Skill UI

This skill provides the comprehensive workflow for interacting with the Anthropic Messages API. It covers advanced features including streaming responses, multimodal vision input, generating structured JSON output using system prompts, and managing multi-turn conversations. It redirects users to the current, primary guide for all Claude API patterns.

This guide provides production-ready code patterns for integrating with the Cohere AI API v2 using TypeScript and Python. It covers essential best practices, including singleton client management, robust retry mechanisms, type-safe chat wrappers, streaming, embedding, reranking, and structured JSON output. Use this when establishing team coding standards or refactoring existing Cohere integrations.

One-shot CLI wrapper around DeepSeek's deepseek-chat model via the OpenAI-compatible chat/completions endpoint. Ideal for quick summarization, extraction, and classification; returns JSON output and degrades gracefully when the API key is missing.

Wraps DeepSeek's deepseek-reasoner (R1) model to return chain-of-thought separately from the final answer. Supports JSON/table output, reasoning token accounting, and graceful degradation when the API key is missing or unreachable.

Run Claude Code non-interactively for coding tasks via the claude -p CLI with JSON output. Covers availability checks, timeouts, read-only tool defaults, and safe handling of missing authentication.

Run the Gemini CLI non-interactively to delegate coding tasks such as module reviews, using JSON output parsing, finite timeouts, and read-only permissions by default.

Runs the local OpenClaw main agent non-interactively via CLI with a finite timeout, JSON output, and read-only prompts. Use to delegate bounded tasks while avoiding onboarding, TUI, login, and credential handling.

Generate workflow JSON files for chaining AI models on fal.ai. Create reviewable graphs for multi-step media workflows, resolve inputs/outputs/dependencies, and validate model schemas. Use for planning authorized runs through available integrations.

This skill provides comprehensive techniques for optimizing prompts used with any Large Language Model (LLM). It teaches proven methodologies—including Role definition (R), Context setting (S), Constraints (C), Instructions (I), and Templates (T)—to transform vague inputs into precision-engineered instructions. Users can reliably achieve structured JSON output, implement Chain-of-Thought reasoning, reduce hallucinations, and minimize token usage, ensuring consistent and high-quality results across different AI platforms.

Learn how to configure xberg's --format and --content-format flags to optimize document extraction for different consumers. Covers LLM context, RAG indexing, JSON parsing, and token reduction strategies for efficient data pipelines.