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Skills IA Documentation

Découvrez les skills Documentation, comparez leurs dépôts et consultez leurs instructions.

Catégories: Documentation

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GitHub

coding-effectively

pbdeuchler

ALWAYS use this skill when writing or refactoring code. Includes context-dependent sub-skills to empower different coding styles across languages and runtimes.

GitHub

writing-good-tests

pbdeuchler

Use when writing or reviewing tests - covers test philosophy, condition-based waiting, mocking strategy, and test isolation

GitHub

howto-develop-with-postgres

pbdeuchler

Use when writing database access code, creating schemas, or managing transactions with PostgreSQL - enforces transaction safety with TX_ naming, read-write separation, type safety for UUIDs/JSONB, and snake_cas...

GitHub

reasoningbank-intelligence

adebold

Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cogn...

GitHub

agentdb-performance-optimization

adebold

Optimize AgentDB performance with quantization (4-32x memory reduction), HNSW indexing (150x faster search), caching, and batch operations. Use when optimizing memory usage, improving search speed, or scaling t...

GitHub

pair-programming

adebold

AI-assisted pair programming with multiple modes (driver/navigator/switch), real-time verification, quality monitoring, and comprehensive testing. Supports TDD, debugging, refactoring, and learning sessions. Fe...

GitHub

agentic-jujutsu

adebold

Quantum-resistant, self-learning version control for AI agents with ReasoningBank intelligence and multi-agent coordination

GitHub

agentdb-advanced-features

adebold

Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems,...

GitHub

swarm-orchestration

adebold

Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building d...

GitHub

hooks-automation

adebold

Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination,...

GitHub

swarm-advanced

adebold

Advanced swarm orchestration patterns for research, development, testing, and complex distributed workflows

GitHub

reasoningbank-with-agentdb

adebold

Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learnin...

GitHub

sparc-methodology

adebold

SPARC (Specification, Pseudocode, Architecture, Refinement, Completion) comprehensive development methodology with multi-agent orchestration

GitHub

stream-chain

adebold

Stream-JSON chaining for multi-agent pipelines, data transformation, and sequential workflows

GitHub

hive-mind-advanced

adebold

Advanced Hive Mind collective intelligence system for queen-led multi-agent coordination with consensus mechanisms and persistent memory

GitHub

agentdb-learning-plugins

adebold

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, impleme...

GitHub

skill-builder

adebold

Create new Claude Code Skills with proper YAML frontmatter, progressive disclosure structure, and complete directory organization. Use when you need to build custom skills for specific workflows, generate skill...

GitHub

agentdb-memory-patterns

adebold

Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intell...

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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