AC zenvfx-cli
Use this skill when the user needs to create AI videos, manage canvases, nodes, files, or interact with the ZenVFX platform via CLI. Trigger keywords include "画布", "视频生成", "zenvfx", "canvas", "node", "AI视频", "文生视频".
As a process C 50/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
IntegrationInfrastructureMedia and videotype and topics are labelled automatically from the skill text
How to improve
- 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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6513 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 50/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 30Running it twice. 10 mutating operations with no state check
- 60Tools and files. Uses tools (bash, node) that frontmatter does not declare
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 6513 tokens
- 100Steps. 37 steps
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- low 10 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (19 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
- +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
- +5Description quotes 2 example trigger phrases
- +3Description length 215: enough signal without eating the budget
- +4Structure: 40 headings
- +3Step-by-step instructions: 37 items
- +4Has examples (30 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 79.
External checks
ClawHub: clean
This is a disclosed ZenVFX helper skill whose main risks are expected for managing cloud video projects: installing a CLI, using a token, editing canvases, uploading files, and deleting ZenVFX files.
LLM: benign (medium) · VirusTotal: · 16 Jun 2026