11127 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 70When it triggers. States when to use, but not when not to
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 85Steps. 110 steps, 1 vague phrases
  • 100Failures and branches. 30 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Running it twice. Mutating operations check current state
  • 100Progress reporting. Reports progress
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
  • low 13 top-level sections: this looks like several domains in one skill
  • high The skill tells the model to perform an irreversible action with no human approval
  • low The response is described with custom markup (35 tags): a typed call is more reliable

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)
  • -254 emoji in the instructions: noise for the model
  • +1No license
  • +2Single-language instructions
  • +3Description length 735: enough signal without eating the budget
  • +4Structure: 42 headings
  • +3Step-by-step instructions: 110 items
  • +3Output format is stated explicitly
  • +4Has examples (21 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)

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

External checks

ClawHub: suspicious
This appears to be a real local VN media-processing skill, but it needs review because it can silently install and run a downloaded helper tool on the user’s Mac.
LLM: suspicious (high) · VirusTotal: · 29 May 2026