AC license-audit
Multi-language license compliance audit powered by Trivy. Detects GPL/AGPL/LGPL/MPL/BSL and other risky licenses across Node.js, Python, Java, C#/.NET, Go, Rust, Ruby, PHP, and C/C++. Outputs to terminal, Markdown, HTML, JSON, Feishu Doc, and Feishu Base. Supports NuGet API enrichment for C# projects and automatic stack detection with pre-scan guidance.
Multi-language license compliance audit powered by Trivy.
As a process C 63/100 · Has gaps — weak spots: when it triggers, running it twice, progress reporting
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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 · 1
✓ No critical or high findings
Medium and low: 1
-
low Dangerous commands
cmd-pipe-to-shell-known-hostscripts/audit.py:325Pipe-to-shell installer from a well-known host (still executes remote code) (string literal in code, not executed; documentation of a security skill)"curl -sfL https://raw.githubusercontent.com/aquasecurity/trivy/main/contrib/install.sh | sh -s -- -b /usr/local/bin",
code literalsecurity skill
Files scanned: 5. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 63/100
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 9 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 18 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 2822 tokens
- low 13 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (3 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)
- -216 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 355: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 18 items
- +3Output format is stated explicitly
- +4Has examples (8 code blocks)
- +4Reference files are cited in the instructions (1 of 2)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.