# Log Analysis

> Use this skill when you need to analyze logs into evidence, timelines, anomalies, and follow-up hypotheses; triggers include log analysis.

- Skill: `naodeng/log-analysis` (Agent Skill, multi-file: 7 files)
- Install (CLI): `npx skillmds add naodeng/log-analysis`
- Raw SKILL.md: https://api.skillmd.com/api/skills/naodeng/log-analysis/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: naodeng (https://skillmd.com/u/naodeng)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/naodeng/log-analysis

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# Log Analysis

## When to Use

- Use this skill when you need to reconstruct events from structured and unstructured logs, detect anomalies, and form testable causal hypotheses.
- 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/log-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/log-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

- do not treat a log line as root cause
- redact sensitive data
- state timezone, sampling, and missing-log limitations
- 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: timeline, error clustering, context correlation, frequency shifts, sensitive data, logging gaps, cross-service consistency, 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.