「processing」の検索結果

Fix Elixir/Erlang AST processing bugs in Grafema beam-analyzer. Use when: (1) Elixir parser returns MODULE node but 0 functions/calls — body nesting issue, (2) Erlang parser crashes with "cannot convert list to string" on OTP 26+ — location format changed from integer to keyword list, (3) pipe operator |> creates spurious CALL nodes instead of desugared function calls — clause ordering bug, (4) multi-module .ex files return only the first module — missing __block__ handler, (5) installing Erlang/Elixir on macOS with outdated Xcode/Clang.

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Design and build LLM-powered projects from ideation through deployment. Use when starting new agent projects, choosing between LLM and traditional approaches, or structuring batch processing pipelines.

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Use when receiving emails with Resend - setting up inbound domains, processing email.received webhooks, retrieving email content/attachments, or forwarding received emails. Triggers on "inbound email", "receive email", "email webhook", "Resend inbound", "process incoming email", "email forwarding", "email.received", "MX records for email".

日本語の概要は準備中です。原文の説明を表示しています。

Build AI invoice processing systems. TRIGGERS - Use when user needs help with ai-invoice-processor related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Build AI image processing pipelines for generation and analysis. TRIGGERS - Use when user needs help with ai-image-pipeline related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Design document processing pipelines for extraction and routing. TRIGGERS - Use when user needs help with ai-document-processor related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Build AI batch processing systems for scale. TRIGGERS - Use when user needs help with ai-batch-processor related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Build AI audio processing pipelines. TRIGGERS - Use when user needs help with ai-audio-processor related tasks.

日本語の概要は準備中です。原文の説明を表示しています。

Use when handling external API rate limits (e.g., OpenAI 429s, HubSpot or Stripe rate limits), preventing duplicate work from rapid event bursts (debouncing user actions), spreading load over time, ensuring per-tenant fairness, processing events in batches, limiting concurrent runs of the same operation, or assigning priority to important runs. Covers Inngest flow control: concurrency limits with keys, throttling, rate limiting, debounce, priority, singleton, and event batching.

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End-to-end meeting workflow skill. Pre-meeting: research attendees, build agenda, prepare talking points, create pre-read. Post-meeting: process notes into action items, draft follow-up emails, log decisions, update project notes. Useful for preparing for important calls, during post-meeting processing, when following up with attendees, and for building a running decision log. Use when the user says "prepare for meeting", "meeting prep", "process meeting notes", "meeting follow up", "meeting machine", or "prep for my call".

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Shows you how to use Cowork's parallel workers to run multiple tasks at the same time instead of one after another — dramatically cutting the time on batch work. For processing large batches of files or research tasks, when running multiple independent analyses simultaneously, or during a performance optimization session. Use when the user says "speed up", "parallel workers", "batch task", "process multiple files", "why is cowork so slow", "faster", "do several things at once", or "can cowork multitask".

日本語の概要は準備中です。原文の説明を表示しています。

Use when the user asks about CTI standard operating procedures (daily triage, IOC processing, flash-report cadence, threat-actor profile updates, briefing schedule), or wants to look up a specific SOP.

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Build Shopify applications, extensions, and themes using GraphQL/REST APIs, Shopify CLI, Polaris UI components, and Liquid templating. Capabilities include app development with OAuth authentication, checkout UI extensions for customizing checkout flow, admin UI extensions for dashboard integration, POS extensions for retail, theme development with Liquid, webhook management, billing API integration, product/order/customer management. Use when building Shopify apps, implementing checkout customizations, creating admin interfaces, developing themes, integrating payment processing, managing store data via APIs, or extending Shopify functionality.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image, editing, composition, refinement). Use when working with audio/video files, analyzing images or screenshots, processing PDF documents, extracting structured data from media, creating images from text prompts, or implementing multimodal AI features. Supports multiple models (Gemini 2.5/2.0) with context windows up to 2M tokens.

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Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.

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Expert fintech engineer specializing in financial systems, regulatory compliance, and secure transaction processing. Masters banking integrations, payment systems, and building scalable financial technology that meets stringent regulatory requirements.

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Draft privacy policies, terms of service, disclaimers, and legal notices. Creates GDPR-compliant texts, cookie policies, and data processing agreements.

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Implement GDPR data protection requirements. Configure consent management, data subject rights, and privacy by design. Use when processing EU personal data.

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Generate production-ready business documents including freelance contracts, project proposals, SOWs, NDAs, and MSAs with jurisdiction-aware clauses. Covers US (Delaware), EU (GDPR), UK, and DACH (German law) legal frameworks. Includes contract templates, clause libraries, and DOCX conversion. Use when starting client engagements, writing proposals, drafting partnership agreements, or needing GDPR-compliant data processing addenda.

日本語の概要は準備中です。原文の説明を表示しています。

Post-process PFC 5.0 asphalt CSV and image exports into reproducible curves, fields, rose diagrams, animations, and summary tables without relying on a local PFC installation.

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Adapt and audit a PFC 5.0 asphalt Marshall stability/flow simulation using the external fistPkg26 dc.fis two-head controller as a starting skeleton; use for curved-head geometry, PFC5 wall/contact kinematics, load-deformation histories, peak stability, edition-specific flow processing, and validation against laboratory data.

日本語の概要は準備中です。原文の説明を表示しています。

"Apply dual-process theory to diagnose whether judgments arise from fast intuitive (System 1) or slow analytical (System 2) processing and identify resulting cognitive biases. Use this skill when the user needs to explain why quick decisions go wrong, design choice architectures that account for cognitive defaults, audit decision processes for heuristic errors, or when they ask 'why do people misjudge probability', 'how to reduce snap-judgment errors', or 'when does intuition fail'.".

日本語の概要は準備中です。原文の説明を表示しています。

Distributed computing for larger-than-RAM pandas/NumPy workflows. Use when you need to scale existing pandas/NumPy code beyond memory or across clusters. Best for parallel file processing, distributed ML, integration with existing pandas code. For out-of-core analytics on single machine use vaex; for in-memory speed use polars.

日本語の概要は準備中です。原文の説明を表示しています。

"Optimize e-commerce search relevance across the full pipeline from query understanding to result presentation. Use this skill when the user needs to improve search quality, implement query processing features, or diagnose search relevance issues — even if they say 'search results are bad', 'improve product search', or 'search relevance optimization'.".

日本語の概要は準備中です。原文の説明を表示しています。