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BB video-gen

AI video generation via Seedance, Kling, Gemini Omni, MiniMax H3, and MiniMax H3 Max. Use when the user wants to generate a video clip — text-to-video, image-to-video, first/last-frame transitions, reference-guided generation — or wants to modify / edit / extend an existing generated clip.

ChatCut-Inc/agent-plugin Agent Skills author: ChatCut-Inc 7 files body ≈ 5 606 tokens Open the sourcegithub.com↗ analyzed 19 h ago

AI video generation via Seedance, Kling, Gemini Omni, MiniMax H3, and MiniMax H3 Max.

As a process B 71/100 · Nearly there — weak spots: result and completion

GeneratorMedia 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
B
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
B
71/100
Nearly there
Result and completion w 14
0
Tools and files w 18
60
Inputs and preconditions w 11
70
the three weakest of ten parameters · all ten

The same skill appears in 2 more places: agent-plugin, agent-plugin

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: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning body-long SKILL.md body ≈ 5606 tokens (recommended < 5000); move details to references/
  • note edit-residue the text marks something as outdated (lines 42): check that old rules are not kept next to new ones — the full check reads the text for contradictions

Process rating: all ten parameters 71/100

  • 0Result and completion. Does not say what the result is
  • 60Tools and files. Uses tools (bash) that frontmatter does not declare
  • 70Inputs and preconditions. Inputs and preconditions are listed
  • 70Execution cost. Instruction body is 5606 tokens
  • 85Steps. 61 steps, 1 vague phrases
  • 100When it triggers. States when to use and when not to
  • 100Failures and branches. 6 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
  • low 10 top-level sections: this looks like several domains in one skill

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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 290: enough signal without eating the budget
  • +4Structure: 24 headings
  • +3Step-by-step instructions: 61 items
  • +4Has examples (2 code blocks)
  • +4Reference files are cited in the instructions (6 of 6)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.