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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

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blazemeter-integrations

Blazemeter

Comprehensive guide for BlazeMeter Integrations, including APM tools, CI/CD pipelines, and development tools. Use when working with integrations for (1) Integrating APM tools (AppDynamics, Datadog, New Relic, C...

GitHub

blazemeter-service-virtualization

Blazemeter

Comprehensive guide for BlazeMeter Service Virtualization, including virtual services, transactions, templates, and management. Use when working with Service Virtualization for (1) Creating virtual services and...

GitHub

blazemeter-getting-started

Blazemeter

Getting started guides for BlazeMeter, including onboarding, migration guides, continuous testing journey, glossary, and mobile testing. Use when getting started with BlazeMeter for (1) Navigating BlazeMeter Un...

GitHub

osgrep-reference

tdimino

Comprehensive CLI reference and search strategies for osgrep semantic code search. Use for detailed CLI options, index management commands, search strategy guidance (architectural vs targeted queries), and trou...

GitHub

review

tessro

Thorough code review skill for checking your work before completion. Use this after implementing a feature or fix to verify code quality, completeness, testing, and documentation.

GitHub

debugging

CaptainCrouton89

Systematic debugging methodologies for hard bugs. Covers rubber ducking, code tracing, hypothesis testing, logging strategies. Use when stuck on bugs that resist quick fixes.

GitHub

python-code-review

existential-birds

Reviews Python code for type safety, async patterns, error handling, and common mistakes. Use when reviewing .py files, checking type hints, async/await usage, or exception handling.

GitHub

go-code-review

existential-birds

Reviews Go code for idiomatic patterns, error handling, concurrency safety, and common mistakes. Use when reviewing .go files, checking error handling, goroutine usage, or interface design.

GitHub

react-flow-code-review

existential-birds

Reviews React Flow code for anti-patterns, performance issues, and best practices. Use when reviewing code that uses @xyflow/react, checking for common mistakes, or optimizing node-based UI implementations.

GitHub

shadcn-code-review

existential-birds

Reviews shadcn/ui components for CVA patterns, composition with asChild, accessibility states, and data-slot usage. Use when reviewing React components using shadcn/ui, Radix primitives, or Tailwind styling.

GitHub

watchos-code-review

existential-birds

Reviews watchOS code for app lifecycle, complications (ClockKit/WidgetKit), WatchConnectivity, and performance constraints. Use when reviewing code with import WatchKit, WKExtension, WKApplicationDelegate, WCSe...

GitHub

deepagents-code-review

existential-birds

Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configurat...

GitHub

receive-feedback

existential-birds

Process external code review feedback with technical rigor. Use when receiving feedback from another LLM, human reviewer, or CI tool. Verifies claims before implementing, tracks disposition.

GitHub

12-factor-apps

existential-birds

Perform 12-Factor App compliance analysis on any codebase. Use when evaluating application architecture, auditing SaaS applications, or reviewing cloud-native applications against the original 12-Factor methodo...

GitHub

llm-artifacts-detection

existential-birds

Detects common LLM coding agent artifacts in codebases. Identifies test quality issues, dead code, over-abstraction, and verbose LLM style patterns. Use when cleaning up AI-generated code or reviewing for agent...

GitHub

swift-code-review

existential-birds

Reviews Swift code for concurrency safety, error handling, memory management, and common mistakes. Use when reviewing .swift files for async/await patterns, actor isolation, Sendable conformance, or general Swi...

GitHub

prometheus-go-code-review

existential-birds

Reviews Prometheus instrumentation in Go code for proper metric types, labels, and patterns. Use when reviewing code with prometheus/client_golang metrics.

GitHub

react-router-code-review

existential-birds

Reviews React Router code for proper data loading, mutations, error handling, and navigation patterns. Use when reviewing React Router v6.4+ code, loaders, actions, or navigation logic.

GitHub

llm-judge

existential-birds

LLM-as-judge methodology for comparing code implementations across repositories. Scores implementations on functionality, security, test quality, overengineering, and dead code using weighted rubrics. Used by /...

GitHub

langgraph-code-review

existential-birds

Reviews LangGraph code for bugs, anti-patterns, and improvements. Use when reviewing code that uses StateGraph, nodes, edges, checkpointing, or other LangGraph features. Catches common mistakes in state managem...

GitHub

combine-code-review

existential-birds

Reviews Combine framework code for memory leaks, operator misuse, and error handling. Use when reviewing code with import Combine, AnyPublisher, @Published, PassthroughSubject, or CurrentValueSubject.

GitHub

postgres-code-review

existential-birds

Reviews PostgreSQL code for indexing strategies, JSONB operations, connection pooling, and transaction safety. Use when reviewing SQL queries, database schemas, JSONB usage, or connection management.

GitHub

bubbletea-code-review

existential-birds

Reviews BubbleTea TUI code for proper Elm architecture, model/update/view patterns, and Lipgloss styling. Use when reviewing terminal UI code using charmbracelet/bubbletea.

GitHub

native-module-helper

armanzeroeight

Create custom React Native native modules for iOS and Android. Use when integrating native SDKs, optimizing performance-critical code, or accessing platform-specific APIs. Trigger words include "native module",...

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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