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Agent skill

Error Detector

by majiayu000 in majiayu000/claude-skill-registry

Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors

MITAuto-check passedDevelopment

Install Error Detector

skills CLI
$ npx skills add majiayu000/claude-skill-registry --skill error-detector -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install majiayu000/claude-skill-registry error-detector --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/majiayu000/claude-skill-registry.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/analysis/error-detector-skill-404kidwiz-claude-supercode-ski .claude/skills/error-detector && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
error-detector
GitHub stars
666
Used in
1 other repo
Token cost
~2.7k tokens
SKILL.md length
1,111 words
Files
2
Skills in repo
971
Repo updated
First seen
Licence
MIT

At a glance

Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors

  • Works in 4 steps: Error Tracking: Sentry integration… → Log Aggregation: ELK stack for… → Alerting: PagerDuty integration for… → …
  • Development work in your project
  • SKILL.md covers Purpose, When to Use, Overview and Error Detection Methodologies, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Error Detector is an agent skill from majiayu000/claude-skill-registry. Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors

Its SKILL.md is about 2.7k tokens, which your agent loads only when the skill is triggered. The skill folder holds 1 other file (for example `metadata.json`).

It sits in Development. The repository describes itself as: Searchable Claude Code skills catalog with source-linked guides and generated registry artifacts. The licence is MIT.

When your agent uses it

  • Development work in your project

Example prompts

  • “/error-detector”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  1. Error Tracking: Sentry integration across all services
  2. Log Aggregation: ELK stack for centralized log management
  3. Alerting: PagerDuty integration for critical errors
  4. Dashboard: Custom Grafana dashboards for error metrics

What it can do on your machine

Read from SKILL.md and the folder at commit 000116a. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are bash).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Error Detector loads about 2.7k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 1,111 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~2.7k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from majiayu000/claude-skill-registry at commit 000116a, republished under its MIT licence (© majiayu000). 1,111 words, ~2,651 tokens.

Download SKILL.mdSave it as .claude/skills/error-detector/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
error-detector
description
Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors

Error Detector Skill

Purpose

Provides error analysis and pattern detection expertise specializing in proactive identification of software defects, code analysis, and system behavior monitoring. Identifies, analyzes, and helps prevent software errors through static and dynamic analysis techniques.

When to Use

  • Performing static code analysis and anti-pattern detection
  • Analyzing runtime errors and exception patterns
  • Detecting memory leaks and performance bottlenecks
  • Monitoring and analyzing error logs
  • Identifying security vulnerabilities through code patterns
  • Conducting proactive error prevention analysis

Overview

Specialized in error analysis, pattern detection, and proactive identification of software defects through code analysis, log monitoring, and system behavior analysis.

Error Detection Methodologies

Static Analysis
  • Code pattern recognition
  • Anti-pattern identification
  • Complexity analysis
  • Security vulnerability detection
  • Performance bottleneck identification
Dynamic Analysis
  • Runtime error monitoring
  • Exception pattern analysis
  • Memory leak detection
  • Performance profiling
  • Resource utilization tracking
Log-Based Analysis
bash
# Example patterns for error detection
grep -r "ERROR\|FATAL\|CRITICAL" logs/ --include="*.log" --include="*.txt"
grep -r "exception\|error\|failed" src/ --include="*.js" --include="*.py" --include="*.java"
grep -r "TODO\|FIXME\|HACK" src/ --include="*.*" --exclude-dir=node_modules

Error Categories & Patterns

Common Programming Errors
  • Null pointer exceptions
  • Array index out of bounds
  • Type conversion errors
  • Resource leak issues
  • Concurrency problems
Logic Errors
  • Off-by-one errors
  • Incorrect conditionals
  • Loop termination issues
  • State management problems
  • Data validation failures
Performance Errors
  • Inefficient algorithms
  • Memory optimization issues
  • Database query problems
  • Network timeout handling
  • Resource contention

Advanced Detection Techniques

Machine Learning-Based Detection
  • Anomaly detection in system behavior
  • Pattern recognition in error logs
  • Predictive failure modeling
  • Classification of error types
  • Automated root cause analysis
Statistical Analysis
  • Error frequency distribution
  • Time series analysis of failures
  • Correlation analysis between components
  • Regression testing failure patterns
  • Performance degradation detection
Code Complexity Metrics
  • Cyclomatic complexity analysis
  • Cognitive complexity assessment
  • Maintainability index calculation
  • Technical debt quantification
  • Code duplication detection

Error Analysis Frameworks

Root Cause Analysis (RCA)
  • Five Whys methodology
  • Fishbone diagram analysis
  • Pareto analysis for prioritization
  • Fault tree analysis
  • Change impact assessment
Error Classification Systems
  • Severity categorization
  • Priority assignment frameworks
  • Impact assessment matrices
  • Frequency-based prioritization
  • Business risk evaluation
Pattern Recognition
  • Repetitive error identification
  • Error clustering algorithms
  • Sequence pattern analysis
  • Correlation detection
  • Temporal pattern analysis

Monitoring & Alerting

Real-Time Monitoring
  • System health dashboards
  • Error rate monitoring
  • Performance threshold alerts
  • Log aggregation and analysis
  • Automated incident response
Predictive Analysis
  • Failure prediction models
  • Early warning systems
  • Trend analysis and forecasting
  • Capacity planning alerts
  • Proactive maintenance scheduling
Logging Best Practices
  • Structured logging implementation
  • Log level optimization
  • Sensitive data protection
  • Log rotation policies
  • Centralized log management

Error Prevention Strategies

Code Quality Improvement
  • Peer review processes
  • Automated testing coverage
  • Static analysis tools integration
  • Code style enforcement
  • Documentation standards
Development Process Optimization
  • Test-driven development (TDD)
  • Continuous integration practices
  • Automated deployment pipelines
  • Rollback procedures
  • Feature flag implementation
System Design Patterns
  • Circuit breaker patterns
  • Retry mechanisms
  • Graceful degradation
  • Fallback systems
  • Redundancy implementation

Error Detection Tools & Integration

Static Analysis Tools
  • ESLint for JavaScript/TypeScript
  • Pylint for Python
  • SonarQube for multi-language analysis
  • Checkstyle for Java
  • FxCop for C#
Dynamic Monitoring Tools
  • Application Performance Monitoring (APM)
  • Error tracking services (Sentry, Bugsnag)
  • Log management systems (ELK stack)
  • Distributed tracing tools
  • Infrastructure monitoring
Custom Detection Scripts
  • Error pattern matching
  • Anomaly detection algorithms
  • Automated regression testing
  • Performance benchmarking
  • Data validation checks

Error Response & Resolution

Incident Management
  • Error triage procedures
  • Escalation protocols
  • Communication templates
  • Resolution tracking
  • Post-incident reviews
Automated Recovery
  • Self-healing mechanisms
  • Automatic restart procedures
  • Failover systems
  • Data recovery processes
  • Service restoration workflows
Knowledge Management
  • Error documentation databases
  • Solution repositories
  • Best practice libraries
  • Training materials
  • Lessons learned archives

Specific Domain Expertise

Web Application Errors
  • HTTP error code analysis
  • JavaScript runtime errors
  • API failure patterns
  • Database connection issues
  • Frontend performance problems
Mobile Application Errors
  • Device-specific issues
  • Network connectivity problems
  • App store rejection patterns
  • Battery usage optimization
  • Memory management issues
Backend System Errors
  • Database transaction failures
  • Message queue processing errors
  • Authentication and authorization issues
  • Microservices communication problems
  • Resource exhaustion scenarios

Reporting & Analytics

Error Metrics
  • Mean Time To Detection (MTTD)
  • Mean Time To Resolution (MTTR)
  • Error frequency trends
  • Resolution effectiveness
  • Preventive action impact
Quality Dashboards
  • Real-time error monitoring
  • Historical trend analysis
  • Team performance metrics
  • System health indicators
  • Compliance status tracking

Deliverables

Analysis Reports
  • Comprehensive error analysis
  • Root cause identification
  • Impact assessment documentation
  • Resolution recommendations
  • Prevention strategies
Implementation Plans
  • Error detection system design
  • Monitoring setup procedures
  • Alerting configuration guides
  • Automated testing frameworks
  • Process improvement recommendations
Training Materials
  • Error handling best practices
  • Troubleshooting guides
  • Tool usage documentation
  • Process workflow diagrams
  • Knowledge base articles
Show full SKILL.md (439 more words)Show less

Examples

Example 1: E-Commerce Platform Error Monitoring

Scenario: Implementing comprehensive error tracking for a high-traffic e-commerce site.

Implementation:

  1. Error Tracking: Sentry integration across all services
  2. Log Aggregation: ELK stack for centralized log management
  3. Alerting: PagerDuty integration for critical errors
  4. Dashboard: Custom Grafana dashboards for error metrics

Results:

  • MTTD reduced from hours to minutes
  • 40% reduction in time-to-resolution
  • Proactive identification of emerging issues
Example 2: Mobile App Crash Reporting

Scenario: Setting up crash reporting for iOS and Android applications.

Approach:

  1. Crash Reporting: Firebase Crashlytics integration
  2. Symbolication: Automated dSYM upload for readable stack traces
  3. Breadcrumbs: User action tracking for context
  4. Release Tracking: Correlation of crashes with app versions

Key Metrics Tracked:

  • Crash-free users rate (target: 99.5%)
  • Top crashers by device and OS version
  • Session data with crash-free rate trends
  • User feedback correlation with crashes
Example 3: API Gateway Error Analysis

Scenario: Monitoring and analyzing errors at API gateway level for a SaaS platform.

Monitoring Setup:

  1. Request Logging: All API requests logged with status codes
  2. Rate Tracking: Monitoring for 429 Too Many Requests patterns
  3. Latency Analysis: P95, P99 latency tracking by endpoint
  4. Authentication Errors: Tracking failed auth attempts for security

Alert Configuration:

  • Error rate spikes (> 5% for 5 minutes)
  • Latency degradation (> 1s for P95)
  • Authentication failures (> 100/min from single IP)
  • Circuit breaker state changes

Best Practices

Error Detection Configuration
  • Comprehensive Coverage: Instrument all code paths, not just critical functions
  • Context-Rich Data: Include user IDs, request IDs, environment details
  • Sensitive Data Handling: Scrub PII and secrets before error reporting
  • Sampling Strategy: Balance detail collection with performance impact
  • Tagging: Use consistent tagging for filtering and aggregation
Alert Management
  • Threshold Tuning: Adjust sensitivity to reduce alert fatigue
  • Escalation Paths: Clear procedures for different severity levels
  • Business Hours: Different expectations for on-call vs. business hours
  • Alert Fatigue Prevention: Consolidate related alerts, avoid duplicates
  • On-Call Rotation: Sustainable schedules with clear responsibilities
Metrics and Reporting
  • Key Metrics: Track MTTD, MTTR, error rate, resolution rate
  • Trend Analysis: Weekly/monthly comparisons to identify patterns
  • SLA Reporting: Error impact on service level agreements
  • Team Dashboards: Custom views for different teams and roles
  • Executive Reporting: High-level summaries for leadership
Error Handling Best Practices
  • Defensive Programming: Validate inputs, handle edge cases
  • Graceful Degradation: Fallback mechanisms when dependencies fail
  • Error Recovery: Automatic retry with exponential backoff
  • User Communication: Meaningful error messages for end users
  • Logging: Comprehensive logs for debugging and audit trails
Continuous Improvement
  • Post-Incident Reviews: Learn from every significant error
  • Pattern Analysis: Identify recurring issues for systemic fixes
  • Knowledge Base: Document errors and solutions for future reference
  • Tool Evolution: Regularly evaluate and update detection tools
  • Team Training: Ensure consistent error handling practices

© majiayu000, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 1 other file in skills/analysis/error-detector-skill-404kidwiz-claude-supercode-ski of majiayu000/claude-skill-registry.

  • SKILL.md
  • metadata.json

Open the folder on GitHubat commit 000116a

Used in 1 other repository

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in majiayu000/claude-skill-registry, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Error Detector next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Categories

Questions about Error Detector

What does Error Detector do?

Advanced error analysis and pattern detection specialist for identifying, analyzing, and preventing software errors. Error Detector is an agent skill from majiayu000/claude-skill-registry.

When should I use Error Detector?

Error Detector fits situations like: development work in your project.

How do I install Error Detector in Claude Code?

Run `npx skills add majiayu000/claude-skill-registry --skill error-detector -a claude-code`. Or copy the skill folder (skills/analysis/error-detector-skill-404kidwiz-claude-supercode-ski in majiayu000/claude-skill-registry) into .claude/skills/error-detector in your project. Claude Code loads it when a task matches its description.

How do I install Error Detector in Codex?

Run `npx skills add majiayu000/claude-skill-registry --skill error-detector -a codex`. Or copy the skill folder (skills/analysis/error-detector-skill-404kidwiz-claude-supercode-ski in majiayu000/claude-skill-registry) into .agents/skills/error-detector in your project. Codex loads it when a task matches its description.

Can I use Error Detector in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add majiayu000/claude-skill-registry --skill error-detector -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/error-detector, .gemini/skills/error-detector, .github/skills/error-detector and .opencode/skills/error-detector in your project.

What does Error Detector need to run?

SKILL.md names no scripts, command-line tools or credentials: Error Detector is instructions for the agent only. Our summary lists: Python 3.

Does Error Detector access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Error Detector safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Error Detector use?

Error Detector is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Error Detector use?

About 2.7k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Error Detector?

Skills that share tags, products or a category with Error Detector: Diagram Design (cathrynlavery/diagram-design, 44k stars), R Function Input Validation (tidyverse/dplyr, 5.1k stars), Archify (molvqingtai/WebChat, 2.6k stars) and JSON Processing with jq (charmbracelet/crush, 29k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Error Detector?

majiayu000 (a GitHub user) maintains it in majiayu000/claude-skill-registry, which has 666 GitHub stars. The repository holds 971 skills in this directory. The repository was last updated on October 7, 2026.

Source: majiayu000/claude-skill-registry on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.