404건 · 관련도순
Session-persistent browser automation via Playwright CLI — form filling, screenshots, data extraction, multi-step flows. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information across multiple requests. NOT for AI-driven long-autonomous flows (see mk:agent-browser); NOT for generating .spec.ts E2E code (see mk:qa-manual).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Fallback for general coding work that does not match any specific sextant sub-skill. Use for lightweight tasks: config changes, style fixes, utility functions, one-off scripts, or tasks too small to warrant a dedicated workflow. All dedicated sub-skills take priority over this one — check these first: sextant:fix-bug (broken behavior), sextant:debug (unknown bug location), sextant:add-feature (new functionality), sextant:modify-feature (changing existing behavior), sextant:review-code (evaluating code), sextant:write-tests (test coverage), sextant:refine-requirements (ambiguous scope), sextant:plan (multi-step sequencing), sextant:ship (release prep), sextant:migrate (cross-module upgrade), sextant:security (vulnerability audit). Only use this skill when none of the above match.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you have a spec or requirements for a multi-step task, before touching code
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Central entry point of the afonsoft agent harness. Use when starting a new project, resuming an existing one, planning features/Epics/releases, or running any multi-step agent-driven work. Validates and reconciles SPECs (SDD), audits the codebase and harness for gaps (security, architecture, performance, hygiene), proposes improvements, fragments work into GitHub Issues, delegates implementation/QA/review to specialized skills, and re-validates everything until delivery. Also use to review unapproved SPECs, reconcile open GitHub Issues with code, or run a final gap check before closing a release.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Jev (TypeSafe System One) inner loop for ego-browser — one ~0.4 s typed decision per DOM step instead of an LLM turn. Use for multi-step clicking through a semantic page: fill known values into a form, set filters, open a row/card/menu by name, reach a page via nav or site search. Escalates login, payment, free text and content reading back to you; canvas/icon targets only with opt-in screenshot mode.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use for multi-step / ambiguous / end-to-end work — refactor a whole module, drive a vague ticket to a verified result, plan+build+verify a migration; coordinates the reasoning chain across skills.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Autonomous multi-step investigation — deep research carried end-to-end without per-step approval; explicit request only, never for normal feature work.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you have a spec or requirements for a multi-step task, before touching code
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply to multi-step work (sweeps, migrations, runs of similar edits) and to how you stack commits and PRs. Break work into small units that each end in a verifiable state, check each before the next, and order delivery so the sequence proves itself to a reviewer.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Controle saída de LLM com regex e gramáticas, garanta geração válida de JSON/XML/código, aplique formatos estruturados e construa fluxos de trabalho multi-etapas com Guidance - framework de geração restrita da Microsoft Research
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when you have a spec or requirements for a multi-step task, before touching code
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills, context loading, or multi-step workflows, to make information discoverable and use context efficiently.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate reusable multi-step agent workflow blueprints. Use for trigger/action orchestration, deterministic workflow definitions, and automation handoff artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Start a project on the Visual Stack — a multi-step setup form captures what's being built, recommends a stack, and either instantiates the vstack-template-base template, sets up a specs-and-design-only workspace, or records an existing codebase without touching it. Use when the user wants to start a new project, scaffold a repo, set up a codebase from the Cavalry template, pick a stack or add-ons, run Day-1 setup, or bring an existing app onto the visual stack.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Apply the Workflow Orchestration methodology for complex, multi-step tasks — plan-first approach, subagent strategy, self-improvement loop, verification before done, elegant solutions, and autonomous bug fixing. Use for any non-trivial task with 3+ steps or architectural decisions.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Act as a Senior Frontend Systems Engineer to architect interactive modules — multi-step forms, pricing calculators, faceted search, dashboards, and auth flows. Defines state machines, data flow, error handling, and React component structure. Use when designing complex UI logic for Figma Make prototypes or React implementation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build AI-powered browser agents that autonomously navigate, scrape, fill forms, and extract data from dynamic websites using LLM reasoning + Playwright. Use when building a scraper that must survive site redesigns, an agent that completes multi-step web workflows, or a replacement for brittle Selenium/Puppeteer scripts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Unified document optimization skill that chains docling-converter, imagemagick-expert, and markdown-to-pdf. Automatically detects input format, applies appropriate conversion pipeline, optimizes embedded images, and produces web/print-ready output with configurable quality presets. Use when converting documents through multi-step pipelines, optimizing PDF images, creating print-ready documents from various sources, or batch processing documents with image optimization.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Control and interact with a live browser session on any scraped page — click buttons, fill forms, navigate flows, and extract data using natural language prompts or code. Use when the user needs to interact with a webpage beyond simple scraping: logging into a site, submitting forms, clicking through pagination, handling infinite scroll, navigating multi-step checkout or wizard flows, or when a regular scrape failed because content is behind JavaScript interaction. Also useful for authenticated scraping via profiles. Triggers on "interact", "click", "fill out the form", "log in to", "sign in", "submit", "paginated", "next page", "infinite scroll", "interact with the page", "navigate to", "open a session", or "scrape failed".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Manus-style file-based planning for multi-step work. Keep task_plan.md, findings.md, progress.md in your run workspace so the filesystem is your working memory. Use for any research/build with 5+ tool calls. Adapted for OpenMark sub-agents from Ahmad's planning-with-files v3.0.0.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
HTML-first persistent planning skill. Generates a single self-contained plan.html with interactive phases, drag-and-drop tickets, sliders, mockups, and embedded JSON state. Survives /clear via session catchup, tamper-protected by SHA-256, mirrors across 17 IDEs, ships 10 templates across Thariq's 9 categories, exports back to Markdown on demand. Use when asked to "plan it", "make me an html plan", "show me the plan", "render the plan", or when starting any multi-step task that needs a navigable artifact instead of a markdown wall.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build multi-step AI content creation pipelines combining image, video, audio, and text. Workflow examples: generate image -> animate -> add voiceover -> merge with music. Tools: FLUX, Veo, Kokoro TTS, OmniHuman, media merger, upscaling. Use for: YouTube videos, social media content, market...
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.