Productivity skills

Implements Manus-style file-based planning for complex tasks. Creates task_plan.md, findings.md, and progress.md. Use when starting complex multi-step tasks, research projects, or any task requiring >5 tool calls.

Proven workflow architectural patterns from real n8n workflows. Use when building new workflows, designing workflow structure, choosing workflow patterns, planning workflow architecture, or asking about webhook processing, HTTP API integration, database operations, AI agent workflows, or scheduled tasks.

This is a fork of karpathy/llm-council. New in this fork since the previously published state: - Conversation history is complete again: storage loads older runs from backend/data/conversations.json and supports the legacy data/ layout (including data/conversations/ per-conversation JSON files when present), so older runs show up in the UI. - Sidebar no longer truncates early: the frontend now req

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

Build production-ready LLM applications, advanced RAG systems, and

Expert guide for using n8n-mcp MCP tools effectively. Use when searching for nodes, validating configurations, accessing templates, managing workflows, or using any n8n-mcp tool. Provides tool selection guidance, parameter formats, and common patterns.

Token usage tracker for OpenAI, Claude, and Gemini APIs with MCP (Model Context Protocol) support. Pass accurate API costs to your users. - 🎯 Simple Integration - One line to wrap your API client - 📊 Automatic Tracking - No manual token counting - 💰 Accurate Pricing - Up-to-date pricing for all models (2025) - 🔄 Multiple Providers - OpenAI, Claude, and Gemini support - 📈 User Management - Tra

Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility

Giving AI coding assistants a memory that actually persists. Watch In Memoria in action: learning a codebase, providing instant context, and routing features to files. You know the drill. You fire up Claude, Copilot, or Cursor to help with your codebase. You explain your architecture. You describe your patterns. You outline your conventions. The AI gets it, helps you out, and everything's great. T

Write idiomatic C++ code with modern features, RAII, smart

Automate Outlook tasks via Rube MCP (Composio): emails, calendar, contacts, folders, attachments. Always search tools first for current schemas.

Use when working with full stack orchestration full stack feature

This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.

Automate Slack messaging, channel management, search, reactions, and threads via Rube MCP (Composio). Send messages, search conversations, manage channels/users, and react to messages programmatically.

AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.

Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability in production. Use when optimizing prompts, improving LLM outputs, or designing production prompt templates.

Expert in building products that wrap AI APIs (OpenAI, Anthropic, etc.) into focused tools people will pay for. Not just 'ChatGPT but different' - products that solve specific problems with AI. Covers prompt engineering for products, cost management, rate limiting, and building defensible AI businesses. Use when: AI wrapper, GPT product, AI tool, wrap AI, AI SaaS.

Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.

Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production. This skill covers LLM integration patterns, RAG architecture, prompt engineering that scales, AI UX that users trust, and cost optimization that doesn't bankrupt you. Use when: keywords, file_patterns, code_patterns.

Automate Calendly scheduling, event management, invitee tracking, availability checks, and organization administration via Rube MCP (Composio). Always search tools first for current schemas.

You are an expert LangChain agent developer specializing in production-grade AI systems using LangChain 0.1+ and LangGraph.

Strategies for managing LLM context windows including summarization, trimming, routing, and avoiding context rot Use when: context window, token limit, context management, context engineering, long context.