GitHub
deep-research
Deep technical research using Firecrawl Agent for autonomous web investigation, competitive analysis, and implementation pattern discovery.
Skills IA research llm ai
Découvrez les skills research llm ai, comparez leurs dépôts et consultez leurs instructions.
Catégories: research llm ai
research llm ai Effacer les filtres
Explorer
GitHub
openmemory
Persistent long-term agent memory for storing and querying past work, patterns, and learnings.
GitHub
evaluation
Agent evaluation methods including LLM-as-judge, rubrics, and quality gates.
GitHub
ai-ml-infra
KubeAI, GPU operators, and model serving patterns for AI/ML infrastructure on Kubernetes.
GitHub
ralph-technique
The Ralph Wiggum technique for minimal, declarative prompts that enable loop-based autonomous execution.
GitHub
skill-authoring
Meta-skill for creating and improving agent skills. Use when designing new skills, refining existing skills, or understanding skill architecture patterns. Provides progressive disclosure, context efficiency pri...
GitHub
bun-llm-docs
Fetch Bun runtime documentation via llms.txt for up-to-date API references
GitHub
project-development
Methodology for identifying LLM-suited tasks, designing pipelines, and iterating with agent-assisted development.
GitHub
linear-agent-api
Linear Agent API expertise - sessions, activities, signals, and best practices. Use when working with Linear agent integration, verifying dialogue population, or implementing two-way communication.
GitHub
llm-docs
Fetch LLM-optimized documentation from llms.txt endpoints for up-to-date API references
GitHub
mcp-development
Guide for creating MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Use when building MCP servers, integrating external APIs, implementing tool servers, or creating agen...
GitHub
context-degradation
Recognize and mitigate context failures including lost-in-middle, context poisoning, distraction, confusion, and clash.
GitHub
context-fundamentals
Foundational understanding of context engineering for AI agent systems - context windows, attention mechanics, progressive disclosure, and context budgeting
GitHub
tool-design
Design effective tools for AI agents - tool descriptions, consolidation principles, architectural reduction, MCP naming conventions, and error handling
GitHub
frontend-design
Create distinctive, production-grade frontend interfaces with high design quality. Use when building UI components, pages, or applications to avoid generic AI aesthetics.
GitHub
mcp-manager
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
GitHub
meme-generation
Generate relevant memes using the memegen.link API. Use this skill when users request memes, want to add humor to content, or need visual aids for social media. Supports hundreds of popular meme templates with...
GitHub
persona-creation
Master the art of creating effective AI agent personas for Jules. Learn how to design role-specific prompts that guide Jules to execute tasks with precision, following best practices, and maintaining project st...
GitHub
jules-api
Delegate asynchronous coding tasks to Jules (Google's AI coding agent) to maximize efficiency. Use for code reviews, refactoring, adding tests, bug fixes, and documentation. Proactively suggest Jules delegation...
GitHub
task-authoring
Task Authoring skill for the ikigai project
GitHub
errors
Error Handling skill for the ikigai project
GitHub
meta
Meta - Agent System infrastructure for the ikigai project
GitHub
create-ai-tool
Create AI chat tools with the Vercel AI SDK for structured data access and actions. Use when adding tools for the AI assistant to call databases, APIs, or perform calculations.
GitHub
mastering-python-skill
Modern Python coaching covering language foundations through advanced production patterns. Use when asked to "write Python code", "explain Python concepts", "set up a Python project", "configure Poetry or PDM",...
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.