context-fundamentals
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
312 skills found
Foundational guidelines for context engineering: optimizing token budgets, attention mechanics, and system architecture for AI agents.
High-performance Solana meme coin trading for AI agents: sniping, MEV-protected execution, rug detection, and automated position management.
Comprehensive guide for scaffold, configure, and structure gitagent projects. Manage agent.yaml, SOUL.md, RULES.md, and project directory layouts.
A deterministic orchestration engine for autonomous coding agents, managing workflow loops, state persistence, and checkpoint-based execution.
Full-stack application orchestrator that analyzes natural language requests to determine tech stacks, scaffold projects, and coordinate specialized development agents.
Automated setup and configuration of Model Context Protocol (MCP) servers for Claude Code to enable seamless integration with external databases, APIs, and file systems.
A Git-backed memory store for agent skills. Download, version, edit, and share custom agent behaviors and procedural knowledge using a CLI.
Orchestrate complex workflows by coordinating multiple specialized AI agents for multi-perspective code analysis, feature implementation, and system-wide reviews.
Architect production-grade LLM applications using LangChain 1.x and LangGraph. Implement stateful AI agents, multi-step workflows, and custom memory systems for complex conversational and automation tasks.
Generate personalized, verified daily news briefings tailored to your interests, projects, and competitive landscape with strict 7-day source freshness.
Implement Linkerd service mesh patterns for security, traffic policy management, and zero-trust networking in Kubernetes environments.
Dialectical reasoning and adversarial coding agent for MCP-enabled editors, forcing LLMs to resolve internal contradictions for higher quality outputs.