AB power-law-distribution
Activate when: user is allocating capital or resources across a portfolio and wants to know where to concentrate; user says 'our average customer / deal / employee performs at X' and is making decisions from that average; user is building a risk model using standard deviation or VaR; user asks why a few customers or deals drive almost all revenue; user is evaluating VC fund returns or startup portfolio outcomes. Do NOT activate when: the distribution is demonstrably Gaussian (e.g., manufacturing tolerances under statistical process control); stakes are low enough that distribution shape does not affect the decision. More: deciqai.com/c/power-law-distribution
Activate when: user is allocating capital or resources across a portfolio and wants to know where to concentrate; user says 'our average customer / deal /…
As a process B 67/100 · Nearly there — weak spots: inputs and preconditions, failures and branches
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: 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 67/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 50When it triggers. No condition that starts the skill
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 23 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1927 tokens
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
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
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 666: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 23 items
- +3Output format is stated explicitly
- +4Has examples (1 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 95.