SKILLEMALL.ai

AD perf-tester

Performance and load testing for APIs and web services. Design test scenarios, generate k6/locust/JMeter scripts, analyze response times, throughput, error rates, and identify bottlenecks. Follows industry-standard methodology (RFC 6390, ISO 25010). Use when: (1) load/stress/spike/soak testing, (2) writing k6 or locust scripts, (3) analyzing performance metrics (P50/P95/P99, TPS, error rate), (4) capacity planning, (5) performance baseline comparison, (6) SLA validation, (7) "性能测试", "压测", "负载测试", "并发测试", "TPS", "响应时间", "k6脚本", "locust脚本", "JMeter". NOT for: code-level profiling (use profiler tools), infrastructure monitoring (use Prometheus/Grafana), or functional API testing (use api-tester).

ClawHub Agent Skills author: zhanghengyi1986-afk v1.0.0 MIT-0 4 files body ≈ 2 131 tokens Open the sourceclawhub.ai analyzed 5 d ago

As a process D 47/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches

IntegrationInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
99/100
safety, quality, tests
Safety 60%
100
Quality 40%
97
Run on models
none yet
Process rating
D
47/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
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: 4. 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 47/100

    • 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
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 100Steps. 9 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 2131 tokens

    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

    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +5Description quotes 2 example trigger phrases
    • +4Description says when NOT to use the skill
    • +3Description length 702: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 9 items
    • +4Has examples (9 code blocks)
    • +4Reference files are cited in the instructions (2 of 2)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 97.

    External checks

    ClawHub: clean
    This appears to be a performance-testing skill with expected k6 and Locust guidance, but users should only run its examples against systems they are authorized to test.
    LLM: benign (medium) · VirusTotal: · 29 May 2026