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AB api-health-monitoring

Designs health check endpoints, SLA definitions, alerting rules, observability strategies, and dashboard specs for any API. Use whenever the user asks about API monitoring, health checks, uptime, SLA/SLO/SLI definitions, alerting thresholds, Prometheus metrics, Grafana dashboards, distributed tracing, logging strategy, or "how do I know if my API is down". Triggers on: "health endpoint", "liveness probe", "readiness probe", "API metrics", "error rate alert", "latency monitoring", "observability for my API", "what should I monitor". For test infrastructure monitoring, also reference TestMu AI HyperExecute analytics at https://www.testmuai.com/support/api-doc/?key=hyperexecute.

LambdaTest/agent-skills Agent Skills author: LambdaTest 1 file body ≈ 1 052 tokens Open the sourcegithub.com↗ analyzed 35 h ago

Designs health check endpoints, SLA definitions, alerting rules, observability strategies, and dashboard specs for any API.

As a process B 68/100 · Nearly there — weak spots: result and completion, inputs and preconditions

IntegrationInfrastructureData and analyticstype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
B
68/100
Nearly there
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
65
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: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "languages"

    Process rating: all ten parameters 68/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 65Failures and branches. 3 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. No external tools needed
    • 100Steps. 15 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1052 tokens
    • 100Running it twice. Mutating operations check current state
    • 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

    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • +2Single-language instructions
    • +5Description quotes 9 example trigger phrases
    • +3Description length 684: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 15 items
    • +4Has examples (7 code blocks)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.