AC cross-verify
交叉验证信息准确性、识别偏见和逻辑漏洞。当用户说"这段话有没有问题"、"这个数据可靠吗"、"帮我看看这段有没有偏见"、"这个信息准确吗"、"核查一下这段"，或直接丢入一段文字/数据要求检查时触发。Also triggers when the user pastes any text containing statistics, claims, or assertions and asks for verification.
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
AnalyzerData and analyticsInfrastructuretype and topics are labelled automatically from the skill text
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: 2. 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 59/100
- 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
- 0Progress reporting. Says nothing while it works
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 28 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 385 tokens
- 100Running it twice. No mutating operations
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 5 example trigger phrases
- +3Description length 213: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 28 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.
External checks
ClawHub: clean
This is an instruction-only fact-checking skill that may use web search for pasted claims, with no code execution, credentials, persistence, or hidden behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026