task-planning-shrimp
Plan, split, and track multi-step work using Shrimp Task Manager MCP (or equivalent). Use for multi-file refactors, migrations, or any work that benefits from task tracking.
Plan, split, and track multi-step work using Shrimp Task Manager MCP (or equivalent). Use for multi-file refactors, migrations, or any work that benefits from task tracking.
Memory system with three MCP tools: mcp__plugin_kg_kodegen__memory_list_libraries (discover namespaces), mcp__plugin_kg_kodegen__memory_memorize (store with embeddings), mcp__plugin_kg_kodegen__memory_recall (semantic search). Use for storing/retrieving knowledge by meaning. WHEN: User says "remember this", "recall", "what did I save", "my notes", "find my knowledge about". WHEN NOT: File operations (use fs_*), exact keyword search (use fs_search).
Generate a next-week training plan from weekly load + risk outputs (derived from Strava MCP data).
Provides Things3 task management workflows, organization patterns, and productivity methodologies. Includes personal taxonomy integration, priority systems, and automation patterns for effective task management. MANDATORY: Claude must read this skill file before using any Things3 MCP tools (read_tasks, create_task, edit_task, migrate_inbox_to_notion).
Enable AI-driven Todo management using natural language via MCP tools. Use when building conversational todo agents, implementing natural language task management, designing AI assistants for todo apps, or creating MCP-based tool integrations. Covers intent interpretation, safe tool invocation, and stateless agent design.
Use when user wants music recommendations based on their YouTube Music playlists, wants to analyze their music taste, asks about similar artists or songs, or wants to discover new music matching their style
Guide for effective use of che-ical-mcp for macOS Calendar & Reminders management. Use when user asks about calendar events, reminders, scheduling, or time management.
MCP-free Todoist integration with GTD coaching. Uses Python CLI (todoist) to query Todoist API v1 directly. Provides semantic understanding of the user's GTD structure (outcomes as sections, team vs personal, 3-tier ontology), query patterns, and outcome quality coaching. Also handles weekly review orchestration and pattern detection. Triggers on 'clean up outcomes', 'team priorities', 'Q4 review', 'is this a good outcome', 'weekly review', 'am I overcommitting', 'check my patterns', 'should I take this on', 'I said yes to', 'another meeting', 'they asked me to', 'scope creep', 'this grew into', 'freedom score', or ANY Todoist-related task. (user)
Structured file-based planning for Alexandria's complex multi-step tasks. Creates task_plan.md, findings.md, progress.md. REQUIRED for: >5 tool calls, database schema changes, queue architecture, external API integrations, performance optimization, data migration. DO NOT use mcp__pal__planner for file-based implementation tasks.
Complete MCP tool reference and usage patterns. Load this skill to become an expert in all 80+ GoodVibes MCP tools. Use when you need to understand tool capabilities, find the right tool for a task, or learn proper tool invocation patterns.
Update and maintain Memory Bank files (activeContext, progress, decisionLog). Triggers: MB, memory, 記憶, 進度, 更新記憶, update memory, 記錄進度, 更新上下文, sync, 同步, 記下來, note, 筆記, context, 脈絡, 追蹤, track, 狀態, status.
Comprehensive guide for ActivityWatch setup, configuration, watchers, integrations, API usage, and automation. Covers aw-qt, aw-watcher modules, aw-client libraries, aw-sync, data export, MCP server integration, and package managers. Use when working with ActivityWatch components, creating custom watchers, querying data, setting up sync, integrating with analytics dashboards, or using the ActivityWatch API.
MCP-free Todoist integration with GTD coaching. Uses Python CLI (scripts/todoist.py) to query Todoist API v1 directly. Provides semantic understanding of the user's GTD structure (outcomes as sections, team vs personal, 3-tier ontology), query patterns, and outcome quality coaching. Also handles weekly review orchestration and pattern detection. Triggers on 'clean up outcomes', 'team priorities', 'Q4 review', 'is this a good outcome', 'weekly review', 'am I overcommitting', 'check my patterns', 'should I take this on', 'I said yes to', 'another meeting', 'they asked me to', 'scope creep', 'this grew into', 'freedom score', or ANY Todoist-related task. (user)
Create, organize, and prioritize tasks from briefs or brain dumps. Sync with Taskmaster MCP. Use to plan work, track status, and pick next steps.
Diagnose and fix PayPal MCP server initialization issues by installing a custom runner script.
Comprehensive guide for integrating Stripe with Firebase, Next.js, React, and TypeScript. Use when working with Stripe payments, subscriptions, webhooks, or checkout. Covers webhook setup with Firebase Cloud Functions v2, Next.js App Router webhook handling, signature verification, common errors and solutions, Stripe CLI usage, and MCP integration. Essential for setting up Stripe correctly in Firebase+Next.js+TypeScript projects.
Manage GitHub Project board items - add issues, update status, move between columns. Use when user asks to add issues to board, change status, or organize the project.
Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
Manage Model Context Protocol (MCP) servers - discover, analyze, and execute tools/prompts/resources from configured MCP servers. Use when working with MCP integrations, need to discover available MCP capabilities, filter MCP tools for specific tasks, execute MCP tools programmatically, access MCP prompts/resources, or implement MCP client functionality. Supports intelligent tool selection, multi-server management, and context-efficient capability discovery.
Phase 2 of assist workflow - Determine appropriate component type (Skill, Agent, Command, Hook, MCP)