123 boosters for "owl" — open source, verified from GitHub, ready to install
A booster that helps developers optimize slow Python code through profiling, async/await patterns, and concurrent execution strategies. Ideal for Python developers dealing with performance bottlenecks who need guidance on measurement before optimization.
Elastic-Claude provides local search infrastructure to index and retrieve project knowledge, helping developers quickly find related prior work and context when starting new tasks or ingesting documents.
"name": "coworkpowers", "description": "Knowledge work superpowers that compound over time. Research, execute, review, and capture learnings to make each task easier than the last.", "name": "Nabeel Hyatt",
"name": "firecrawl-workflows", "displayName": "Firecrawl Workflows", "description": "Outcome-focused Firecrawl workflow skills for research, SEO audits, QA, knowledge bases, lead lists, dashboard reporting, shopping, and design-system extraction.",
"$schema": "https://anthropic.com/claude-code/marketplace.schema.json", "name": "opentrace-oss", "description": "Knowledge graph tools for exploring system architecture, code structure, and service dependencies",
"description": "🍰 Sugar - The autonomous layer for AI coding agents", "name": "Steven Leggett", "email": "contact@roboticforce.io"
31 specialized agents covering every department from solo founder Day 0 to IPO. 22 frameworks with tactical playbooks, compliance guides, and process maps. Before loading any agent files, consult . It contains:
You are tasked with retrieving relevant knowledge from the Obsidian vault using multi-layer semantic search. 1. First Layer - Initial Search: 2. Second Layer - Direct Associations:
Brain in the Fish evaluates documents (essays, policies, contracts, clinical reports, surveys) against evaluation criteria using a panel of AI agents. Each agent's mental state exists as OWL ontology. Scoring is grounded in an Evidence Density Scorer (EDS) that makes hallucination mathematically det
Ask an opening question to gauge where the user stands: Use these question types, escalating from simple to complex: When the user arrives at the answer, ask them to summarize:
Litmus spawns multiple OpenClaw subagents that experiment on your GPU overnight. Each runs on its own git branch in a shared lab repository — every experiment is a commit, agents can read each other's code, cherry-pick breakthroughs, and build on the global best at any time.
A Cursor rules-based research agent specialized in exploring repositories, documentation, and remote codebases using Nia MCP tools without modifying files. Ideal for developers conducting deep technical research, knowledge discovery, and cross-team information handoffs.
The AI doesn't have memory — it reads memory. OpenClaw injects workspace files into the system prompt at session start, giving the AI persistent context across sessions. Obsidian visualizes the knowledge graph. QMD provides semantic search so the AI finds relevant context without loading everything.
xAI/Grok-oriented meta prompt for the knowledge-graph construction (extraction) stage of the SEOCHO vector-vs-graph experiment. Authored for used as a plain chat completion (no reasoning/CoT scaffolding, no "thinking out loud").
Create production-ready AI skills by extracting domain expertise and system ontologies, ensuring reliable performance in real-world applications. Ideal for developers building AI assistants that need deep contextual knowledge.
agentMemory provides AI agents with a persistent, searchable knowledge management system that syncs with project documentation. Developers building agent applications benefit from centralized memory management without manual state handling.
VMware family entry point — AI-powered VM lifecycle, deployment, and alarm management — 34 MCP tools. vmware-aiops is the entry point. Add modules for additional capabilities: 1. Browse datastore for OVA images →
"name": "@aashari/mcp-server-atlassian-confluence", "description": "Node.js/TypeScript MCP server for Atlassian Confluence. Provides tools enabling AI systems (LLMs) to list/get spaces & pages (content formatted as Markdown) and search via CQL. Connects AI seamlessly to Confluence knowledge bases us
The captain's log for your codebase. Every decision, discovery, and change logged as you ship code. Use GitHub as a complete knowledge graph where every brainstorm, commit, review, and decision is traceable. This skill orchestrates existing skills and references; it defines when and how to invoke th
"description": "LLM-powered personal wiki — autonomous knowledge base with research-on-miss, ingestion, search, a browsable web UI, and universal data gravity. Saves and retrieves knowledge automatically whenever relevant.", "repository": "https://github.com/Oshayr/llm-wiki", "agents/backlink-manage
"name": "design-system-ops", "description": "Staff-level design system auditing, governance, documentation, validation, and communication — 39 skills, 4 agents, and 11 knowledge notes for the full design system lifecycle", "name": "Murphy Trueman"
Turn a folder of raw files into a Markdown vault that an LLM can grep, and then answer questions over that vault responsibly. source file, carrying retrieval frontmatter (abstract / tags / synonyms) + a
Laravel Hub Windsurf Rules is a comprehensive coding standard and architecture guide for Laravel developers using Windsurf, covering PHP 8.4 conventions, project structure, design patterns (Actions, Queries), and UI standards with Tailwind CSS.
"description": "Codebase vital signs — hotspot detection, ROI-ranked diagnosis, co-change coupling, knowledge risk, and AI provenance tracking", "name": "Tejas Chopra", "email": "chopratejas@gmail.com"
