「graph」の検索結果

Build robust backend systems with modern technologies (Node.js, Python, Go, Rust), frameworks (NestJS, FastAPI, Django), databases (PostgreSQL, MongoDB, Redis), APIs (REST, GraphQL, gRPC), authentication (OAuth 2.1, JWT), testing strategies, security best practices (OWASP Top 10), performance optimization, scalability patterns (microservices, caching, sharding), DevOps practices (Docker, Kubernetes, CI/CD), and monitoring. Use when designing APIs, implementing authentication, optimizing database queries, setting up CI/CD pipelines, handling security vulnerabilities, building microservices, or developing production-ready backend systems.

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。

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.

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。

Standards and audits for writing academic papers, especially ML/NLP submissions to NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL. Covers the author's house prose rules (zero em dashes, zero fluff, every sentence earns its place), verification of hallucinated claims and hallucinated or malformed bibliography entries, the ACL policy on AI writing assistance and what must be disclosed, and best practices for paper structure, experiments, reproducibility checklists, and rebuttals. Use this skill whenever the user is drafting, editing, polishing, restructuring, or reviewing a paper, abstract, intro, related work, rebuttal, or camera-ready; whenever they ask to "clean up" or "tighten" academic writing; whenever citations or a .bib file need checking; and whenever they ask what has to be disclosed about AI assistance. Reach for it even when the user only says "make this section better", "check my references", or "is this ready to submit".

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。

Defines and applies visual brand — logo usage, palette, typography, imagery direction, and the guidelines that keep expression consistent across product and marketing surfaces. Use this to establish a brand from scratch, audit whether an existing brand is being applied consistently, build brand guidelines, or decide how a brand should express itself in an unfamiliar format.

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。

Decides whether and how to enter a new market — sizing demand from the bottom up rather than from a market report, testing whether your advantage transfers, choosing between organic entry, partnership and acquisition, sequencing the operational and regulatory work that entry actually requires, and setting the criteria that would tell you to stop. Use this to evaluate a new geography, segment or vertical, pressure-test an entry plan, or work out why a launched market never reached scale.

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。

Universal UI/UX design principles covering visual hierarchy, interaction laws, typography foundations, and WCAG accessibility requirements. Use when making design decisions not covered by a specific design system, validating principle compliance, or resolving conflicts between aesthetics and accessibility. Design-system-agnostic and applies to every surface.

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。

Generate, extend, or audit design tokens in DTCG format with the 3-tier architecture (primitive → semantic → component). Use when the user wants a color palette, type scale, spacing/shadow/radius/motion tokens, multi-brand theming, or wants to validate token files. Covers colors, typography, spacing, shadows, borders, breakpoints, motion, gradients, opacity, blur, sizing, states, theming.

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。

Enforce modern Chinese writing standards including tone, spacing rules (Pangu), full-width punctuation, paragraph structure, and active voice. Provides specific guidelines for blog posts, error messages, UI text, and technical documentation.

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。

Amap (Gaode) Maps comprehensive service for POI search, route planning, travel planning, nearby search, heatmap visualization, and geocoding. Use when user wants to search locations, plan routes, find nearby places, or visualize geographic 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。

Helps organize reviewer comments and generate a standardized Word (.docx) response letter that maps each change to its exact location (page/paragraph/line). Use when revising a manuscript, replying to peer-review feedback, or preparing internal review responses.

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 creating journal cover images, generating scientific artwork prompts, or designing graphical abstracts. Creates detailed prompts for AI image generators to produce publication-quality scientific visuals.

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 you need a standardized R CLI workflow to build a protein-protein interaction network from a local gene list and an offline STRING cache, export node and edge tables, and render a reproducible PDF network plot. NOT for online API fetching, arbitrary graph databases, multi-omics integration, or non-STRING interaction sources.

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 generating Sankey or alluvial plots from sample annotation tables where rows are samples and selected columns are categorical stages such as risk group, response status, subtype, or cohort labels. NOT for: gene network flow analysis, continuous-value trajectories, or graph-structured pathway visualization.

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。

Acknowledges limitations in sample, design, measurement, and validation in a professional way that improves credibility without undermining the whole paper. Use when writing the limitations paragraph of a Discussion section, preparing a grant risk assessment, responding to reviewers about study weaknesses, or framing scope boundaries for a paper. Also triggers on "write my limitations", "how should I address the limitation of", "reviewer said my sample is too small", or "help me word this limitation".

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。

Writes complete, publication-grade figure legends that can stand on their own. Use when writing or revising figure legends for any scientific figure — bar charts, line graphs, scatter plots, box plots, heatmaps, survival curves, flow cytometry plots, western blots, microscopy images, or schematic diagrams. Also triggers on "write a figure legend for", "help me describe this figure", "my figure needs a legend", "write Figure 1 legend", or "what should a figure legend include".

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。

Rewrites technical research content into a structured lay summary that cross-disciplinary teams can quickly understand and act on. Use when the user wants to explain research to colleagues outside their specialty — clinicians, wet-lab scientists, bioinformaticians, product managers, or leadership. Trigger on: "lay summary", "explain my research to the team", "non-technical summary", "cross-disciplinary summary", "translate my findings", "align our team on the study", or any request to communicate research goals, findings, or next steps to a mixed or non-specialist audience. Part of the AIPOCH Academic Writing skill hub. Sits midstream: after research content is clarified, before downstream deliverables like slide decks or graphical abstracts.

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。

Composes a Discussion around key findings, mechanisms, clinical relevance, and limitations. Use when writing or improving a Discussion section for any biomedical manuscript — including interpreting results, connecting to prior literature, addressing unexpected findings, framing limitations, and writing the conclusion. Also triggers on "write my discussion", "help me discuss my findings", "how do I compare to prior studies", "write the limitations paragraph", or "draft a discussion for my paper".

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 profiling CPU/memory hot paths, generating flame graphs, or capturing JFR/perf evidence.

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。

Emulated Microsoft Entra ID (Azure AD) OAuth 2.0 / OpenID Connect for local development and testing. Use when the user needs to test Microsoft sign-in locally, emulate Entra ID OIDC discovery, handle Microsoft token exchange, configure Azure AD OAuth clients, work with Microsoft Graph /me, or test PKCE/client credentials flows without hitting real Microsoft APIs. Triggers include "Microsoft OAuth", "Entra ID", "Azure AD", "emulate Microsoft", "mock Microsoft login", "test Microsoft sign-in", "Microsoft OIDC", "local Microsoft auth", or any task requiring a local Microsoft OAuth/OIDC provider.

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。

Emulated Linear GraphQL API for local development and testing. Use when the user needs to test Linear integrations locally, emulate Linear issues, comments, teams, workflow states, OAuth apps, webhooks, agent sessions, or work with the Linear API without hitting the real Linear service. Triggers include "Linear API", "emulate Linear", "mock Linear", "test Linear OAuth", "Linear webhook", "Linear agent", "local Linear", or any task requiring a local Linear API.

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。

Redesign GitHub README homepages or create project-native pure SVG, hybrid SVG-composed PNG/WebP, and opt-in animated GIF assets. Use when a user asks to beautify, redesign, rebrand, visually upgrade, simplify, or audit a GitHub README; create only a hero, section headers, diagrams, badges, motion graphics, showcase modules, or other README assets; or turn a repository homepage into a cohesive visual story. If whole-README work versus asset-only work is unclear, ask which scope the user wants. For hero-like assets where pure SVG and generated raster material are both viable, explain the tradeoffs and confirm the implementation before creating the asset.

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 for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Explore an existing PostgreSQL database to understand its objects and relationships - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates - Plan or execute safe schema migrations with zero downtime **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration

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。

Search and retrieve content from Product Hunt. Get posts, topics, users, and collections via the GraphQL API. Use when user mentions Product Hunt, PH, or product launches.

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。