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Answer current ChatCut product questions using the latest official Docs, Releases, and Changelog. Use for how ChatCut works; UI layout, buttons, and feature instructions; troubleshooting; feature or fix availability on Web, Desktop, or Agent Plugin and minimum version requirements; credits including costs, balance, usage history, validity, or recent charges; plans, subscriptions, renewals, ChatCut Pro, pricing, card or Alipay (支付宝) payments, and billing; Desktop downloads; Agent Plugin installation or updates for Codex, Claude Code and Antigravity; and manual GUI guidance when an action cannot be completed directly. NOT for live project, asset, timeline, or item state; use the matching read tool instead.

ChatCut-Inc/agent-plugin Agent Skills author: ChatCut-Inc 1 file body ≈ 2 258 tokens Open the sourcegithub.com↗ analyzed 19 h ago

Answer current ChatCut product questions using the latest official Docs, Releases, and Changelog.

As a process C 64/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureAI and agentsSoftware developmenttype 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
B
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
84
Run on models
none yet
Process rating
C
64/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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 · 0

    ✓ No critical or high findings

    Files scanned: 1. 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 64/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 85Steps. 31 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Failures and branches. 2 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2258 tokens
    • 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
    • +3Output format is not stated: the model decides each time
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 713: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 31 items

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.