Claude Code AI Agent Skills

Professional equity research and trading analysis powered by Claude AI, delivering institutional-grade investment insights with Goldman Sachs-style formatting and comprehensive risk assessment. - Institutional-Grade Analysis: Generates professional equity research reports matching Wall Street standards - AI-Powered Intelligence: Leverages Claude AI for comprehensive fundamental and technical analy

Reddit automation Skills — directly uses your logged-in browser and real account, operating Reddit as an ordinary user. Supports OpenClaw and all AI Agent platforms compatible with the SKILL.md format (e.g. Claude Code). Supports chained operations — you can give compound natural-language instructions and the Agent will automatically chain multiple skills. For example: The Agent will execute: sear

Claude Code forgets everything between sessions. Built-in auto-memory exists but: - It's opaque (Claude decides what's "meaningful") - Limited to 200 lines loaded at startup - Not tightly integrated into the agentic loop - No hierarchical organization (scales poorly) A skill-based memory protocol with: - Hierarchical storage: core.md summaries → topics/ .md details - Background agents: Memory ops

Connect Claude Code to your Robinhood account. Analyze your portfolio, get real-time quotes, and execute trades—all through conversation. Then in Claude Code: 1. Type /mcp → select trayd → click Authorize 2. Sign in with Google 3. Say: "Link my Robinhood account" 4. Approve on your phone 5. Start trading! Instead of clicking through dozens of screens: Important to understand: - Your Robinhood emai

Use when the request is end-to-end, spans multiple workflow phases, or the correct tracking-workflow entry point is still unclear.

Point to browser DOM elements for agentic coding tools via MCP! MCP Pointer is a local tool combining an MCP Server with a Chrome Extension: 1. 🖥️ MCP Server (Node.js package) - Bridges between the browser and AI tools via the Model Context Protocol 2. 🌐 Chrome Extension - Captures DOM element selections in the browser using Option+Click The extension lets you visually select DOM elements in the

Multi-agent AI consultation framework for Claude Code via MCP. Get a second (and third) opinion from other LLMs when Claude Code alone isn't enough. Claude Code is powerful, but one brain can miss bugs, overlook edge cases, or get stuck in a local optimum. Critical decisions benefit from diverse perspectives. Concilium runs parallel consultations with multiple LLMs through standard MCP protocol. E

The semantic engine for MCP clients. Define metrics once, query from anywhere. Docs · Getting Started · Changelog · Discord · Bonnard is an agent-native semantic layer CLI. Deploy an MCP server and governed analytics API in minutes -for AI agents, BI tools, and data teams. Define metrics and dimensions in YAML, validate locally, and ship to production. Works with Snowfl

A next-generation framework documentation provider for Claude Code via Model Context Protocol (MCP). Provides query-focused API context by extracting TypeScript definitions directly from npm packages - delivering minimal, accurate information instead of dumping entire documentation pages. mcp-name: dev.augments/mcp Version 4.0 introduces a fundamentally new approach to framework documentation: Doc

Use when user provides a topic and wants an automated video podcast created, OR when user wants to learn/analyze video design patterns from reference videos — handles research, script writing, TTS audio synthesis, Remotion video creation, and final MP4 output with background music. Also supports design learning from reference videos (learn command), style profile management, and design reference library.

This skill should be used when the user says "why now", "is the timing right", "what's changed", "why didn't this exist before", "why is this possible now", "what's the tailwind", or needs to formalize whether there is a structural timing advantage for this idea — not just "AI is taking off.

Persistent memory for Claude Code. Also: loop detection, scope guards, code analysis, and a local LLM for second opinions. Claude Code forgets everything between sessions and after context compaction. Mini Claude provides: - Checkpoints - Save task state, auto-restore after compaction - Mistake memory - Log errors, get warned next time you touch that file - Decision memory - Log WHY you chose some

Claude Code forgets everything between sessions. CogmemAi fixes that. One command. Claude Code remembers your architecture, patterns, decisions, bugs, and preferences — permanently. That's it. The setup wizard verifies your API key, configures Claude Code, and you're ready. Start Claude Code by typing claude and your memories are ready. Don't have an API key yet? Get one free at hifriendbot.com/de

The memory system for AI coding agents. Decisions, lessons, and context that persist across sessions. mcp-name: io.github.omega-memory/omega-memory AI coding agents are stateless. Every new session starts from zero. - Context loss. Agents forget every decision, preference, and architectural choice between sessions. Developers spend 10-30 minutes per session re-explaining context that was already e

Stop pasting API keys into AI chat. When you paste sk-proj-xxx into Claude Code, that secret is now in your conversation history, Anthropic's logs, and potentially exposed to prompt injection attacks. secretctl fixes this. Your AI gets command results, never secret values. Every day, developers paste secrets into AI coding assistants: This is a security incident waiting to happen. - Secrets in con

Provides score interpretation (< 0.3 good, > 0.5 skip), query optimization, and source naming for query_documents, ingest_file, ingest_data tools. Use this skill when working with RAG, searching documents, ingesting files, saving web content, or handling PDF, HTML, DOCX, TXT, Markdown.

Free Local RAG for Claude Code - Save Tokens & Time 日本語版はこちら | Japanese Version DevRag is a lightweight RAG (Retrieval-Augmented Generation) system designed specifically for developers using Claude Code. Stop wasting tokens by reading entire documents - let vector search find exactly what you need. When using Claude Code, reading documents with the Read tool consumes massive amounts of tokens: - ❌

Headless IDA Pro binary analysis via Model Context Protocol. Go orchestrates multi-session concurrency while Python workers handle IDA operations. Key features: - Multi-session concurrency via process isolation - 52 MCP tools for binary analysis - Automatic session timeouts (4 hours default, configurable) - Paginated results with configurable limit (default 1000) - Il2CppDumper metadata import for

Manage WhatsApp channel access control — approve or deny pairings, add or remove users from the allowlist, set DM policy (pairing/allowlist/disabled), and configure group access. Use when the user asks to pair a contact, approve someone, check who's allowed, revoke access, lock down the channel, or change WhatsApp policy. Triggers on /whatsapp:access, "pair", "approve", "allowlist", "who can message", "lock down whatsapp".

AI驱动的综合健康分析系统，整合多维度健康数据、识别异常模式、预测健康风险、提供个性化建议。支持智能问答和AI健康报告生成。

Instead of manually checking the Rybbit dashboard, let your AI assistant query analytics data directly: - "How many users visited the site today?" - "Show me the top pages by bounce rate this week" - "What errors occurred in the last hour?" - "Show user journey from homepage to checkout" - "Compare browser usage between mobile and desktop" Or clone and build: Add to your ~/.claude/settings.json or

Arquitecto de Soluciones Principal y Consultor Tecnológico de Andru.ia. Diagnostica y traza la hoja de ruta óptima para proyectos de IA en español.

- Fixed reply threading — auto-resolves In-Reply-To and References headers so email replies land in the correct thread instead of creating orphaned messages (upstream PR 91, still pending) - Send-as alias support — optional from parameter for multi-identity email management (send from any configured Gmail alias) - Reply-all tool — replyall automatically fetches the original email, builds To/CC rec

A feedback loop for people building AI skills and MCP servers. You're building a skill, an MCP server, or a custom prompt strategy that's supposed to make an AI coding assistant better at a specific job. But how do you know it actually works? How do you know your latest commit made things better and not worse? Pitlane gives you the answer. Define the tasks your skill should help with, set up a bas