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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

1,033–1,056 sur 5,502

data ai debugging Effacer les filtres

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GitHub

test-coverage

jmagly

Analyze test coverage, identify gaps, and recommend test improvements. Use when relevant to the task.

GitHub

sanity-groq

sanity-io

Expert assistance for writing, optimizing, and debugging GROQ queries. Use when writing queries or debugging performance.

GitHub

vercel-design-guidelines

Rich-Harris

Check web interfaces against Vercel's design guidelines. Use when asked to "review my UI", "check accessibility", "audit design", "review UX", "check my site against best practices", or "apply Vercel design gui...

GitHub

reading-logs

rileyhilliard

Analyzes logs efficiently through targeted search and iterative refinement. Use when investigating errors, debugging incidents, or analyzing patterns in application logs.

GitHub

optimizing-performance

rileyhilliard

Measure-first performance optimization that balances gains against complexity. Use when addressing slow code, profiling issues, or evaluating optimization trade-offs.

GitHub

systematic-debugging

rileyhilliard

Four-phase debugging framework that finds root causes before proposing fixes. Use when investigating bugs, errors, unexpected behavior, failed tests, or when previous fixes haven't worked.

GitHub

ui-preview

zainfathoni

Preview and screenshot local dev servers and storybooks. Use when asked to view UI components, take screenshots of storybooks, or inspect the web/server apps.

GitHub

jutsu-bunbun-runtime

TheBushidoCollective

Use when working with Bun's runtime APIs including file I/O, HTTP servers, and native APIs. Covers modern JavaScript/TypeScript execution in Bun's fast runtime environment.

GitHub

react-native-web-performance

TheBushidoCollective

Use when optimizing React Native Web performance. Provides patterns for code splitting, bundle optimization, memoization, and web-specific performance improvements.

GitHub

ruby-metaprogramming

TheBushidoCollective

Use when working with Ruby metaprogramming features including dynamic method definition, method_missing, class_eval, define_method, and reflection.

GitHub

elixir-pattern-matching

TheBushidoCollective

Use when Elixir pattern matching including function clauses, case statements, with statements, and destructuring. Use for elegant control flow.

GitHub

performance-optimization

TheBushidoCollective

Use when optimizing code for performance, reducing bundle size, improving load times, or fixing performance bottlenecks. Emphasizes measurement-driven optimization.

GitHub

debugging

TheBushidoCollective

Use when investigating bugs, diagnosing issues, or understanding unexpected behavior. Provides systematic approaches to finding root causes.

GitHub

graphql-performance

TheBushidoCollective

Use when optimizing GraphQL API performance with query complexity analysis, batching, caching strategies, depth limiting, monitoring, and database optimization.

GitHub

c-memory-management

TheBushidoCollective

Use when managing memory in C programs with malloc/free, pointers, and avoiding common memory safety pitfalls.

GitHub

discover-formal

rand

Automatically discover formal methods and verification skills when working with formal methods. Activates for formal development tasks.

GitHub

discover-plt

rand

Automatically discover programming language theory skills when working with compilers. Activates for plt development tasks.

GitHub

discover-zig

rand

Automatically discover Zig programming skills when working with Zig. Activates for zig development tasks.

GitHub

discover-observability

rand

Automatically discover observability and monitoring skills when working with monitoring. Activates for observability development tasks.

GitHub

discover-ir

rand

Automatically discover intermediate representation and compiler skills when working with intermediate representation. Activates for ir development tasks.

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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