SKILLEMALL.ai

BA 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 Codex. 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 diagnostics and editing tools.

ChatCut-Inc/agent-plugin Agent Skills author: ChatCut-Inc 1 file body ≈ 8 881 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 81/100 · Runs to the end — weak spots: execution cost, running it twice

ReferenceMedia and videoWriting and documentstype and topics are labelled automatically from the skill text
JSON
Technical rating
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
77
Run on models
none yet
Process rating
A
81/100
Runs to the end
Running it twice w 4
30
Execution cost w 6
40
Tools and files w 18
60
the three weakest of ten parameters · all ten

How to improve

  1. 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 · 0

✓ No critical or high findings

Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 8881 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 244): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 81/100

  • 30Running it twice. 20 mutating operations with no state check
  • 40Execution cost. Instruction body is 8881 tokens: crowds the task out of the window
  • 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
  • 60Result and completion. Output format stated, no completion criterion
  • 100Steps. 19 steps
  • 100When it triggers. States when to use and when not to
  • 100Inputs and preconditions. Inputs and preconditions are listed
  • 100Failures and branches. 10 branches, has a failure section
  • 100Consistency. Name and required fields are in place
  • 100Progress reporting. Reports progress
  • low 12 top-level sections: this looks like several domains in one skill
  • low The response is described with custom markup (5 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)
  • +1No license
  • +2Single-language instructions
  • +3Description length 677: enough signal without eating the budget
  • +4Structure: 28 headings
  • +3Step-by-step instructions: 19 items
  • +3Output format is stated explicitly
  • +4Has examples (1 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 77.