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Skills IA data ai debugging

Découvrez les skills data ai debugging, comparez leurs dépôts et consultez leurs instructions.

Catégories: data ai debugging

2,593–2,616 sur 5,502

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Explorer

GitHub

systematic-debugging

jrc1883

Four-phase debugging: root cause → patterns → hypothesis → implement. For complex bugs, test failures, multi-component issues. NOT for obvious syntax errors.

GitHub

bug-reporter

jrc1883

Capture bug context, generate reports, and optionally create GitHub issues or share patterns

GitHub

feasibility-analysis

flight505

Project feasibility analysis toolkit. Evaluate technical feasibility, resource requirements, market viability, risk factors, and implementation challenges for informed go/no-go decisions on software projects.

GitHub

building-blocks

flight505

Component specification toolkit for breaking software projects into discrete, buildable blocks. Create detailed specifications with interfaces, dependencies, test criteria, and effort estimates for Claude Code...

GitHub

cheatsheets

Integrum-Global

Quick reference cheatsheets for Kailash SDK patterns, nodes, workflows, and best practices. Use when asking about 'quick tips', 'cheat sheet', 'quick reference', 'common mistakes', 'node selection', 'workflow p...

GitHub

go-code-review

curtbushko

Auto-review Go code for 100+ common mistakes when analyzing .go files, discussing Go patterns, or reviewing PRs with Go code. Checks error handling, concurrency, interfaces, performance, testing, and stdlib usa...

GitHub

bug-triage

dasien

Systematically reproduce, diagnose, and analyze bugs to determine root cause, assess severity, and plan fix strategy

GitHub

structured-logging

phrazzld

Apply structured logging best practices using Pino for Node.js applications: JSON output, log levels, context, redaction, correlation IDs, and centralization. Use when implementing logging, reviewing log statem...

GitHub

cartographer

phrazzld

Codebase mapping and documentation using parallel AI subagents. Invoke for: map this codebase, document architecture, understand codebase, onboarding to new project, create CODEBASE_MAP.md, generate architectur...

GitHub

swift-performance

fusengine

Optimize SwiftUI and Swift app performance with Instruments profiling, lazy loading, memory management, and view optimization. Use when diagnosing slow UI, fixing memory leaks, optimizing scroll performance, or...

GitHub

research

fusengine

Technical research methodology using Context7, Exa, and Sequential Thinking for documentation, best practices, and complex investigations.

GitHub

exploration

fusengine

Codebase exploration techniques for rapid discovery, architecture analysis, pattern detection, and dependency mapping.

GitHub

react-performance

fusengine

React performance optimization - memo, useMemo, useCallback, lazy loading, virtualization. Use when optimizing React app performance.

GitHub

code-quality

fusengine

Code quality validation with linters, SOLID principles, error detection, and architecture compliance across all languages.

GitHub

evaluator

wtthornton

Evaluate TappsCodingAgents framework effectiveness and provide continuous improvement recommendations. Use for analyzing usage patterns, workflow adherence, and code quality metrics.

GitHub

debugger

wtthornton

Investigate and fix bugs. Use when debugging errors, analyzing stack traces, or tracing code execution. Includes Context7 error pattern knowledge and library documentation lookup.

GitHub

reviewer

wtthornton

Code reviewer providing objective quality metrics, security analysis, and actionable feedback. Use for code reviews with scoring, linting, type checking, and duplication detection.

GitHub

improver

wtthornton

Refactor and improve code quality. Use for code refactoring, performance optimization, and quality improvements. Includes Context7 refactoring patterns lookup.

GitHub

skill-analyzer

KyleKing

Analyzes codebases to identify missing skills and recommend new ones. Invoke when user wants to audit skills, identify knowledge gaps, or determine what skills would benefit the project.

Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.

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