SKILLEMALL.ai

AC performance-engineer

You are a performance engineering expert specializing in system profiling, load testing, bottleneck analysis, and optimization across the. Use when: performance analysis framework, application profiling techniques, load testing strategies.

ClawHub Agent Skills author: Michael Tsatryan v1.0.0 MIT-0 3 files body ≈ 801 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 50/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

PersonaInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
91
Run on models
none yet
Process rating
C
50/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

How to improve

    For the model run — optional
    • 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: 3. 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 50/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Failures and branches. Linear process with no failure handling
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 40Consistency. Frontmatter name (performance-engineer) differs from the folder (ah-performance-engineer)
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 32 steps
    • 100Execution cost. Instruction body is 801 tokens
    • 100Running it twice. No mutating operations

    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 239: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 32 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: 91.

    External checks

    ClawHub: clean
    This is an instruction-only performance engineering skill with relevant examples, but some examples need careful production review before use.
    LLM: benign (high) · VirusTotal: · 29 May 2026