GitHub
coder-memory-recall
Retrieve universal coding patterns from vector database using true two-stage retrieval. Auto-invokes before complex tasks or when user says "--recall". Searches relevant role collections based on task context.
Skills IA testing security llm ai
Découvrez les skills testing security llm ai, comparez leurs dépôts et consultez leurs instructions.
Catégories: testing security llm ai
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GitHub
coder-memory-store
Store universal coding patterns and insights into vector database. Use after completing difficult tasks with broadly-applicable lessons that work across ANY project. Use when user says "--coder-store" or "--lea...
GitHub
coder-memory-store
Store universal coding patterns into vector database. Auto-invokes after difficult tasks with broadly-applicable lessons. Trigger with "--store" or when user expresses frustration (strong learning signals). Use...
GitHub
rails-service-patterns
Rails service object patterns and business logic organization. Automatically invoked when working with service objects, extracting business logic, implementing command/query patterns, or organizing app/services...
GitHub
prompt-engineering
Designing effective prompts - system/user prompts, few-shot learning, chain-of-thought, defensive prompting, injection defense. Use when crafting prompts, improving outputs, or securing AI applications.
GitHub
executing-plans
AI agent follows implementation plans systematically with progress tracking, quality gates, and blocker resolution. Use when implementing features, following plans, or executing tasks.
GitHub
building-with-supabase
AI agent builds full-stack applications with Supabase PostgreSQL, authentication, Row Level Security, Edge Functions, and real-time subscriptions. Use when building apps with Supabase, implementing RLS policies...
GitHub
dispatching-parallel-agents
AI agent orchestrates parallel task execution through concurrent specialized agents for maximum efficiency. Use when tasks can run independently, multi-concern reviews, or parallel exploration.
GitHub
developing-mcp-servers
Creates Model Context Protocol servers with tools, resources, and prompts for AI assistant integration. Use when extending Claude with custom capabilities, building AI tool integrations, or exposing APIs to AI...
GitHub
model-deployment
Model deployment strategies including serving infrastructure, containerization, model packaging, versioning, and production deployment patterns.
GitHub
designing-database-schemas
AI agent designs production-grade database schemas with proper normalization, indexing strategies, and data modeling patterns. Use when creating new databases, designing tables, modeling relationships, or revie...
GitHub
design-system-context
Automatic design system context injection for UI consistency
GitHub
receiving-code-reviews
AI agent handles code review feedback professionally by categorizing, responding appropriately, and extracting learnings. Use when PR feedback arrives, addressing reviewer comments, or improving from feedback.
GitHub
optimizing-tokens
AI agent maximizes efficiency and minimizes costs through strategic token usage while maintaining output quality. Use when managing AI interactions, designing prompts, or reducing costs.
GitHub
implementing-defense-in-depth
AI agent designs layered security architecture with multiple independent protective barriers ensuring no single point of failure. Use when building security systems, reviewing architecture, or hardening applica...
GitHub
ml-serving-optimization
ML serving optimization techniques including batching, caching, model compilation, and latency reduction for production ML systems.
GitHub
dataset-engineering
Building and processing datasets - data quality, curation, deduplication, synthesis, annotation, formatting. Use when creating training data, improving data quality, or generating synthetic data.
GitHub
user-feedback
Collecting and using user feedback - explicit/implicit signals, feedback analysis, improvement loops, A/B testing. Use when improving AI systems, understanding user satisfaction, or iterating on quality.
GitHub
requesting-code-reviews
AI agent requests effective code reviews through proper preparation, clear descriptions, and appropriate reviewer selection. Use when creating PRs, requesting feedback, or submitting for review.
GitHub
verifying-before-completion
AI agent validates work through evidence-based verification with comprehensive checklists, automated validation, and proof-of-work documentation. Use when completing tasks, preparing for review, or ensuring qua...
GitHub
ai-engineering
Building production AI applications with Foundation Models. Covers prompt engineering, RAG, agents, finetuning, evaluation, and deployment. Use when working with LLMs, building AI features, or architecting AI s...
GitHub
inference-optimization
Optimizing AI inference - quantization, speculative decoding, KV cache, batching, caching strategies. Use when reducing latency, lowering costs, or scaling AI serving.
GitHub
coding-with-omega-leverage
Applies AI-first development patterns for 10-100x productivity through specification-driven generation and leverage multiplication. Use when developing features with AI assistance or optimizing development work...
GitHub
debugging-systematically
AI agent follows a 5-phase debugging process with reproduction, isolation, hypothesis testing, and root cause resolution. Use when investigating bugs, troubleshooting issues, or hunting errors.
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.