Find the perfect capability for your agent.
Calculate training costs for Tinker fine-tuning jobs. Use when estimating costs for Tinker LLM training, counting tokens in datasets, or comparing Tinker model training prices. Tokenizes datasets using the correct model tokenizer and provides accurate cost estimates.
Composable patterns for advanced skill usage including skill chaining, telemetry instrumentation, visual cataloging, and skill versioning. Use when building complex multi-skill workflows, adding observability to skills, visualizing skill relationships, or managing skill evolution.
Guidelines for using Context7 as the primary source of truth for documentation and standards. Trigger when the user asks about documentation, standards, or when gathering context for a task.
Perform AI-powered web searches with real-time information using Perplexity models via LiteLLM and OpenRouter. This skill should be used when conducting web searches for current information, finding recent scientific literature, getting grounded answers with source citations, or accessing information beyond the model knowledge cutoff. Provides access to multiple Perplexity models including Sonar Pro, Sonar Pro Search (advanced agentic search), and Sonar Reasoning Pro through a single OpenRouter API key.
AI-powered web search, research, and reasoning via Perplexity
Generate a skeleton template for a new Claude Code Skill.
Manages long-term memory for users across conversations. Automatically queries relevant memories when starting a new topic, and saves important personal information, habits, schedules, and user-requested memories. Use this when discussing user background, preferences, or plans; starting a new task or project; users mention personal details or ask to remember something.
Connect to the Pieces MCP server (SSE) and reliably query or write to Pieces Long‑Term Memory (LTM) using query/write tool patterns (e.g., ask_pieces_ltm + create_pieces_memory), with practical troubleshooting and request-shaping examples.
Manages the Notion LLM Config Table database tracking model configurations and parameters across multiple model providers. Use when working with the LLM config table, adding models, updating model parameters, or querying model architecture details.
Automatic documentation memory system for AI agents. Ensures context is loaded at session start and documentation is updated after changes.
Use OpenAI Codex CLI to answer questions about code, analyze files, or perform read-only codebase exploration. Invoke this skill when the user asks to use Codex, wants a second opinion from another AI agent, or wants to compare Claude's answer with Codex's response.
Respond with different character personalities (pirate, butler, professor) when the user requests character-style responses. Use when the user says phrases like "talk like a pirate", "respond as a butler", "explain like a professor", or similar requests in Japanese or English.
Patterns for coordinating multiple AI agents. Use when single-agent context limits are exceeded, tasks decompose naturally, or specialized agents improve quality.
The Orchestrator Agent - Master coordinator for the Nebuchadnezzar v4.0 Pipeline OS. Manages the Four Pillars: Expert CRUD, Observability, ADHD Execution Loop, and Learning Propagation. This agent ensures all domain experts stay aligned, improve continuously, and execute the right tasks. Use when: (1) Starting a new session - runs HUD check and recommends focus (2) "orchestrate" or "coordinate" - multi-expert task routing (3) "which expert" or "load expert" - expert selection guidance (4) "system health" or "pipeline status" - full observability report (5) "propagate learning" or "update experts" - cross-expert knowledge sharing (6) Any red stage in HUD - automatically routes to correct expert Triggers on: "orchestrate", "coordinate", "system health", "which expert", "load expert", "propagate", "adhd loop", "four pillars", "pipeline os"
Collaborates with OpenAI Codex as an external AI agent for second opinions on complex decisions. Provides guidelines for brainstorming, plan validation, and code review workflows. Use when making architectural decisions, validating implementation plans, reviewing code after significant changes, or consulting codex, codex-reply, or codex-review MCP tools.
Understand authentication and authorization defects in AI-generated code including insecure password storage, broken session management, and access control bypasses. Use this skill when you need to learn about auth vulnerabilities in AI code, understand why AI suggests MD5/plaintext passwords, recognize broken session patterns, or identify access control gaps. Triggers include "auth vulnerabilities AI", "password storage AI", "session management", "broken access control", "authentication defects", "MD5 passwords", "session hijacking", "authorization bypass".
This skill should be used when the user asks to "add WebSocket", "implement real-time", "create file upload", "add S3 upload", "send email", "implement notifications", "broadcast events", or mentions WebSockets, file storage, email service, push notifications, or real-time communication. Provides WebSocket, file upload, and notification patterns.
Comprehensive development guides for advanced Kailash SDK features including custom node development, MCP development, async patterns, testing strategies, production deployment, RAG systems, security patterns, monitoring, and SDK internals. Use when asking about 'development guide', 'advanced features', 'custom node development', 'async node development', 'MCP development', 'production deployment', 'testing strategies', 'RAG implementation', 'security patterns', 'monitoring setup', 'circuit breaker', 'compliance', 'edge computing', or 'SDK internals'.
Instructions for managing AI models and providers, including icon usage and implementation status.
