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Skills IA devops llm ai

Découvrez les skills devops llm ai, comparez leurs dépôts et consultez leurs instructions.

Catégories: devops llm ai

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GitHub

multi-ai-debugging

adaptationio

Systematic debugging using Claude, Gemini, and Codex as specialized agents. Multi-agent root cause analysis, log analysis, error classification, and auto-fix generation. Use when debugging production issues, an...

GitHub

auto-claude-memory

adaptationio

Auto-Claude Graphiti memory system configuration and usage. Use when setting up memory persistence, configuring LLM/embedding providers, querying knowledge graph, or optimizing memory performance.

GitHub

bedrock-agentcore-evaluations

adaptationio

Amazon Bedrock AgentCore Evaluations for testing and monitoring AI agent quality. 13 built-in evaluators plus custom LLM-as-Judge patterns. Use when testing agents, monitoring production quality, setting up ale...

GitHub

memory-manager

adaptationio

External persistent memory for cross-session knowledge. Use when storing error patterns, retrieving learned solutions, managing causal memory chains, or persisting project knowledge.

GitHub

ac-knowledge-graph

adaptationio

Manage knowledge graph for autonomous coding. Use when storing relationships, querying connected knowledge, building project understanding, or maintaining semantic memory.

GitHub

auto-claude-cli

adaptationio

Auto-Claude CLI command reference and usage patterns. Use when running specs, managing builds, checking status, or using CLI commands for autonomous coding tasks.

GitHub

compound-docs

Bind

Capture solved problems as searchable documentation with pattern detection. This skill auto-triggers when users confirm a fix worked ("that worked", "it's fixed", "working now") or manually via /compound comman...

GitHub

agents-md-generator

buYoung

Analyze repository structure and generate standardized AGENTS.md files that serve as contributor guides for AI agents. Measures LOC to determine character limits and produces 5-section documents covering overvi...

GitHub

skill-maker

buYoung

Create new AI agent skills following the SKILL.md guidelines. Generates complete skill structure including SKILL.md with YAML frontmatter, optional bundled resources (scripts/, references/, assets/), and update...

GitHub

ai-sdk-ui

constellos

This skill should be used when the user asks to "add AI chat", "implement streaming UI", "use useChat hook", "add AI completion", "implement useCompletion", "create conversational interface", "add streaming tex...

GitHub

code-recall

AbianS

Use semantic memory from code-recall MCP to remember decisions, patterns, and learnings across sessions. Call get_briefing at session start, check_rules before major changes, and store_observation after importa...

GitHub

authoring-stitch-prompts

joaquimscosta

Converts natural-language descriptions or UI spec files into optimized Google Stitch prompts. Use when creating, refining, or validating design directives for Google Stitch. Follows Stitch best practices with s...

GitHub

code-explanation

joaquimscosta

Explains complex code through clear narratives, visual diagrams, and step-by-step breakdowns. Use when user asks to explain code, understand algorithms, analyze design patterns, wants code walkthroughs, or ment...

GitHub

skill-name

joaquimscosta

What this skill does. Use when user mentions "keyword1", "keyword2", or "keyword3". Keep under 1,024 characters and include specific trigger keywords.

GitHub

deep-research

joaquimscosta

Deep research on technical topics using EXA tools with intelligent two-tier caching. Use when user asks to research a topic, investigate best practices, look up information, find patterns, or explore architectu...

GitHub

domain-driven-design

joaquimscosta

Expert guidance for Domain-Driven Design architecture and implementation. Use when designing complex business systems, defining bounded contexts, structuring domain models, choosing between modular monolith vs...

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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