Skill UI

This skill set provides advanced, structured strategies for optimizing Large Language Model (LLM) interactions. It covers techniques such as intelligent context summarization, dynamic trimming, context routing, and precise token budget allocation to prevent 'context rot'. It is essential for building robust, long-running, multi-turn conversational AI systems.

This skill manages and refines search results obtained from Exa's neural search API. It provides comprehensive control over content extraction scope, allowing users to selectively retrieve metadata, highlights, full text, or structured summaries. Key features include implementing result caching with TTL, deduplicating citations, and crucial token budget management, ensuring the retrieved context fits within LLM context window limits for robust RAG pipelines.

A comprehensive guide to optimizing LLM API usage within LangChain frameworks. Techniques include implementing token usage tracking, model tiering to route tasks based on complexity, utilizing caching to eliminate duplicate calls, prompt compression for efficiency, and enforcing strict budget limits to manage operational costs effectively.

Learn how to monitor, analyze, and optimize the expenditure of Large Language Models (LLMs) using Langfuse's advanced analytics suite. This guide covers techniques like automatic token tracking, programmatic cost reporting via the Metrics API, and implementing smart model routing to automatically select the most cost-effective model based on task complexity and input length. Essential for any developer managing AI budgets and ensuring operational efficiency.

This guide provides advanced techniques for managing API rate limits (429 errors) and complexity budgets when interacting with the Linear API. It covers reading rate limit headers, implementing exponential backoff with jitter for robust retries, controlling request bursts via token buckets, and optimizing GraphQL queries to minimize complexity scores. Use this when developing reliable integrations that must adhere to strict API quotas.

This toolkit provides a comprehensive framework for managing and optimizing API costs when using Mistral AI. It includes functions for calculating token-based costs across various models, implementing a smart model router based on task complexity, and managing daily/monthly budgets with critical alerts. Furthermore, it offers methods for prompt optimization to reduce token consumption, making it essential for large-scale application cost control and billing analysis.

This skill provides a comprehensive guide to understanding OpenRouter's API billing structure. It teaches users how to accurately calculate costs based on prompt (input) and completion (output) tokens. Users can utilize provided scripts to estimate costs, check credit balances, and implement cost-saving strategies like using `:floor` or `:free` model variants. Ideal for budgeting, performance comparison, and production cost tracking.

This tool audits the token consumption across all components (agents, skills, rules, MCP servers) within a Claude Code session. It analyzes token overhead, identifies bloat (e.g., overly long descriptions, redundant components), and generates a detailed context budget report. Use it to maintain optimal context window usage, improve performance, and prevent context saturation.

This skill intercepts the response flow before generation, allowing the user to explicitly control the desired depth, length, and token usage of the AI's answer. It estimates the complexity of the input prompt and presents multiple options (e.g., 25%, 50%, 75%, 100%), ensuring the output matches the user's need, whether they require a brief summary or a comprehensive, detailed analysis.

Provides strategies to optimize costs when utilizing the Cohere API. This guide covers model tier selection (economy, standard, premium), implementing token budgets, reducing embedding storage costs (e.g., using int8), and establishing comprehensive usage trackers. It is essential for maintaining cost predictability in production AI deployments.

This skill defines canonical, ordered middleware patterns for LangChain and LangGraph agents. It addresses critical production-level LLM deployment issues, including PII leakage across tenants, token budget overconsumption, prompt injection defense (Guardrails), and cache key corruption. Use this to build secure, scalable, and cost-aware AI applications.

This skill implements advanced 'Gatekeeper' logic to optimize AI agent performance in long-running workflows. It prevents context window bloat by sharding metadata, allocating token budgets, and enforcing Atomic Precision to deliver purely functional, filler-free responses. Ideal for complex coding, multi-step reasoning, and large document analysis where conciseness is critical.