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Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources.
Instalar com Codex ou Claude Copie este prompt, cole no Codex, Claude ou outro assistente e deixe que ele revise a página da skill e instale para você.
Baseado na classificação ocupacional SOC
| name | skill-builder |
| description | Automatically detect source types and build AI skills using Skill Seekers. Use when the user wants to create skills from documentation, repos, PDFs, videos, or other knowledge sources. |
You have access to the Skill Seekers MCP server which provides 35 tools for converting knowledge sources into AI-ready skills.
Use this skill when the user:
Automatically detect the source type from user input:
| Input Pattern | Source Type | Tool to Use |
|---|---|---|
https://... (not GitHub/YouTube) | Documentation | scrape_docs |
owner/repo or github.com/... | GitHub | scrape_github |
*.pdf | scrape_pdf | |
| YouTube/Vimeo URL or video file | Video | scrape_video |
| Local directory path | Codebase | scrape_codebase |
*.ipynb, *.html, *.yaml (OpenAPI), *.adoc, *.pptx, *.rss, *.1-.8 | Various | scrape_generic |
| JSON config file | Unified | Use config with scrape_docs |
generate_config or fetch_config if neededestimate_pages for documentation sitesenhance_skill if the user wants AI-powered improvementspackage_skill for the target platformexport_to_* toolsgenerate_config — Generate a scraping config from a URLlist_configs — List available preset configsvalidate_config — Validate a config filescrape_docs — Documentation sitesscrape_github — GitHub repositoriesscrape_pdf — PDF filesscrape_video — Video transcriptsscrape_codebase — Local code analysisscrape_generic — Jupyter, HTML, OpenAPI, AsciiDoc, PPTX, RSS, manpage, Confluence, Notion, chatenhance_skill — AI-powered skill enhancementpackage_skill — Package for target platformupload_skill — Upload to platform APIinstall_skill — End-to-end install workflowdetect_patterns — Design pattern detection in codeextract_test_examples — Extract usage examples from testsbuild_how_to_guides — Generate how-to guides from testssplit_config — Split large configs into focused skillsexport_to_weaviate, export_to_chroma, export_to_faiss, export_to_qdrant — Vector DB exportUse when the user wants to design, redesign, shape, critique, audit, polish, clarify, distill, harden, optimize, adapt, animate, colorize, extract, or otherwise improve a frontend interface. Covers websites, landing pages, dashboards, product UI, app shells, components, forms, settings, onboarding, and empty states. Handles UX review, visual hierarchy, information architecture, cognitive load, accessibility, performance, responsive behavior, theming, anti-patterns, typography, fonts, spacing, layout, alignment, color, motion, micro-interactions, UX copy, error states, edge cases, i18n, and reusable design systems or tokens. Also use for bland designs that need to become bolder or more delightful, loud designs that should become quieter, live browser iteration on UI elements, or ambitious visual effects that should feel technically extraordinary. Not for backend-only or non-UI tasks.
Use when starting any conversation - establishes how to find and use skills, requiring Skill tool invocation before ANY response including clarifying questions
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot learning, creating system prompts with personas and guardrails, building JSON/function-calling schemas, or developing prompt evaluation frameworks to measure and improve model performance.