BF afrexai-performance-engineering
Complete performance engineering system — profiling, optimization, load testing, capacity planning, and performance culture. Use when diagnosing slow applications, optimizing code/queries/infrastructure, load testing before launch, planning capacity, or building performance into CI/CD. Covers Node.js, Python, Go, Java, databases, APIs, and frontend.
Complete performance engineering system — profiling, optimization, load testing, capacity planning, and performance culture.
As a process F 31/100 · Will not run — weak spots: steps, result and completion, when it triggers
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 7222 tokens (recommended < 5000); move details to references/
Process rating: all ten parameters 31/100
- 0Steps. Prose only: no discrete steps
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 70Execution cost. Instruction body is 7222 tokens
- 100Consistency. Name and required fields are in place
- 100Running it twice. No mutating operations
- 100Progress reporting. Reports progress
- low 12 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)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -2localhost URLs: will not work for another user
- +1No license
- +2Single-language instructions
- +3Description length 351: enough signal without eating the budget
- +4Structure: 47 headings
- +4Has examples (32 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.