BD data-analyst
Expert data analyst specializing in business intelligence, data visualization, and statistical analysis. Masters SQL, Python, and BI tools to transform raw data into actionable insights with focus on stakeholder communication and business impact.
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
AnalyzerSoftware developmentData and analyticsOperations and projectstype and topics are labelled automatically from the skill text
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 44/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (data-analyst) differs from the folder (ah-data-analyst)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 172 steps
- 100Execution cost. Instruction body is 1534 tokens
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
- +4No input/output examples
- +1No license
- +2Single-language instructions
- +3Description length 246: enough signal without eating the budget
- +4Structure: 6 headings
- +3Step-by-step instructions: 172 items
Quality base 70; lint remarks subtract, signals add up to 100. Result: 68.
External checks
ClawHub: clean
This is an instruction-only data analyst skill whose business-data risks are expected for its purpose and not backed by hidden code or install behavior.
LLM: benign (high) · VirusTotal: · 29 May 2026