Loading...
Loading...
Found 3,513 Skills
Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern. Used in production at LinkedIn, Uber, and 400+ companies. This is LangChain's recommended approach for building agents. Use when: langgraph, langchain agent, stateful agent, agent graph, react agent.
Generates image prompts for Seedream 5.0/4.0 (Jimeng AI), and can call the API to generate images and automatically download them to the output/ directory. Workflow: describe your idea → the agent outputs a prompt for review → user confirms → the agent runs generate.py. It covers text-to-image, image editing, multi-image fusion, character consistency, knowledge cards, posters, PPT backgrounds, e-commerce images, avatars, and group/storyboard generation. Activate this tool when the user mentions terms like seedream, jimeng, AI image generation, text-to-image, image-to-image, seedream prompt, prompt keyword, one-click image generation, knowledge card, poster design, e-commerce image, character consistency, or image generation.
Workflow automation is the infrastructure that makes AI agents reliable. Without durable execution, a network hiccup during a 10-step payment flow means lost money and angry customers. With it, workflows resume exactly where they left off. This skill covers the platforms (n8n, Temporal, Inngest) and patterns (sequential, parallel, orchestrator-worker) that turn brittle scripts into production-grade automation. Key insight: The platforms make different tradeoffs. n8n optimizes for accessibility
The foundational theory of interactive experience design - loops, motivation, feel, and the art of meaningful playUse when "game design, core loop, game feel, player motivation, game mechanics, meaningful choice, progression system, game economy, game balance, playtesting, GDD, game document, fun factor, engagement, flow state, risk reward, player agency, juice, game polish, 8 kinds of fun, bartle types, MDA framework, game-design, player-experience, core-loop, motivation, game-feel, MDA, playtesting, GDD, systems-thinking, player-psychology, engagement, flow-state" mentioned.
Salesforce debugging and troubleshooting skill with log analysis, governor limit detection, and agentic fix suggestions. Parse debug logs, identify performance bottlenecks, analyze stack traces, and automatically suggest fixes.
Systematically explore and test a mobile app on iOS/Android with agent-device to find bugs, UX issues, and other problems. Use when asked to "dogfood", "QA", "exploratory test", "find issues", "bug hunt", or "test this app" on mobile. Produces a structured report with reproducible evidence: screenshots, optional repro videos, and detailed steps for every issue.
Expert guide on prompt engineering patterns, best practices, and optimization techniques. Use when user wants to improve prompts, learn prompting strategies, or debug agent behavior.
Convert websites into LLM-ready data with Firecrawl API. Features: scrape, crawl, map, search, extract, agent (autonomous), batch operations, and change tracking. Handles JavaScript, anti-bot bypass, PDF/DOCX parsing, and branding extraction. Prevents 10 documented errors. Use when: scraping websites, crawling sites, web search + scrape, autonomous data gathering, monitoring content changes, extracting brand/design systems, or troubleshooting content not loading, JavaScript rendering, bot detection, v2 migration, job status errors, DNS resolution, or stealth mode pricing.
The base44 SDK is the library to communicate with base44 services. In projects, you use it to communicate with remote resources (entities, backend functions, ai agents) and to write backend functions. This skill is the place for learning about available modules and types. When you plan or implement a feature, you must learn this skill
Autonomous AI coding with spec-driven development. Implements Geoffrey Huntley's iterative bash loop methodology where agents work through specs one at a time, outputting a completion signal only when acceptance criteria are 100% met.
Review AI-generated code changes before committing using GitHuman. Use when reviewing code changes, creating code reviews, checking what the AI agent wrote, preparing to commit, or when user mentions "review", "GitHuman", or "before commit".
AI-powered code review using CodeRabbit. Default code-review skill. Trigger for any explicit review request AND autonomously when the agent thinks a review is needed (code/PR/quality/security).