103 boosters for "owl" — open source, verified from GitHub, ready to install
This booster provides expert guidance for developing, debugging, and optimizing Azure AI Document Intelligence applications, covering architecture, security, best practices, and deployment patterns. Developers building document processing solutions on Azure will benefit from its comprehensive troubleshooting and design pattern knowledge.
Procedural memory for AI coding agents. Transforms scattered sessions into persistent, cross-agent memory. Uses a three-layer cognitive architecture that mirrors human expertise development. AI coding agents accumulate valuable knowledge but it's: You've solved auth bugs three times this month acros
"description": "A virtual design team for Claude Code, Cursor, Windsurf, Gemini CLI, and Copilot — powered by Naksha. Assembles specialist roles — UI designer, UX researcher, content designer, Figma expert, data viz, email, social, motion, presentation, brand strategy, illustration, video, conversat
Skyll enables agents to dynamically discover and retrieve skills at runtime. Instead of having all skills pre-loaded, agents can search for relevant skills on-demand and inject them into context. Get a specific skill: The API returns ranked skills with relevance scores (0-100):
Activate when the user says any of: If the user provides a name as an argument (e.g. ), skip Q1 in intake and use it directly as the slug. Enter evolution mode when:
This project is an MCP server that's built using Node.js and the TypeScript SDK for MCP. When working on this project, make sure to refer to the type definitions for the MCP TypeScript SDK: https://raw.githubusercontent.com/modelcontextprotocol/typescript-sdk/refs/heads/main/src/types.ts.
Query Google's AI Search mode to retrieve comprehensive, source-grounded answers from across the web. Trigger this skill when the user: 1. Include Current Year (2026) for up-to-date results
A fully autonomous AI research agent that ingests sources into Google NotebookLM, runs deep web research, synthesizes knowledge through cited Q&A and 9 downloadable artifact types, creates polished content drafts, and optionally publishes to social platforms.
Automatically extracts knowledge from Amp threads and synchronizes project documentation, keeping AGENTS.md and other docs up-to-date after epics and major work. Ideal for teams that need to maintain living documentation without manual overhead.
This command queries the BEADS knowledge base for facts relevant to your current context and injects them into the conversation, ensuring you: 1. Follow established patterns and rules 2. Avoid known gotchas and pitfalls
"name": "llm-wiki-compiler", "name": "llm-wiki-compiler", "source": "./plugin",
"name": "compound-knowledge-plugin", "url": "https://every.to" "description": "Workflows for knowledge work that compounds over time",
"name": "research-to-diagram", "description": "Deep research and auto-generate knowledge relationship diagrams in PDF format. From research to visualization in one tool.", "repository": "https://github.com/wshuyi/research-to-diagram",
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",
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:
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
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:
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.
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").
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 →
