Skill UI

A comprehensive framework for rigorously testing and benchmarking LLM agents. It moves beyond simple pass/fail checks to assess complex behaviors, reliability metrics, and capability consistency across multiple runs. Ideal for production monitoring and identifying subtle failure modes in advanced AI agents.

This guide provides comprehensive strategies for optimizing the execution speed, reliability, and cost efficiency of AI agents. Learn how to minimize latency, right-size models (e.g., Flash vs. Opus), consolidate multiple LLM steps into single prompts, and optimize workflow triggers. Ideal for use when agents are slow, consuming excessive credits, or producing inconsistent results.

Guides engineers through advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability across few-shot learning, reasoning patterns, templates, and system prompt design.

This skill provides advanced guidelines for designing robust and effective tools specifically for autonomous AI agents. It shifts the focus from traditional developer APIs to contracts optimized for Language Models (LLMs). Key topics covered include treating tool descriptions as prompt engineering, implementing namespacing, applying the Consolidation Principle, and utilizing Architectural Reduction patterns (like file system access) to maximize agent reliability and workflow complexity.

This methodology converts verbose, natural language system instructions into an atomic, structured-label format (e.g., trigger:, action:). By structuring rules, it bypasses the limitations of natural language processing, ensuring LLMs allocate full attention to every constraint, thereby maximizing compliance, improving reliability, and reducing token usage.

PydanticAI is a robust Python framework that integrates Pydantic's type-checking and validation capabilities into Large Language Models (LLMs). It allows developers to build production-ready AI agents that guarantee structured, validated outputs, moving beyond simple raw strings. Key features include supported tool calling, dependency injection for testability, and multi-model compatibility across major providers like OpenAI, Anthropic, and Gemini. Use this when reliability and correct data structure are paramount.

Automates comprehensive regression testing for all enabled cloud Large Language Model (LLM) providers. This includes verifying core connectivity, standard chat completion, structured JSON output, and Server-Sent Events (SSE) streaming capabilities. Essential for ensuring the reliability and stability of multi-cloud AI integrations in a CI/CD pipeline.

This tool provides comprehensive AI security scanning using the Tencent AI-Infra-Guard framework. It systematically audits AI infrastructure, scans tools and skills, checks agent security, and performs advanced LLM jailbreak evaluations to identify vulnerabilities and ensure the robustness and reliability of AI systems.

This skill performs a rigorous, cross-model audit of experimental results to ensure the integrity and reliability of reported findings. It detects common fraudulent patterns in LLM evaluations, such as fake ground truth creation, suspicious score normalization (e.g., dividing by model's own max), phantom results, and insufficient scope claims. Use this tool after an experiment is complete to verify the scientific validity of the claims.