BC warehouse
Plan and review read-only data warehouse analysis with explicit scope, privacy, provenance, and validation checks.
The skill promises structured data warehouse analysis with explicit scope, privacy, and provenance checks. Sounds reasonable on paper.
One file, 1617 tokens. Safety score 100, quality 85, but process score drops to 62—that's the weak spot. No critical findings, but validation logic needs work. Never tested on models, no sandbox run. Supports most platforms including Claude and Cursor.
Install if you need a template for read-only analysis and can patch validation yourself. Without real-world test runs, risky to deploy as-is.
Plan and review read-only data warehouse analysis with explicit scope, privacy, provenance, and validation checks.
As a process C 62/100 · Has gaps — weak spots: result and completion, failures and branches, running it twice
The same skill appears in 1 more place: agentic-awesome-skills
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: 1. 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 62/100
- 0Result and completion. Does not say what the result is
- 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
- 50When it triggers. No condition that starts the skill
- 70Inputs and preconditions. Inputs and preconditions are listed
- 100Tools and files. No external tools needed
- 100Steps. 49 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1617 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
- +3Description length 114: 120–800 characters recommended
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +4Structure: 15 headings
- +3Step-by-step instructions: 49 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.