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Found 9,265 Skills
Create publication-quality scientific diagrams using Nano Banana Pro AI with iterative refinement. AI generation is the default method for all diagram types. Generates high-fidelity images with automatic quality review. Specialized in neural network architectures, system diagrams, flowcharts, biological pathways, and complex scientific visualizations.
Search for new preprints in infectious disease modelling from arXiv, medRxiv, and bioRxiv
Use when building ongoing loop animations - loading states, ambient motion, background effects that run indefinitely without user fatigue
Weave authentication webs with patient precision. Spin the threads, connect the strands, secure the knots, and bind the system. Use when integrating auth, setting up OAuth, or securing routes.
How to add or modify Next.js experimental feature flags end-to-end. Use when editing config-shared.ts, config-schema.ts, define-env-plugin.ts, next-server.ts, export/worker.ts, or module.compiled.js. Covers type declaration, zod schema, build-time injection, runtime env plumbing, and the decision between runtime env-var branching vs separate bundle variants.
Comprehensive technical research by combining multiple intelligence sources — Grok (X/Twitter developer discussions via Playwright), DeepWiki (AI-powered GitHub repository analysis), and WebSearch. Dispatches parallel subagents for each source and synthesizes findings into a unified report. This skill should be used when evaluating technologies, comparing libraries/frameworks, researching GitHub repos, gauging developer sentiment, or investigating technical architecture decisions. Trigger phrases include "tech research", "research this technology", "技术调研", "调研一下", "compare libraries", "evaluate framework", "investigate repo".
Full feature pipeline — pre-flight checks, TDD cycle, scope guard, quality commit. Combines pre-flight + tdd + scope-check + quality-commit into one flow. Use when implementing a feature, adding an endpoint, or building any non-trivial code change.
Creates beautiful, protein-focused weekly dinner menus for families with research capabilities. Generates printer-ready PDFs (8.5x11) with random design styles, identifies leftover opportunities, suggests local restaurants, emphasizes seasonal ingredients, and includes Homemade Pizza Fridays. Use when users want to plan family dinners, create refrigerator menus, or need meal planning assistance.
Decompose research ideas into atomic, self-contained concepts with bidirectional math-code mapping. For each concept, extract the math formula from papers and find code implementations. Use for complex system papers requiring formal grounding.
Enrich contact, company, and influencer data using x402-protected APIs. Superior to generic web search for structured business data. USE FOR: - Enriching person profiles by email, LinkedIn URL, or name - Enriching companies by domain - Finding contact details (email, phone) with confidence scores - Scraping full LinkedIn profiles (experience, education, skills) - Searching for people or companies by criteria - Bulk enrichment operations (up to 10 at a time) - Verifying email deliverability before outreach - Enriching influencer/creator profiles across social platforms TRIGGERS: - "enrich", "lookup", "find info about", "research" - "who is [person]", "company profile for", "tell me about" - "find contact for", "get LinkedIn for", "get email for" - "employee at", "works at", "company details" - "verify email", "check email", "is this email valid" - "influencer", "creator", "influencer contact", "influencer marketing" ALWAYS use `npx agentcash fetch` for stableenrich.dev endpoints - never curl or WebFetch. Returns structured JSON data, not web page HTML. IMPORTANT: Use exact endpoint paths from the Quick Reference table below. All paths include a provider prefix (`https://stableenrich.dev/api/apollo/...`, `https://stableenrich.dev/api/clado/...`, etc.).
Generate color ramps designed for WCAG-compliant contrast pairing. Creates 11-step scales with predictable foreground/background combinations.
Use when combining information from multiple Glean sources or when needing to synthesize results across documents, meetings, code, and people searches. Triggers on complex queries that span multiple data types, when results seem contradictory, when building comprehensive answers from partial information, or when the user asks for a complete picture of something that requires multiple queries.