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Use when designing how an Agentforce agent extracts structured input parameters (slots) from a user's natural-language utterance to invoke an action. Triggers: 'agent extract date from user', 'agent action argument extraction', 'agentforce slot filling', 'agent invocable input mapping', 'agent fails to fill required parameter'. NOT for action authoring (use agentforce/agent-actions) or for prompt-template variable binding (use agentforce/prompt-builder-templates).

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End-of-session pass that persists durable facts from the CURRENT session into .claude/memory/. Runs IN-SESSION in the main agent (never via Task — a subagent starts cold with no transcript), prompted by the Stop hook's nudge. Triggers on "persist durable facts from this session", "run memory-extraction", "flush session memory", or "extract memory before close". Reads the in-context transcript plus existing memory, applies the durable-fact predicate below, and writes one file per fact plus one MEMORY.md index line — append-or-update, never clobbering a consumer's existing memory.

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Guides the practical path from 'we have no design system' or 'we have a mess' to a real, functional design system. Component inventory, design token extraction, headless UI adoption (Radix, React Aria, Headless UI), migration order of operations, and the assessment-to-rollout pipeline. Activate on 'design system from scratch', 'bootstrap design system', 'no design system', 'design system migration', 'headless UI adoption', 'component audit', 'design token extraction', 'design system strategy'. NOT for building specific components once a system exists (use design-system-creator), not for generating token files/configs (use design-system-generator).

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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).

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Expert-level separation processes covering distillation, absorption, extraction, crystallization, membrane separations, and adsorption for chemical engineering applications.

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Web search, content extraction, crawling, and deep research via the Tavily CLI. Use this skill whenever the user wants to search the web, find articles, research a topic, look something up online, extract content from a URL, grab text from a webpage, crawl documentation, download a site's pages, discover URLs on a domain, or conduct in-depth research with citations. Also use when they say "fetch this page", "pull the content from", "get the page at https://", "find me articles about", or reference extracting data from external websites. This provides LLM-optimized web search, content extraction, site crawling, URL discovery, and AI-powered deep research — capabilities beyond what agents can do natively. Do NOT trigger for local file operations, git commands, deployments, or code editing tasks.

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AI-powered autonomous data extraction that navigates complex sites and returns structured JSON. Use this skill when the user wants structured data from websites, needs to extract pricing tiers, product listings, directory entries, or any data as JSON with a schema. Triggers on "extract structured data", "get all the products", "pull pricing info", "extract as JSON", or when the user provides a JSON schema for website data. More powerful than simple scraping for multi-page structured extraction.

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Deploy and use the Exasol Text AI Extension (TXAIE) with notebook-connector. Covers `deploy_license`, `initialize_text_ai_extension`, the `PYTHON3_TXAIE` language container, the Extraction API classes `StandardExtractor`, `NamedEntityExtractor`, `TopicClassifierExtractor`, `PipelineExtractor`, and `BranchExtractor`, named-entity, zero-shot classification, and feature extraction runs, default-model installation, and result-table querying such as `TXAIE_AUDIT_LOG`.

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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".

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Detect model stealing, model inversion, and membership inference performed through inference-API abuse by monitoring query patterns, applying output perturbation, and red-teaming your own model's extractability.

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Reads a process flow diagram (image, PDF or native export) with its job header and returns a provenance-tagged extraction register: equipment, streams, tags, connectivity, instruments already shown, notes and legends, connectivity findings and an explicit UNKNOWN list, then holds at gate G1. Extracts and records only; never enriches, tags or proposes. Use when the user asks to "start a PFD to P&ID job", "read in the attached PFD", "extract the equipment and streams from this PFD" or "read this PFD into a register". Do not use for checking the model, use process-model-check instead; do not use to propose lines, instruments or tags, use piping-enrichment or instrumentation-and-control-enrichment after gate G2. Drafts for human review; never approves, authorises or signs off.

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Checks an extracted PFD register as a process model and returns findings and unknowns only: connectivity closure, stream numbering, presence of design conditions, equipment duty text, phase and service labels, package and off-sheet scope, boundaries, operating cases, and agreement between the drawing and attached process documents, then holds at gate G2. Creates no design values. Use when the user asks to "check the process model", "verify connectivity" or "is the PFD ready for enrichment" after gate G1 and before enrichment. Do not use for extraction from the drawing, use pfd-intake-and-extraction instead; do not use to propose lines or instruments, use piping-enrichment. Drafts for human review; never approves, authorises or signs off.

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Exhaustive idea extraction through recursive breadth-before-depth exploration with Deep Think protocol and fractal structure. Use during /ideate to transform raw ideas into comprehensive, structured ideation output. Writes to a fractal folder hierarchy — every node has an index, CX file, and children.

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Crawl and extract a bounded set of pages from one website through Tavily. Use for documentation downloads, site-section collection, or semantic multi-page extraction when map plus individual extract calls are insufficient.

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Build or review production-ready Tavily SDK and API integrations for web search, extraction, crawling, mapping, and research. Use when implementing Tavily in an agent, RAG pipeline, or application rather than only running one CLI command.

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Route Tavily web-search, extraction, mapping, crawling, and cited-research requests to the narrowest `tvly` command. Use for command-line Tavily work, installation checks, authentication setup, or choosing among the specialized Tavily skills.

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CLI tool for AI-powered web scraping, data extraction, search, and crawling via ScrapeGraph AI. Use when the user needs to scrape websites, extract structured data from URLs, convert pages to markdown, crawl multi-page sites, search the web for information, automate browser interactions (login, click, fill forms), get raw HTML, discover sitemaps, or generate JSON schemas. Triggers on tasks involving: (1) extracting data from websites, (2) web scraping or crawling, (3) converting webpages to markdown, (4) AI-powered web search with extraction, (5) browser automation, (6) generating output schemas for scraping. The CLI is just-scrape (npm package just-scrape).

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Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

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Use when designing how an Agentforce agent extracts structured input parameters (slots) from a user's natural-language utterance to invoke an action. Triggers: 'agent extract date from user', 'agent action argument extraction', 'agentforce slot filling', 'agent invocable input mapping', 'agent fails to fill required parameter'. NOT for action authoring (use agentforce/agent-actions) or for prompt-template variable binding (use agentforce/prompt-builder-templates).

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Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library

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Integrate Iteration Layer APIs for document, website, image, and sheet processing. Composable APIs — Document Extraction, Website Extraction, Document to Markdown, Image Transformation, Image Generation, Document Generation, and Sheet Generation — share one credit pool and chain together into workflows.

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Extract structured data from documents using AI-powered field extraction.

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Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

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Extract structured domain knowledge from AI models in-session or from local open-source models via Ollama. No API key needed.

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