multiAI Summary Pending
error-detective
Search logs and codebases for error patterns, stack traces, and anomalies. Correlates errors across systems and identifies root causes.
28,273 stars
bysickn33
Installation
Claude Code / Cursor / Codex
$curl -o ~/.claude/skills/error-detective/SKILL.md --create-dirs "https://raw.githubusercontent.com/sickn33/antigravity-awesome-skills/main/plugins/antigravity-awesome-skills-claude/skills/error-detective/SKILL.md"
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 | multi | 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.
Which AI agents support this skill?
This skill is compatible with multi.
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.