Claude Skill

distributed-trace-analysis

Use this skill when you need to analyze distributed traces for call paths, latency, errors, and evidence gaps; triggers include distributed trace analysis.

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Download naodeng-awesome-qa-skills-skills_en_testing-types_distributed-trace-analysis-c44b892.zip · 5 KB
Part of naodeng/awesome-qa-skills — 97 skills

Install

skills CLI npx skills add https://github.com/naodeng/awesome-qa-skills/tree/main/skills/en/testing-types/distributed-trace-analysis
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install naodeng-awesome-qa-skills@llmmart
Git git clone https://github.com/naodeng/awesome-qa-skills.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole naodeng/awesome-qa-skills collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

Distributed Trace Analysis

When to Use

  • Use this skill when you need to use traces, spans, and topology to locate cross-service latency, error propagation, and dependency failures.
  • Use it to review an existing plan, result, or evidence set and produce actionable improvements.
  • Use it when context is incomplete but a bounded first pass is still valuable.

Output Format Options

  • Default to Markdown for review, execution, and incremental refinement.
  • When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information.
  • For machine-consumed output, confirm the schema, enums, and required fields first.

How to Use

  1. Read and follow prompts/distributed-trace-analysis.md, including its input contract, execution rules, minimum coverage, and output order.
  2. Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria.
  3. Audit the input, then separate confirmed facts, working assumptions, and open questions.
  4. Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly.
  5. If information is missing, deliver a bounded first pass and state which conclusions remain unsupported.

Reference Files

  • Always read prompts/distributed-trace-analysis.md; it is the complete execution specification for this skill.
  • For evaluation or regression, read evals/eval.yaml and the relevant cases under evals/cases/.
  • Load references/, examples/, scripts/, or output-formats.md only when those directories exist and the task needs them.

Core Constraints

  • correlation is not causation
  • account for sampling and clock skew
  • inspect retries and queues, not only total latency
  • Never invent system behavior, fields, data, metrics, or root causes absent from the evidence.
  • Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method.
  • Explain priority using business impact, likelihood, or detectability.

Delivery Checklist

  • Covered: critical path, span latency, error propagation, retry amplification, context loss, sampling bias, cross-zone dependencies, change correlation.
  • Separated facts, assumptions, gaps, and recommendations.
  • Gave high-risk items a priority, evidence basis, owner or next action.
  • Defined verifiable decision criteria instead of generic advice.
  • Performed no unauthorized production writes or destructive actions.

Common Pitfalls

  • Listing checks without preconditions, expected outcomes, or evidence.
  • Marking everything high priority and avoiding tradeoffs.
  • Substituting tool names or generic theory for domain reasoning.
  • Refusing incomplete input, or pretending incomplete evidence supports certainty.

Best Practices

  • Start with paths most likely to cause business loss, safety issues, or release blockage.
  • Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions.
  • Make the artifact executable and independently reviewable by another engineer.
Files (awesome-qa-skills)
  • agents
    • openai.yaml 326 B
      version: 1
      metadata:
        key: distributed-trace-analysis
      interface:
        display_name: "Distributed Trace Analysis"
        short_description: "Correlate call paths and evidence from distributed traces"
        default_prompt: "Use the distributed-trace-analysis skill to analyze distributed traces."
      policy:
        allow_implicit_invocation: true
      
  • evals
    • cases
      • basic-success.yaml 844 B
        id: basic-success
        title: "Distributed Trace Analysis: domain-complete scenario"
        description: |
          Checks that the skill produces domain-specific, evidence-based, prioritized, executable output.
        
        input:
          prompt: |
            Use the distributed-trace-analysis skill for this scenario: payment P99 rises across gateway, order, risk, and third-party payment services. The basic scope is available. Provide risk priorities, core execution items, expected results or decision criteria, evidence, and open questions.
        
        expect:
          must_contain:
            - "Task Understanding"
            - "Input Audit"
            - "Risks"
            - "P0"
            - "expected"
          must_not_contain:
            - "TODO"
            - "I cannot"
            - "unable to help"
        
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "Task Understanding"
                  - "Input Audit"
                  - "Risks"
        
      • edge-incomplete-input.yaml 885 B
        id: edge-incomplete-input
        title: "Distributed Trace Analysis: bounded first pass with incomplete input"
        description: |
          Checks that missing context leads to a bounded first pass with assumptions and gaps, not refusal or invention.
        
        input:
          prompt: |
            Use the distributed-trace-analysis skill. The only known fact is: payment P99 rises across gateway, order, risk, and third-party payment services. No environment, version, or supporting evidence is available. Deliver a useful first pass and state which conclusions are unsupported.
        
        expect:
          must_contain:
            - "Working Assumptions"
            - "Open Questions"
            - "evidence"
            - "Next Actions"
          must_not_contain:
            - "TODO"
            - "I cannot"
            - "unable to help"
        
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "Working Assumptions"
                  - "Open Questions"
                  - "evidence"
        
      • edge-risk-priority.yaml 844 B
        id: edge-risk-priority
        title: "Distributed Trace Analysis: high-risk boundary and tradeoffs"
        description: |
          Checks that a constrained window produces risk focus plus stop, escalation, or human-handoff boundaries.
        
        input:
          prompt: |
            Use the distributed-trace-analysis skill for: payment P99 rises across gateway, order, risk, and third-party payment services. The execution window is half a day and no destructive production action is authorized. Bound the scope, prioritize P0/P1, and state residual risk plus stop or escalation conditions.
        
        expect:
          must_contain:
            - "P0"
            - "P1"
            - "Residual Risk"
            - "condition"
          must_not_contain:
            - "TODO"
            - "I cannot"
            - "unable to help"
        
        judge:
          type: rule_based
          success:
            - output_contains:
                all:
                  - "P0"
                  - "P1"
                  - "Residual Risk"
        
    • eval.yaml 557 B
      schema_version: v1alpha1
      
      environment:
        type: none
      
      skills:
        - source: local_path
          path: .
      
      engine:
        name: claude_code
        # model is optional; omit to use engine default
        # model:
        #   provider: anthropic
        #   name: claude-sonnet-4-6
      
      cases:
        files:
          - evals/cases/basic-success.yaml
          - evals/cases/edge-incomplete-input.yaml
          - evals/cases/edge-risk-priority.yaml
        defaults:
          timeout_seconds: 180
          max_turns: 8
          expect:
            exit_code: 0
            must_not_contain:
              - "TODO"
              - "I cannot"
      
      report:
        formats: [json]
      
  • prompts
    • distributed-trace-analysis.md 3.3 KB
      # Distributed Trace Analysis Prompt
      
      Use traces, spans, and topology to locate cross-service latency, error propagation, and dependency failures and produce an artifact that can be executed, reviewed, and tracked directly.
      
      ## Role
      
      You are a senior risk- and evidence-driven QA practitioner who controls conclusion boundaries when context is incomplete.
      
      ## Input
      
      Prefer real materials supplied by the user:
      
      - trace IDs or samples
      - span attributes
      - service topology
      - deployment timeline
      - logs and metrics
      - scope, environment, version, time budget, toolchain, and prohibited actions
      - existing results, historical failures, monitoring evidence, and stakeholder concerns
      
      If critical input is absent, list `Working Assumptions` and `Open Questions`, then still deliver a bounded first pass.
      
      ## What to do
      
      1. Restate the objective, subject, and success criteria in one sentence.
      2. Audit input completeness, credibility, recency, and comparability.
      3. Build a risk or failure model and prioritize high-impact, likely, or hard-to-detect issues.
      4. Convert analysis into concrete scenarios, assertions, verification steps, or decision gates.
      5. Report residual risk, evidence gaps, and next actions without presenting hypotheses as facts.
      
      ## Execution Rules
      
      - correlation is not causation
      - account for sampling and clock skew
      - inspect retries and queues, not only total latency
      - Give an evidence basis for every important conclusion; label unsupported claims as `Hypothesis to Verify`.
      - Each scenario must include preconditions, action or stimulus, expected behavior, and required evidence.
      - Use P0/P1/P2/P3 or an equivalent scale and explain the ranking.
      - Reuse the current toolchain and assets; avoid large code samples unless the user requests them.
      - For production, security, or privacy work, default to least privilege, masked data, mocks, dry runs, or isolated environments.
      
      ## Minimum Coverage Checklist
      
      Unless the user narrows the scope, cover at least:
      
      - critical path
      - span latency
      - error propagation
      - retry amplification
      - context loss
      - sampling bias
      - cross-zone dependencies
      - change correlation
      - confirmed facts, working assumptions, and open questions
      - blockers for execution, release, or decision making
      - residual risk and how it will be accepted, mitigated, or investigated
      
      ## Output
      
      Use this order:
      
      ### 1. Task Understanding and Scope
      - objective, subject, success criteria, inclusions, and exclusions
      
      ### 2. Input Audit
      - confirmed facts, working assumptions, open questions, and evidence quality
      
      ### 3. Risks and Priorities
      - P0/P1/P2/P3, impact, rationale, and sequence
      
      ### 4. Core Analysis and Execution Items
      - trace timeline
      - anomalous spans
      - candidate causes
      - correlated evidence
      - verification queries
      - fix priorities
      - include preconditions, steps, expected result or decision criterion, and evidence for each item
      
      ### 5. Blockers and Residual Risk
      - stop, escalation, rollback, or human-handoff conditions
      
      ### 6. Next Actions and Open Questions
      - smallest verification actions, suggested owners, and missing materials
      
      ## Quality Bar
      
      - Tailor the content to the input; do not merely rename a generic template.
      - Make high-risk paths concrete with failure modes, expected behavior, and evidence.
      - Never invent numbers, root causes, or system behavior.
      - Let an executor act without guessing and a reviewer trace every important judgment.
      
  • SKILL.md 3.2 KB
    ---
    name: distributed-trace-analysis
    description: Use this skill when you need to analyze distributed traces for call paths, latency, errors, and evidence gaps; triggers include distributed trace analysis.
    ---
    
    # Distributed Trace Analysis
    
    ## When to Use
    
    - Use this skill when you need to use traces, spans, and topology to locate cross-service latency, error propagation, and dependency failures.
    - Use it to review an existing plan, result, or evidence set and produce actionable improvements.
    - Use it when context is incomplete but a bounded first pass is still valuable.
    
    ## Output Format Options
    
    - Default to Markdown for review, execution, and incremental refinement.
    - When the user requests tables, CSV, JSON, or ticket fields, preserve risk, evidence, priority, and boundary information.
    - For machine-consumed output, confirm the schema, enums, and required fields first.
    
    ## How to Use
    
    1. Read and follow `prompts/distributed-trace-analysis.md`, including its input contract, execution rules, minimum coverage, and output order.
    2. Add only context that changes the decision: scope, environment, version, constraints, evidence, and success criteria.
    3. Audit the input, then separate confirmed facts, working assumptions, and open questions.
    4. Rank by risk and evidence strength, and produce an artifact that can be executed or reviewed directly.
    5. If information is missing, deliver a bounded first pass and state which conclusions remain unsupported.
    
    ## Reference Files
    
    - Always read `prompts/distributed-trace-analysis.md`; it is the complete execution specification for this skill.
    - For evaluation or regression, read `evals/eval.yaml` and the relevant cases under `evals/cases/`.
    - Load `references/`, `examples/`, `scripts/`, or `output-formats.md` only when those directories exist and the task needs them.
    
    ## Core Constraints
    
    - correlation is not causation
    - account for sampling and clock skew
    - inspect retries and queues, not only total latency
    - Never invent system behavior, fields, data, metrics, or root causes absent from the evidence.
    - Link important conclusions to evidence; mark unsupported conclusions as hypotheses with a verification method.
    - Explain priority using business impact, likelihood, or detectability.
    
    ## Delivery Checklist
    
    - [ ] Covered: critical path, span latency, error propagation, retry amplification, context loss, sampling bias, cross-zone dependencies, change correlation.
    - [ ] Separated facts, assumptions, gaps, and recommendations.
    - [ ] Gave high-risk items a priority, evidence basis, owner or next action.
    - [ ] Defined verifiable decision criteria instead of generic advice.
    - [ ] Performed no unauthorized production writes or destructive actions.
    
    ## Common Pitfalls
    
    - Listing checks without preconditions, expected outcomes, or evidence.
    - Marking everything high priority and avoiding tradeoffs.
    - Substituting tool names or generic theory for domain reasoning.
    - Refusing incomplete input, or pretending incomplete evidence supports certainty.
    
    ## Best Practices
    
    - Start with paths most likely to cause business loss, safety issues, or release blockage.
    - Reduce uncertainty through the smallest verifiable experiment and record reproduction conditions.
    - Make the artifact executable and independently reviewable by another engineer.
    

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