Loading...
Loading...
Found 1,650 Skills
Query fan-out coverage for AI visibility. Covers semantic variation analysis and sub-question targeting.
Coin comparison. Use this skill whenever the user asks to compare two or more coins. Trigger phrases include: compare, versus, vs, which is better, difference. MCP tools: info_marketsnapshot_get_market_snapshot, info_coin_get_coin_info per coin (or batch/search when available).
Expert skill for using DeepSeek-OCR, a vision-language model for optical character recognition with context optical compression supporting documents, PDFs, and images.
当用户的 PinMe 项目（Worker TypeScript）需要集成发送邮件（send_email）或调用大模型 API（chat/completions）时使用此技能。指导 AI 生成正确的 Worker TS 代码。
Review tweet drafts in Claude Code against 8 voice rules. Scores 1-10, breaks down every rule, and rewrites anything that scores below 7.
This skill should be used when the user asks to "audit prompts for safety", "check prompts for injection vulnerabilities", "manage a prompt catalog", "version control prompts", or "review prompt quality and compliance".
Подробная русскоязычная справка по Open WebUI: архитектура, авторизация, функции, пайплайны, API, RAG, масштабирование, отладка и скрытые возможности. Используй этот скилл при любых вопросах об Open WebUI — как он устроен, как развернуть, настроить авторизацию (OAuth, LDAP, JWT), написать функцию или пайплайн, подключить модель (Ollama, OpenAI), настроить RAG/knowledge base, масштабировать на production, отладить проблему. Также используй при написании кода для Open WebUI: функции (filter, pipe, action), пайплайны, конфигурации, docker-compose.
Compare Replicate models by cost, speed, quality, and capabilities.
Discover PUDA experiment workflows for bears and choose the right experiment for the task. Use when you need to run, set up, or understand a PUDA experiment such as colour mixing optimization.
Read production traces, identify what's failing, and build failure taxonomies using open coding and axial coding methodology. Use when debugging agent or pipeline quality, investigating "why are my outputs bad?", or before building any evaluator — error analysis must come first. Do NOT use when you already have identified failure modes and need evaluators (use build-evaluator) or datasets (use generate-synthetic-dataset).
Anthropic integration. Manage data, records, and automate workflows. Use when the user wants to interact with Anthropic data.
Check whether AutoDeploy YAML configs were actually applied by analyzing server logs and optionally graph dumps (AD_DUMP_GRAPHS_DIR). Use when the user wants to verify config application, debug config issues, or check if AutoDeploy transforms (piecewise CUDA graph, multi-stream, sharding, fusion, etc.) were applied or fell back. Triggers on: "check config", "verify config", "ad-conf-check", "were my configs applied", "config not working", "check if piecewise is enabled", "check log for config", or any request to compare AD YAML settings against runtime behavior.