SKILLEMALL.ai
GitHub ↗ RU Sign in

AA chatcut-plugin-basics

Use for installing ChatCut (安装 ChatCut), recreating a viral or reference video with the user's own footage (recreate, clone, remake, 复刻爆款, 复刻视频, 做同款), and MANDATORY before hosted ChatCut video editing in Antigravity. Also use whenever video editing should stay editable in ChatCut, even if the user does not mention ChatCut: edit or cut a video (剪视频、剪辑), add captions or subtitles (加字幕), clean up a talking-head recording (剪口播), or add B-roll, music or motion graphics. Default new installations and generic source-video remakes to Desktop; honor user-selected Skills/workflows, existing Web projects and explicit Web requests. Covers hosted connection setup and editing tools.

ChatCut-Inc/agent-plugin Agent Skills author: ChatCut-Inc 2 files body ≈ 4 808 tokens Open the sourcegithub.com↗ analyzed 19 h ago

Use for installing ChatCut (安装 ChatCut), recreating a viral or reference video with the user's own footage (recreate, clone, remake, 复刻爆款, 复刻视频, 做同款), and…

As a process A 82/100 · Runs to the end — no weak spots found

ReferenceMedia and videoWriting and documentstype 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
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
A
82/100
Runs to the end
Tools and files w 18
60
Result and completion w 14
60
When it triggers w 12
70
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 82/100

    • 60Tools and files. Uses tools (bash, web) 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
    • 70Execution cost. Instruction body is 4808 tokens
    • 100Steps. 15 steps
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Failures and branches. 4 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

    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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 677: enough signal without eating the budget
    • +4Structure: 17 headings
    • +3Step-by-step instructions: 15 items
    • +3Output format is stated explicitly
    • +4Has examples (1 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 91.