player-comparison-tool
Side-by-side stat comparisons with context. Adjust for era, pace of play, league differences. Advanced metrics explained in plain English.
Side-by-side stat comparisons with context. Adjust for era, pace of play, league differences. Advanced metrics explained in plain English.
Use when backing up, restoring, or validating golden datasets. Prevents data loss and ensures test data integrity for AI/ML evaluation systems.
Use when validating golden dataset quality. Runs schema checks, duplicate detection, and coverage analysis to ensure dataset integrity for AI evaluation.
This skill should be used when the user wants to invoke Google Gemini CLI for complex reasoning tasks, research, and AI assistance. Trigger phrases include "use gemini", "ask gemini", "run gemini", "call gemini", "gemini cli", "Google AI", "Gemini reasoning", or when users request Google's AI models, need advanced reasoning capabilities, research with web search, or want to continue previous Gemini conversations. Automatically triggers on Gemini-related requests and supports session continuation for iterative development.
FAQ identification from support tickets, step-by-step tutorial creation, screenshot/video script guidance, search optimization, and self-service deflection tracking.
Multi-agent frameworks beyond LangGraph. CrewAI crews, Microsoft Agent Framework, OpenAI Agents SDK. Use when building multi-agent systems, choosing frameworks.
Comprehensive prompt engineering with Chain-of-Thought, few-shot learning, prompt versioning, and optimization. Use when designing prompts, improving accuracy, managing prompt lifecycle.
Use when designing agent system prompts, optimizing RAG retrieval, or when context is too expensive or slow. Reduces tokens while maintaining quality through strategic positioning and attention-aware design.
Use when creating or improving golden datasets for AI evaluation. Defines quality criteria, curation workflows, and multi-agent analysis patterns for test data.
Local LLM inference with Ollama. Use when setting up local models for development, CI pipelines, or cost reduction. Covers model selection, LangChain integration, and performance tuning.
Use when testing web applications with AI-assisted Playwright. Features autonomous test agents for planning, generating, and self-healing tests automatically.
Testing patterns for LLM-based applications. Use when testing AI/ML integrations, mocking LLM responses, testing async timeouts, or validating structured outputs from LLMs.
Use this skill when documenting significant architectural decisions. Provides ADR templates following the Nygard format with sections for context, decision, consequences, and alternatives. Helps teams maintain architectural memory and rationale for backend systems, API designs, database choices, and infrastructure decisions.
LLM output evaluation and quality assessment. Use when implementing LLM-as-judge patterns, quality gates for AI outputs, or automated evaluation pipelines.
系统化降低AI检测率至30%以下,通过三遍审校流程(内容、风格、细节)增加人味。当用户提到"AI味太重"、"像AI写的"、"降低AI检测率"、"更像人写的"、"自然一些"、"口语化"时使用此技能。
Expert guidance for Flutter and Dart development. Use when building Flutter apps, implementing state management, setting up routing, writing tests, or working with Flutter UI components. Provides access to detailed rules for Bloc, Riverpod, Provider, Mocktail, and more.
Generate realistic coach/player interview responses for wins, losses, controversies, injuries. Authentic coachspeak and player personalities.
Provider-native prompt caching for Claude and OpenAI. Use when optimizing LLM costs with cache breakpoints, caching system prompts, or reducing token costs for repeated prefixes.
LLM fine-tuning with LoRA, QLoRA, DPO alignment, and synthetic data generation. Efficient training, preference learning, data creation. Use when customizing models for specific domains.
自动识别prompt类型并保存到相应分类(技术/内容/教学/产品/通用),支持自动文件命名和索引管理。当用户提到"保存prompt"、"记录prompt"、"管理prompt"、"整理prompt"、"prompt库"时使用此技能。