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CD connect-chatcut-desktop

Install, download, open or connect ChatCut Desktop (安装 ChatCut), or start editing or recreating a video with it: edit or cut a video, add captions or subtitles, clean up a talking-head recording, or recreate a viral or reference video with the user's own footage (剪视频、加字幕、剪口播、复刻爆款、复刻视频、做同款). Reuse an existing Desktop connection and its bundled video-gen Skill. For existing Web projects or an explicit Web request, keep the hosted workflow.

ChatCut-Inc/agent-plugin Agent Skills author: ChatCut-Inc 2 files body ≈ 886 tokens Open the sourcegithub.com↗ analyzed 19 h ago

Install, download, open or connect ChatCut Desktop (安装 ChatCut), or start editing or recreating a video with it: edit or cut a video, add captions or…

As a process D 46/100 · Unfinished process — weak spots: steps, result and completion, running it twice

ReferenceMedia and videotype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
C
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
69
Run on models
none yet
Process rating
D
46/100
Unfinished process
Steps w 15
0
Result and completion w 14
0
Progress reporting w 2
0
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 · 0

✓ No critical or high findings

Files scanned: 2. 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")

Process rating: all ten parameters 46/100

  • 0Steps. Prose only: no discrete steps
  • 0Result and completion. Does not say what the result is
  • 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, web) that frontmatter does not declare
  • 60Failures and branches. 2 branches
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 886 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)
  • +3No numbered steps or checklist
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 441: enough signal without eating the budget
  • +4Structure: 3 headings
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.