Skill UI

Automates Signwell tasks through Rube MCP using Composio toolkits, guiding users to discover current schemas, confirm active connections, and run workflows with schema-compliant arguments, memory placeholders, pagination handling, and session reuse.

Automates Supportbee workflows via Composio’s Supportbee toolkit and Rube MCP, guiding users through tool discovery, connection validation, schema-compliant arguments, and memory-aware multi-tool execution before running operations.

Provides comprehensive observability for the TwinMind AI meeting system. This skill allows users to monitor critical performance metrics, including transcription quality (using Ear-3 model), meeting coverage, action item extraction accuracy, and memory vault health. It is essential for implementing robust, enterprise-grade AI workflows and managing the operational status of advanced meeting AI applications.

Automates Waboxapp operations through Composio’s Rube MCP by guiding the search for current tool schemas, verifying toolkit connections, and executing schema-compliant workflows with consistent session and memory handling.

Coordinate Wati operations through Composio’s Rube MCP, always fetching the latest tool schemas, validating active connections, and executing schema-compliant workflows with proper session and memory handling.

Orchestrates Yelp workflows via Rube MCP and Composio by walking through tool discovery, connection validation, schema-compliant execution, memory/session practices, and pagination-aware multi-tool calls.

Tracks approved board meeting decisions using a two-layer memory system, storing raw transcripts and founder-approved outcomes, with CLI switches to review decisions, overdue actions, owner filters, and conflict alerts.

This system functions as a persistent, disk-based memory for complex workflows. It structures and tracks multi-step projects and research by utilizing three key Markdown files: task_plan.md (main plan), findings.md (research discoveries), and progress.md (session log). It ensures that critical context, progress state, and key findings are never lost to the limited context window, making it ideal for long-term, structured work.

A structured system for managing complex, multi-step tasks and research projects by using persistent Markdown files as external memory. It enforces systematic planning, progress tracking, and critical finding logging across sessions, ensuring that core knowledge is never lost in the ephemeral context window. Ideal for engineering workflows, deep research, and multi-stage development.

Coordinates ingestion, sync, deduplication, and retrieval across layered knowledge stores, guiding when to activate the knowledge base, how to classify and store updates, and how to mirror conversations, workspace state, and release truth between repos, memory servers, and external stores.

This system helps organize and track progress for complex, multi-stage projects, research tasks, or deep work involving over five tool calls. By utilizing persistent Markdown files (task_plan.md, findings.md, progress.md) as an external memory, it ensures that critical context, plans, and findings are never lost, even across session resets. It enforces structured planning and systematic process updates.

This meta-skill acts as a comprehensive Learning Science Coach, teaching users *how* to learn effectively rather than just *what* to study. It draws upon advanced cognitive science principles—including Spaced Repetition, Active Recall, Metacognition, and Bloom's Taxonomy—to diagnose learning gaps, optimize study methods, and build structured, long-term memory retention systems. Ideal for students, professionals, and lifelong learners seeking systematic methods to accelerate knowledge acquisition.