「pipeline」の検索結果

Implement reference architecture for Anima design-to-code automation. Use when designing a design system automation pipeline, structuring a Figma-to-React project, or planning team-scale design handoff. Trigger with: "anima architecture", "design-to-code architecture", "anima project structure", "figma automation architecture".

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 Figma webhooks to trigger automatic Anima code generation on design changes. Use when building event-driven design-to-code pipelines, auto-generating components when Figma files change, or integrating design updates into CI. Trigger with: "anima webhook", "figma webhook", "anima auto-generate on change".

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 a reliable Algolia indexing pipeline for full replacements, incremental updates, settings, synonyms, and rules. Use when a source of truth must publish deterministic search state. Trigger with "Algolia indexing workflow", "replace index records", or "sync Algolia".

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。

Gate Alchemy integration changes with deterministic unit, contract, fork, secret, and negative-path checks. Use when adding Alchemy to CI or hardening an existing pipeline. Trigger with "Alchemy CI", "Alchemy GitHub Actions", or "test Alchemy in CI".

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。

End-to-end external OSINT engagement autopilot. Run the FULL osint-methodology pipeline to completion in ONE go for an authorized domain — engagement folder, Stages 1-5 (seed, expansion, enrichment, exposure, convergence), multi-agent per-host content+JS fan-out, headline verification, auto-generated findings, and a consolidated multi-tab .xlsx deliverable. Stops ONLY for Stage 6 (active exploitation) arming and out-of-scope sibling assets. Use whenever asked to "run OSINT / recon / attack-surface" on a target — do NOT deliver a thin passive first pass.

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。

Comprehensive OSINT methodology for external red-team operations and authorized attack-surface assessments. Covers the 6-stage recon pipeline (seed → asset expansion → enrichment → exposure analysis → convergence → operator-armed active validation) with connector-resilience and stage-vs-gating discipline, asset-graph discipline, severity rubric, confidence upgrade workflows, time budgeting, identity-fabric mapping, breach×identity correlation with per-person identity dossiers, detectability tagging, detection-aware probing, WAF/CDN bypass, vulnerability prioritization, phishing infrastructure planning, bug bounty submission, and client deliverable templates. Use when planning or executing reconnaissance against authorized targets, mapping an organization's external attack surface, investigating a person/entity, or producing client deliverables.

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。

LLM integration patterns including API usage, streaming, function calling, RAG pipelines, and cost 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。

Data engineering patterns for ETL pipelines, data warehousing, Apache Spark, and data quality validation

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。

CI/CD pipeline design with GitHub Actions, Docker, Kubernetes, Helm, and GitOps patterns

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。

Guides performance investigation, benchmarking, and optimization of the Grida Canvas web editor (TypeScript reducer, Immer, React hooks). Use when profiling reducer dispatch cost, diagnosing slow interactions (drag, resize, color change), writing or running editor benchmarks, instrumenting with PerfObserver, or optimizing the JS-side state management pipeline.

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。

Guides work on the Figma I/O package (@grida/io-figma, packages/grida-canvas-io-figma/). Covers the fig-kiwi binary parser, Kiwi→REST→Grida conversion pipeline, fig2grida CLI, REST API JSON conversion, and testing with clipboard/fig/REST fixtures. Use when adding node type support, fixing conversion bugs, extending fig2grida, working on the fig-kiwi parser, writing tests for Figma import, or debugging clipboard paste failures after a Figma update.

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 this skill whenever the user wants to generate detailed English prompts for AI image tools (NanoBanana / Gemini / DALL-E / Midjourney) to produce top-conference-quality academic figures — including framework diagrams, network architecture diagrams, pipeline flowcharts, module detail diagrams, comparison/ablation figures, and data pattern grids — especially when the user says "论文配图提示词", "生成论文配图", "学术论文生图", "架构图提示词", "框架图提示词", "顶会风格配图", "CVPR 风格图", "NeurIPS 风格图", "paper figure prompt", "academic diagram prompt", or provides a LaTeX/PDF/Word paper and asks for figure prompts. If the user has not specified a color scheme, present the 8 preset palette options and color tool links before generating any prompt.

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。

Upgrade Three.js games from prototype visuals to premium browser graphics: art-direction critique, procedural model building, material and texture libraries, world prop kits, shaders, VFX, lighting and render pipeline, LOD and instancing, render budgets, and a 10-category visual scorecard. Use when screenshots still look basic or the user asks for premium, AAA, high-fidelity, showcase, or less-basic graphics.

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。

Create, edit, analyze, or convert Excel spreadsheets (.xlsx, .xlsm, .xltx) where the workbook file is the primary deliverable. Use for formulas, formatting, financial models, multi-sheet workbooks, and tabular cleanup exported to Excel. Also applies to .csv/.tsv when the user wants spreadsheet output. Do NOT use for Word documents, HTML reports, standalone Python scripts, database pipelines, or Google Sheets API work.

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。

Insecure file upload playbook. Use when testing upload validation, storage paths, processing pipelines, preview behavior, overwrite risks, and upload-to-RCE chains.

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。

HTTP request smuggling and desynchronization testing. Use when front proxies, CDNs, or load balancers disagree with the origin on message framing (Content-Length vs Transfer-Encoding), on HTTP/2→HTTP/1 translation, or when exploring client-side desync via browser fetch pipelines.

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。

Command injection playbook. Use when user input may reach shell commands, process execution, converters, import pipelines, or blind out-of-band command sinks.

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。

Entry P1 category router for file access and upload workflows. Use when testing download endpoints, file paths, local file inclusion, upload flows, preview pipelines, archive extraction, or storage and sharing boundaries.

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 production-ready systems with stability patterns: circuit breakers, bulkheads, timeouts, and retry logic. Use when the user mentions "production outage", "circuit breaker", "deployment pipeline", "chaos engineering", "retry storm", "health checks", "my service keeps crashing", "prevent cascading failures", or "make it resilient". Also trigger when designing resilient microservices, planning zero-downtime deployments, or capacity-planning for peak load. Covers stability patterns, capacity planning, deploy/release decoupling, and observability. For data systems, see ddia-systems. For system architecture, see system-design.

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 data systems by understanding storage engines, replication, partitioning, transactions, and consistency models. Use when the user mentions "database choice", "which database should I use", "SQL or NoSQL", "replication lag", "partitioning strategy", "consistency vs availability", "stream processing", "ACID transactions", "eventual consistency", "my queries are slow at scale", or "data is inconsistent across replicas". Also trigger when choosing a datastore, designing data pipelines, or debugging distributed-system consistency issues. Covers data models, batch/stream processing, and distributed consensus. For system design, see system-design. For resilience, see release-it.

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。

Guided journey from a business with lucky months to a repeatable growth engine that produces a forecast. Orchestrates nine skills phase by phase - one-page-marketing, hundred-million-offers, predictable-revenue, contagious, influence-psychology, crossing-the-chasm, cold-start-problem, lean-analytics, negotiation - asking the user questions at every decision point and recording results in the project docs/ folder (MARKETING.md, OFFER.md, METRICS.md, GROW-BUSINESS-PLAN.md) so the journey resumes across sessions. Use when the user wants to build a marketing and sales system, manufacture predictable pipeline, engineer word-of-mouth and referrals, or says 'revenue is real but lumpy and I need growth that repeats'. If the offer, retention, or operations underneath are broken, run improve-business first; with no paying customers yet, start with create-business; when the product itself must carry the growth loops, use grow-app. For one framework in isolation, invoke that skill directly.

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。

Deep cultural adaptation of an already translated blog post for one target market: local examples, CTAs, legal references, statistic sources, and formality (Sie/du, tu/vous). Built-in profiles for DACH, Francophone, Hispanic, and Japanese markets. Use when the user says "localize blog", "cultural adaptation", "adapt for Germany", "lokalisieren", "localiser", "adaptar" and a translation already exists. Not for translating (use blog-translate), auditing a folder of translations (use blog-locale-audit), or the full write-translate-localize pipeline (use blog-multilingual).

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。

One-command multilingual blog creation. Writes a blog post, translates it into user-specified languages, applies cultural adaptation, and emits hreflang tags, sitemap entries, and a CMS-ready language map. The complete write-to-publish pipeline for international content. Orchestrates blog-write, blog-translate, blog-localize, and (optionally) seo-hreflang. Use when user says "multilingual blog", "blog multilingual", "write in multiple languages", "international blog", "mehrsprachiger Blog", "blog multilingue", "blog multilingue", "create blog in German and French".

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。

Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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。