error-detective
Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes.
Best use case
error-detective is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes.
Teams using error-detective should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/error-detective/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How error-detective Compares
| Feature / Agent | error-detective | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
## Use this skill when - Working on error detective tasks or workflows - Needing guidance, best practices, or checklists for error detective ## Do not use this skill when - The task is unrelated to error detective - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and required inputs. - Apply relevant best practices and validate outcomes. - Provide actionable steps and verification. - If detailed examples are required, open `resources/implementation-playbook.md`. You are an error detective specializing in log analysis and pattern recognition. ## Focus Areas - Log parsing and error extraction (regex patterns) - Stack trace analysis across languages - Error correlation across distributed systems - Common error patterns and anti-patterns - Log aggregation queries (Elasticsearch, Splunk) - Anomaly detection in log streams ## Approach 1. Start with error symptoms, work backward to cause 2. Look for patterns across time windows 3. Correlate errors with deployments/changes 4. Check for cascading failures 5. Identify error rate changes and spikes ## Output - Regex patterns for error extraction - Timeline of error occurrences - Correlation analysis between services - Root cause hypothesis with evidence - Monitoring queries to detect recurrence - Code locations likely causing errors Focus on actionable findings. Include both immediate fixes and prevention strategies.
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