Agent Skills Hub

Hive Agent Architecture

Framework for building, registering, and orchestrating Model Context Protocol (MCP) tools and AI agent workflows within the Hive native Rust architecture.

openrouter-trending-models

Fetch and analyze current trending programming models from OpenRouter. Ideal for selecting models for reviews, optimizing AI costs, and staying updated on AI coding trends with real-time pricing and context window data.

strategy-scaling-architecture

Architectural planning and scaling for spectre-build, covering GUI, server layers, multi-model support, and industrial pipeline orchestration.

personal-brand-agent

Autonomous multi-agent LinkedIn system using LangGraph and Claude Opus 4.5 for trend research, content creation, voice profiling, and analytics-driven optimization.

zotero-mcp-code

Efficiently search your Zotero library using Python code execution. Enables comprehensive multi-strategy queries, automated deduplication, and relevance ranking without context overflow or system crashes.

karpathy-guidelines

Behavioral guidelines for LLMs to reduce coding mistakes, follow best practices, and improve output quality by enforcing simplicity, surgical changes, and goal-driven verification.

research-tool

Advanced web search and reasoning tool for OpenClaw agents. Features citation-heavy synthesis, multi-step reasoning, and live internet access via OpenRouter.

testops-companion

An AI-powered TestOps platform and MCP server providing automated failure analysis, RCA matching, and intelligent test orchestration for CI/CD pipelines.

Extract structured data from unstructured files (PDF, PPTX, DOCX...)

Implement LlamaExtract for robust structured data extraction from PDF, DOCX, and PPTX files using Pydantic schemas.

trulens-evaluation-workflow

A systematic workflow to instrument, evaluate, and monitor LLM applications using TruLens, supporting frameworks like LangChain, LangGraph, and LlamaIndex.

oracle

CLI tool to bundle repository context, files, and prompts into a one-shot request for advanced AI debugging, refactoring, and code review.

context-degradation

Diagnose, isolate, and mitigate LLM context failures like lost-in-middle, poisoning, distraction, and context clash to improve agent reliability.