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Found 2,493 Skills
Comprehensive Agentforce testing skill with dual-track workflow: multi-turn API testing (primary) and CLI Testing Center (secondary). Execute multi-turn conversations via Agent Runtime API, run single-utterance tests via sf CLI, analyze topic/action/context coverage, and automatically fix failing agents with 100-point scoring across 7 categories.
World-class prompt engineering skill for LLM optimization, prompt patterns, structured outputs, and AI product development. Expertise in Claude, GPT-4, prompt design patterns, few-shot learning, chain-of-thought, and AI evaluation. Includes RAG optimization, agent design, and LLM system architecture. Use when building AI products, optimizing LLM performance, designing agentic systems, or implementing advanced prompting techniques.
Building AI agents with the Convex Agent component including thread management, tool integration, streaming responses, RAG patterns, and workflow orchestration
Use when writing unit/integration tests for Vite projects - provides Vitest configuration, test APIs, mocking patterns, and coverage setup
AWS S3 object storage for bucket management, object operations, and access control. Use when creating buckets, uploading files, configuring lifecycle policies, setting up static websites, managing permissions, or implementing cross-region replication.
Understand Polymarket prediction markets for sports and esports betting. Use when working with Polymarket API, sports arbitrage, binary contracts, CLOB pricing, or cross-platform trading with Kalshi. Triggers on: polymarket, prediction market, sports betting, esports betting, arbitrage, CLOB.
Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.
Automates fuzz test creation for C++ projects using Google FuzzTest with consistent software testing patterns. Use when creating fuzz tests, mutation testing, or when the user mentions fuzzing, AFL, or coverage-guided testing.
Generates comprehensive, workable unit tests for any programming language using a multi-agent pipeline. Use when asked to generate tests, write unit tests, improve test coverage, add test coverage, create test files, or test a codebase. Supports C#, TypeScript, JavaScript, Python, Go, Rust, Java, and more. Orchestrates research, planning, and implementation phases to produce tests that compile, pass, and follow project conventions.
Generate and validate Apex test classes with TestDataFactory patterns, bulk testing (251+ records), mocking strategies, assertion best practices, and disciplined test-fix loops. Use this skill when creating new Apex test classes, improving test coverage, debugging and fixing failing Apex tests, running test execution and coverage analysis, or implementing testing patterns for triggers, services, controllers, batch jobs, queueables, and integrations. Triggers on *Test.cls, *_Test.cls files, sf apex run test workflows, coverage reports, test-fix loops. Do NOT trigger for production Apex code (use generating-apex) or Jest/LWC tests.
Use this skill when you have structured course content (or any chapter-based dataset) in markdown form and need to turn it into a working interactive website — without picking a framework, without a build step. Triggers on phrases like "做成網頁", "轉成 SPA", "course-data.js", "render 函式", "把講義變網頁", "static site from markdown", "vanilla JS site", "no-framework site", "single-page app from markdown". The output is a vanilla HTML + JS single-page app that opens with `npx serve` and persists state in localStorage. Always invoke AFTER `course-content-authoring` (content stable), BEFORE `static-spa-interactions` (this skill produces the scaffold; interactions adorn it).
Select and optimize embedding models for semantic search and RAG applications. Use when choosing embedding models, implementing chunking strategies, or optimizing embedding quality for specific domains.