AD data-analysis
AI-powered data analysis using EvoLink API. Decision-first methodology with statistical rigor. Powered by evolink.ai
As a process D 48/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
IntegrationData and analyticsInfrastructureResearchtype 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: 5. 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 48/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (data-analysis) differs from the folder (evolink-data-analysis)
- 60Tools and files. Uses tools (bash, web) that frontmatter does not declare
- 100Steps. 34 steps
- 100When it triggers. States when to use and when not to
- 100Execution cost. Instruction body is 1225 tokens
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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)
- +3Description length 116: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Structure: 11 headings
- +3Step-by-step instructions: 34 items
- +4Has examples (3 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.
External checks
ClawHub: suspicious
This looks like a real AI data-analysis skill, but it sends full local file contents to an external Evolink API without clear consent or permission scoping.
LLM: suspicious (medium) · VirusTotal: · 29 May 2026