AB qsr-labor-leak-auditor
Real-time labor decision support for restaurant and franchise operators with summary-first mobile-optimized output. All V3 capabilities — surfaced events, state control, goal tracking, recovery planning, forward planning, event-aware comparisons — plus executive-summary-first formatting, math hidden by default, standardized output structure, and concise correction handling. Designed for fast mobile operator use on the shift floor. Built by a franchise GM with 16 years in QSR operations.
As a process B 73/100 · Nearly there — weak spots: inputs and preconditions, running it twice
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 6298 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 73/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 12 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Result and completion. Output format stated, no completion criterion
- 70Execution cost. Instruction body is 6298 tokens
- 100Tools and files. No external tools needed
- 100Steps. 97 steps
- 100Failures and branches. 7 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 25 top-level sections: this looks like several domains in one skill
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)
- -228 emoji in the instructions: noise for the model
- +2Single-language instructions
- +3Description length 491: enough signal without eating the budget
- +4Structure: 54 headings
- +3Step-by-step instructions: 97 items
- +3Output format is stated explicitly
- +4Has examples (4 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.