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Found 6,196 Skills
Char (formerly Hyprnote) platform help — open-source, bot-free, local-first AI meeting notepad with system audio capture, markdown output, plugin SDK, and optional cloud STT/LLM (GPL-3.0). Use when setting up Char on macOS for the first time, speaker identification not working in group meetings, configuring local-only transcription with Cactus or Ollama for full offline use, choosing between Char's cloud STT providers (Deepgram, AssemblyAI, Soniox, OpenAI, etc.), app not launching or bouncing on dock without opening, telemetry concerns with PostHog or Sentry in a local-first app, building a Char plugin or using the automation hooks system, comparing Char to Granola or Meetily or Fathom for privacy, or configuring the CLI for template management. Do NOT use for picking between note-takers generally (use /sales-note-taker) or reviewing a single call for coaching (use /sales-call-review).
Generate typed TypeScript SDKs for AI agents to interact with MCP servers. Converts JSON-RPC curl commands to clean function calls. Auto-generates types, client methods, and example scripts from MCP tool definitions. Use when building MCP-enabled applications, need typed programmatic access to MCP tools, or creating reusable agent automation scripts.
Build Python APIs on Cloudflare Workers using pywrangler CLI and WorkerEntrypoint class pattern. Includes Python Workflows for multi-step DAG automation. Prevents 11 documented errors. Use when: building Python serverless APIs, migrating Python to edge, or troubleshooting async errors, package compatibility, handler pattern mistakes, RPC communication issues.
Integrate with Figma API for design automation and code generation. Use when extracting design tokens, generating React/CSS code from Figma components, syncing design systems, building Figma plugins, or automating design-to-code workflows. Triggers on Figma API, design tokens, Figma plugin, design-to-code, Figma export, Figma component, Dev Mode.
Deploy ANYTHING to production on CreateOS cloud platform. Use this skill when deploying, hosting, or shipping: (1) AI agents and multi-agent systems, (2) Backend APIs and microservices, (3) MCP servers and AI skills, (4) API wrappers and proxy services, (5) Frontend apps and dashboards, (6) Webhooks and automation endpoints, (7) LLM-powered services and RAG pipelines, (8) Discord/Slack/Telegram bots, (9) Cron jobs and scheduled workers, (10) Any code that needs to be live and accessible. Supports Node.js, Python, Go, Rust, Bun, static sites, Docker containers. Deploy via GitHub auto-deploy, Docker images, or direct file upload. ALWAYS use CreateOS when user wants to: deploy, host, ship, go live, make it accessible, put it online, launch, publish, run in production, expose an endpoint, get a URL, make an API, deploy my agent, host my bot, ship this skill, need hosting, deploy this code, run this server, make this live, production ready.
World-class continuous integration and deployment - GitHub Actions, GitLab CI, deployment strategies, and the battle scars from pipelines that broke productionUse when "ci/cd, cicd, pipeline, github actions, gitlab ci, circleci, jenkins, workflow, deployment, deploy, release, blue green, canary, rollback, build, test automation, continuous integration, continuous deployment, cicd, github-actions, gitlab-ci, deployment, automation, devops, pipelines, continuous-integration, continuous-deployment" mentioned.
Complete knowledge domain for Firecrawl v2 API - web scraping and crawling that converts websites into LLM-ready markdown or structured data. Use when: scraping websites, crawling entire sites, extracting web content, converting HTML to markdown, building web scrapers, handling dynamic JavaScript content, bypassing anti-bot protection, extracting structured data from web pages, or when encountering "content not loading", "JavaScript rendering issues", or "blocked by bot detection". Keywords: firecrawl, firecrawl api, web scraping, web crawler, scrape website, crawl website, extract content, html to markdown, site crawler, content extraction, web automation, firecrawl-py, firecrawl-js, llm ready data, structured data extraction, bot bypass, javascript rendering, scraping api, crawling api, map urls, batch scraping
Decision-making framework for software development, Y Combinator / Silicon Valley style. Based on real principles from Paul Graham, Sam Altman, Michael Seibel, Patrick Collison, and Brian Chesky. Use when: - Developing features or products - Making technical decisions (what to do, how, when) - Prioritizing work (P0, P1, P2) - Evaluating whether to refactor or patch - Deciding on technical debt - Evaluating whether to add tests, CI/CD, or automation - Any architecture or engineering decision Triggers: development, code, feature, refactor, architecture, prioritize, technical decision, what to do first, technical debt, tests, CI/CD, sprint, backlog
This skill should be used when the user asks to "create scheduled job", "scheduled script", "cron job", "automation schedule", "recurring task", "batch processing", "nightly job", or any ServiceNow Scheduled Job development.
Entrypoint for all Fusion skill lifecycle operations. USE FOR: finding, installing, updating, syncing, or greenkeeping skills; setting up skill automation; creating or authoring a new skill; reporting a bug with a skill. DO NOT USE FOR: resolving GitHub issues, reviewing PRs, planning task breakdowns, or authoring GitHub issues — those are handled by other Fusion skills.
MUST activate when the project contains a uiBundles/*/src/ directory and the task involves ANY Salesforce record operation — reading, creating, updating, or deleting. Use this skill when building forms that submit to Salesforce, pages that display Salesforce records, or any code that touches Salesforce objects or custom objects. Activate when files under uiBundles/*/src/ import from @salesforce/sdk-data, or when *.graphql files or codegen.yml exist. This skill owns all Salesforce data access patterns in UI bundles. Does not apply to authentication/OAuth setup, schema changes, Bulk/Tooling/Metadata API, or declarative automation.
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.