发现 testing security llm ai 相关 Skills，比较来源仓库和用途，查看适合你工作流的技能说明。
Automated 2-AI verification result documentation and export workflow. Triggers when user requests AI verification report export, documentation of findings, or saving cross-validation results. Use after completi...
Agent memory architecture including working, short-term, long-term, and temporal knowledge graphs.
Fetch LLM-optimized documentation for Clerk, the complete user management platform.
Web scraping, content extraction, and autonomous research agent for deep web investigation.
LLM-as-Judge techniques including direct scoring, pairwise comparison, rubric generation, and bias mitigation.
Healer monitoring expertise - detection patterns, API endpoints, dual-model architecture, and remediation workflows. Use when monitoring Play workflows or debugging agent failures.
Deep technical research using Firecrawl Agent for autonomous web investigation, competitive analysis, and implementation pattern discovery.
Persistent long-term agent memory for storing and querying past work, patterns, and learnings.
Agent evaluation methods including LLM-as-judge, rubrics, and quality gates.
KubeAI, GPU operators, and model serving patterns for AI/ML infrastructure on Kubernetes.
The Ralph Wiggum technique for minimal, declarative prompts that enable loop-based autonomous execution.
Meta-skill for creating and improving agent skills. Use when designing new skills, refining existing skills, or understanding skill architecture patterns. Provides progressive disclosure, context efficiency pri...
Fetch Bun runtime documentation via llms.txt for up-to-date API references
Methodology for identifying LLM-suited tasks, designing pipelines, and iterating with agent-assisted development.
Linear Agent API expertise - sessions, activities, signals, and best practices. Use when working with Linear agent integration, verifying dialogue population, or implementing two-way communication.
Fetch LLM-optimized documentation from llms.txt endpoints for up-to-date API references
Guide for creating MCP (Model Context Protocol) servers that enable LLMs to interact with external services. Use when building MCP servers, integrating external APIs, implementing tool servers, or creating agen...
Recognize and mitigate context failures including lost-in-middle, context poisoning, distraction, confusion, and clash.
Foundational understanding of context engineering for AI agent systems - context windows, attention mechanics, progressive disclosure, and context budgeting
Design effective tools for AI agents - tool descriptions, consolidation principles, architectural reduction, MCP naming conventions, and error handling
Create distinctive, production-grade frontend interfaces with high design quality. Use when building UI components, pages, or applications to avoid generic AI aesthetics.
Conversational interface for managing MCP (Model Context Protocol) server configurations in Claude Code
Generate relevant memes using the memegen.link API. Use this skill when users request memes, want to add humor to content, or need visual aids for social media. Supports hundreds of popular meme templates with...
Master the art of creating effective AI agent personas for Jules. Learn how to design role-specific prompts that guide Jules to execute tasks with precision, following best practices, and maintaining project st...
默认按已收录数据中的 GitHub 仓库 Stars 排序。Stars 属于来源仓库，同一仓库的多个技能可能共享相同数值；它反映仓库关注度，不代表单个技能的安装量或质量评分。