agentic-workflows
Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.
Discover reusable agent skills, browse implementation details, and find the right skill for your workflow.
477 skills found
Build production-grade AI agents using LangGraph, Anthropic/OpenAI/vLLM, and structured outputs. Features streaming, A2A protocol, Pydantic validation, vector memory, and guardrails for resilient, multi-agent workflows.
Automate pull request reviews with structural code analysis, blast radius assessment, and dependency impact mapping.
Audit and synchronize the supported LLM model list in assets.py against the authoritative litellm registry.
Guidelines for testing HashQL code using compiletest (UI tests), unit tests, and insta snapshots. Includes commands for --bless, annotation syntax, and strategies for compiler components.
Comprehensive SEO and GEO optimization suite. Use to analyze domains, find keyword gaps, research backlinks, and generate autocomplete search suggestions using DataForSEO.
Write, structure, and maintain technical documentation like READMEs, API docs, runbooks, and architecture specs to keep your team aligned and informed.
Generate a structured academic paper outline from research narrative, experiment data, and review conclusions.
Automate the migration of Netflix Conductor workflows to Temporal Python, including server orchestration, worker management, and workflow troubleshooting.
A professional UI/UX design guide for web and mobile. Features 50+ styles, 99 UX guidelines, and 97 color palettes. Supports multi-stack recommendations for React, Vue, Next.js, and more.
Develop, test, sign, and publish governance plugins for Memoria using Rhai or gRPC runtimes. Manage the full plugin lifecycle from scaffolding to activation.
Generates UI components, hero sections, and feedback forms with integrated accessibility checks, leveraging specialized design references and quality gates.
Process massive files and large codebases (10M+ tokens) by recursively chunking, sub-querying, and aggregating results to overcome LLM context limits.