AC data-analyzer
Load, analyze, and report on structured data from CSV/Excel/JSON files. Use when the user needs to: (1) compute descriptive statistics (mean, median, std dev, percentiles), (2) detect anomalies and trends, (3) analyze correlations between variables, (4) generate data analysis reports with visualization recommendations, (5) explore and summarize a new dataset.
Load, analyze, and report on structured data from CSV/Excel/JSON files.
As a process C 56/100 · Has gaps — weak spots: inputs and preconditions, consistency, running it twice
How to improve
- 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
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Load, analyze, and report on structured data from CSV/Excel/JSON f… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value - note
frontmatter-keyunknown frontmatter key "emoji"
Process rating: all ten parameters 56/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 30Running it twice. 2 mutating operations with no state check
- 40Consistency. Frontmatter name (data-analyzer) differs from the folder (zcx-data-analyzer)
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (read, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 85Steps. 22 steps, 1 vague phrases
- 100Execution cost. Instruction body is 1868 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)
- +1No license
- +2Single-language instructions
- +3Description length 361: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 22 items
- +3Output format is stated explicitly
- +4Has examples (12 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.