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AC observability-designer

Design production-ready observability strategies combining metrics, logs, and traces. Includes SLI/SLO design, golden-signals monitoring, alert optimization. Use when adding observability to a new service, refactoring alerting that is too noisy, or designing an SLO program before scaling production load.

alirezarezvani/claude-skills Agent Skills author: alirezarezvani MIT 13 files body ≈ 3 381 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Design production-ready observability strategies combining metrics, logs, and traces.

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

ProcedureInfrastructureData 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
93/100
safety, quality, tests
Safety 60%
99
Quality 40%
83
Run on models
none yet
Process rating
C
50/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
When it triggers w 12
20
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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Exfiltration exfil-webhook-url assets/sample_alerts.json:258
      Webhook / callback URL commonly used for exfiltration (verify the destination) (test fixture / example file; quoted — discussed, not commanded)
      "api_url": "https://hooks.slack.com/services/warnings",
      fixturequoted

    Files scanned: 13. 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

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 10 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 100Steps. 140 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3381 tokens
    • 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

    • +5Description has no quoted example phrases that should trigger the skill
    • +4Description does not say when NOT to use the skill (false activations)
    • +3Output format is not stated: the model decides each time
    • -43 reference files, but SKILL.md never points to them: the model will not open them
    • +1No license
    • +2Single-language instructions
    • +3Description length 305: enough signal without eating the budget
    • +4Structure: 50 headings
    • +3Step-by-step instructions: 140 items
    • +4Has examples (1 code blocks)
    • +3All 3 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.