1,120 件 ・ 関連度順
概要と使いどころ
Fix NIP-46 nostrconnect:// URLs not being recognized by signer apps (Amber, nsec.app, nsecBunker). Use when: (1) QR code or URL paste fails in signer app despite valid-looking URL, (2) Using metadata JSON object instead of separate query params, (3) Implementing client-initiated NIP-46 connections. The NIP-46 spec requires name/url/image as separate query parameters, not a metadata JSON object.
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。
Add custom metadata headers (x-amz-meta-*) to AWS v4 signed requests for GCS S3-compatible API. Use when: (1) Adding custom metadata to GCS uploads via S3 API, (2) Getting signature mismatch errors after adding new headers, (3) x-amz-meta-* headers being ignored or causing 403 errors. Custom headers MUST be included in canonical headers and signed headers list.
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。
Automatically translate and sync App Store metadata (description, keywords, what's new, subtitle) to multiple languages using LLM translation and asc CLI. Use when asked to localize an app's App Store listing, translate app descriptions, or add new languages to App Store Connect.
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 or update a complete repository skill from a user's idea, including the workflow instructions, references, scripts or assets, agent metadata, skill-card artwork, cinematic banner artwork, README links, discovery metadata, and validation. Use when the user asks to create a new skill, add a skill to this collection, turn a workflow into a reusable skill, or make a skill's documentation and artwork consistent with the repository.
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。
CustomShader authoring — vertexShaderText and fragmentShaderText against VertexInput, FragmentInput, FeatureIds, Metadata, czm_modelMaterial. Use when reading EXT_mesh_features or EXT_structural_metadata property textures/tables, vertex displacement, or shading VoxelPrimitive.
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。
Battle-hardened NFT developer specializing in ERC-721/1155 implementations, gas-optimized minting, reveal mechanics, and marketplace integration. Has launched 50+ collections from stealth 1/1s to 10k PFP drops.Use when "build nft contract, mint function, erc721 implementation, erc1155 implementation, nft reveal, allowlist mint, dutch auction, lazy mint, on-chain metadata, royalty enforcement, operator filter, batch mint gas, token uri, soulbound token, nft, erc721, erc1155, erc721a, solidity, smart-contracts, opensea, blur, metadata, ipfs, arweave, royalties, minting, reveal, allowlist" mentioned.
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。
数据工程 — 数据平台从业者的认知操作系统, 覆盖把数据从源系统搬运成可靠 / 可查询 / 可信赖形态供分析 / ML / 数据产品消费的全生命周期 (生成 → 摄取 → 存储 → 转换 → 服务 + 安全/数据管理/DataOps/数据架构/编排/软件工程 六条暗流, Reis & Housley 框架): 摄取与集成 (批 + CDC 变更数据捕获 Debezium + EL 工具 Fivetran/Airbyte/Meltano/dlt + Kafka Connect + schema drift) / 存储与文件表格式 (对象存储数据湖 + 列存 Parquet/ORC/Arrow/Avro + 开放表格式 Apache Iceberg/Delta Lake/Apache Hudi + lakehouse + 分区/compaction) / 转换与建模 (ELT dbt/SQLMesh + Spark + 维度建模 Kimball + Inmon + Data Vault + 大宽表 OBT + 渐变维 SCD + 增量模型 + 语义/指标层) / 编排与工作流 (Apache Airflow/Dagster/Prefect/Mage/Kestra/Apache DolphinScheduler + DAG + 幂等 + 回填 backfill + 数据资产调度) / 批流与实时 (Apache Kafka/Apache Flink/Spark Structured Streaming/Kinesis/Pulsar/Redpanda + Lambda vs Kappa + watermark/窗口/exactly-once + 流式 SQL Materialize/RisingWave + 实时 OLAP ClickHouse/Apache Druid/Apache Pinot/StarRocks/Apache Doris) / 数仓与查询引擎 (Snowflake/BigQuery/Redshift/Databricks SQL/Trino/Presto/DuckDB/Polars + 存算分离 + MPP) / 数据质量测试与可观测性 (dbt tests/Great Expectations/Soda + 数据契约 + Monte Carlo data downtime + 新鲜度/量/schema 异常检测) / 数据治理编目与血缘 (DataHub/Amundsen/OpenMetadata/Unity Catalog + 列级血缘 + PII 分类 + 访问控制 + GDPR) / DataOps 与可靠性 (数据 CI/CD + 转换版本控制 + 环境隔离 + 幂等重处理 + 数据 SLA/SLO + 计算存储 FinOps) / 数据架构范式 (现代数据栈 + lakehouse + data mesh + data fabric + 去中心化 vs 中心化所有权) / 分析工程角色 (dbt 时代连接数据工程与分析的桥) — 不含 数据科学/ML 建模本身 (是下游消费者) / BI 仪表盘制作 (serving 下游) / 数据分析报表为终点 / 'data engineer = 跑 Hadoop 的' 过时窄化 / 通用后端应用开发 (平行学科) (Data Engineering — the cognitive operating system of practitioners who design, build, and operate the data platform: moving data from source systems into reliable, queryable, trustworthy form for analytics / ML / products, covering (a) the data engineering lifecycle (generation → ingestion → storage → transformation → serving, with the undercurrents security / data management / DataOps / data architecture / orchestration / software engineering — Reis & Housley framing), (b) ingestion & integration (batch + CDC change-data-capture with Debezium, EL tools Fivetran / Airbyte / Meltano / dlt, Kafka Connect, API + file + database sources, schema drift handling), (c) storage & file/table formats (object storage data lakes, columnar formats Parquet / ORC / Arrow / Avro, open table formats Apache Iceberg / Delta Lake / Apache Hudi, lakehouse architecture, partitioning / compaction / Z-ordering), (d) transformation & modeling (ELT with dbt / SQLMesh, Spark, dimensional modeling Kimball, Inmon CIF, Data Vault, One Big Table / wide tables, normalization vs denormalization, slowly changing dimensions, incremental models, the semantic / metrics layer), (e) orchestration & workflow (Apache Airflow, Dagster, Prefect, Mage, Kestra, Apache DolphinScheduler, DAGs, idempotency, backfills, data-aware / asset-based scheduling), (f) batch vs streaming & real-time (Apache Kafka, Apache Flink, Spark Structured Streaming, Kinesis / Pulsar / Redpanda, the Lambda vs Kappa debate, watermarks / windowing / exactly-once, streaming SQL Materialize / RisingWave, real-time OLAP ClickHouse / Apache Druid / Apache Pinot / StarRocks / Apache Doris), (g) warehouses & query engines (Snowflake, BigQuery, Redshift, Databricks SQL, Trino / Presto, DuckDB, Polars, decoupled storage & compute, MPP), (h) data quality, testing & observability (dbt tests, Great Expectations, Soda, data contracts, Monte Carlo / data downtime, freshness / volume / schema anomaly detection, unit / integration testing of pipelines), (i) data governance, catalog & lineage (DataHub, Amundsen, OpenMetadata, Unity Catalog, column-level lineage, PII / data classification, access control, GDPR / data privacy), (j) DataOps & reliability (CI/CD for data, version control of transformations, environments, idempotent reprocessing, SLAs / SLOs for data, cost / FinOps for compute & storage), (k) data architecture paradigms (modern data stack, data lakehouse, data mesh, data fabric, decentralized vs centralized ownership), (l) the analytics engineering role (the dbt-era bridge between data engineering and analysis); N
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。
Image processing toolkit awareness. Use when: user uploads images for manipulation, requests format conversion, batch processing, compositing, resizing, optimization, analysis, effects, metadata inspection, montages, animated GIFs, color correction, or any image-related task. Also use when working with screenshots, photos, diagrams, icons, or visual assets. Triggers on 'resize', 'crop', 'convert', 'compress', 'optimize', 'thumbnail', 'watermark', 'montage', 'collage', 'gif', 'sprite sheet', 'color space', 'metadata', 'EXIF', 'compare images', 'diff', 'overlay', 'composite', 'batch process', 'image analysis', 'histogram', 'blur', 'sharpen', 'rotate', 'flip', 'border', 'shadow', 'round corners', 'favicon', 'icon set'.
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。
Image optimization analysis for SEO and performance. Checks alt text, file sizes, formats, responsive images, lazy loading, CLS prevention, image SERP rankings (via DataForSEO), and image file optimization (WebP/AVIF conversion, IPTC/XMP metadata injection). Use when user says "image optimization", "alt text", "image SEO", "image size", "image audit", "optimize images", "image metadata", "image SERP", "convert to webp", or "image file optimize".
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。
Suede Labs skill that lints a local music or media release folder and scores it for release readiness: missing files, weak or malformed metadata, artwork and stem problems, split gaps, rights blockers, and platform-delivery issues, produced by a bundled offline script as a scored markdown and JSON report. Use when a creator has one or more release folders of tracks, artwork, and stems and wants to know what is missing before distributing, delivering, or handing it off; when metadata quality or a delivery rejection is the question; or when a release-readiness score is asked for. Reports what is present, missing, or unknown; never upgrades unknown to confirmed and never means legal clearance. NOT FOR: organizing the evidence behind a rights or split gap the report surfaces (use suede-rights-audit); building the transfer package (use suede-rights-passport); a sync one-sheet (use suede-sync-packaging).
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。
Suede Labs skill that finds and organizes the rights gaps in a creator project before packaging: ownership, contributors, splits, samples, licenses, provenance, metadata, licensing readiness, and royalty-routing readiness, each marked confirmed or unknown against an evidence trail. Use when a song, release, or creative project needs a rights check before registry, licensing, sync, or payout discussion; when splits, sample clearance, or chain of title are unclear; or when someone asks whether they have the rights to release, license, or get paid for a work. Organizes evidence only: clears no rights, confirms no ownership, moves no money, writes to no registry. NOT FOR: building the transfer package itself (use suede-rights-passport); linting a release folder's files and metadata (use suede-release-linter); a sync one-sheet or pitch (use suede-sync-packaging).
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。
Read and extract content from PDF files — text, tables, metadata, and images. Use when asked to read a PDF, extract text from a PDF, summarize a PDF, analyze a PDF document, get tables from a PDF, or check PDF metadata. Also triggers on "open this PDF", "what does this PDF say", "parse PDF", "PDF to text", or when a .pdf file path or URL is provided.
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, validate, and explain `auth.md` files — the open protocol for AI agent registration. Use when making apps agent-ready, generating Protected Resource Metadata (RFC 9728), validating auth.md files, or implementing agent registration endpoints. Triggers on "auth.md", "agent registration", "agent auth", "make my app agent-ready", "ID-JAG", "identity_assertion", "service_auth", "protected resource metadata", "agentic registration".
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。
Expert guide for Monte Carlo's push ingestion model. Use this skill whenever a customer or engineer mentions: pushing data to Monte Carlo, the IngestionService, pycarlo push APIs, build me a collection script, push metadata/lineage/query logs, invocation_id tracing, custom lineage nodes or edges, deleting push tables, or any question about why pushed data is not showing up. Also trigger when they ask to generate code that collects metadata, table schema, row counts, freshness, lineage, or query history from any data warehouse or data source and sends it to Monte Carlo. If the user mentions any warehouse, database, or data platform alongside any Monte Carlo topic, this skill is almost certainly relevant.
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。
Query patent simple bibliographic (catalog) data from the Nexscope patent database. Triggered when users mention patent bibliographic information queries, patent basic info retrieval, patent catalog data, patent publication details, searching by patent number for inventors, patent applicant information, patent abstract retrieval, patent classification codes (IPC/CPC), patent citation queries, or any request to search for structured metadata by patent ID or publication number, patent brief bibliography, patent basic info, patent number lookup, patent abstract, Nexscope, or patent metadata. Even if the user does not explicitly mention "Nexscope" or "bibliographic info," this skill should be triggered whenever their need involves querying core bibliographic fields of specific patents.
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。
Query patent bibliographic (catalog) information from the Nexscope patent database by patent ID or publication number. Triggered when users mention patent bibliographic info queries, patent catalog information, patent applicant queries, patent inventor queries, patent classification codes, patent abstract retrieval, patent citation analysis, patent priority claims, patent application citations, patent examiner information, patent bibliographic data, inventor lookup, applicant lookup, patent classification, patent metadata, Nexscope, or patent citations. Even if the user does not explicitly mention "bibliographic info," this skill should be triggered whenever their need involves querying detailed metadata for a specific patent by patent ID or publication number.
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。
Trigger.dev client/frontend surface: subscribe to runs in realtime (runs.subscribeToRun and the @trigger.dev/react-hooks hook useRealtimeRun), consume metadata and AI/text streams in React (useRealtimeStream), trigger tasks from the browser (useTaskTrigger, useRealtimeTaskTrigger), and mint scoped frontend credentials with auth.createPublicToken / auth.createTriggerPublicToken. Load when wiring a frontend (React/Next.js/Remix) or backend-for-frontend to show live run progress, status badges, token streams, trigger buttons, or wait-token approval UIs. NOT for writing the backend task itself (streams.define / metadata.set is trigger-authoring-tasks territory); this is the consumer side.
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 wants full metadata for a paper — from a DOI, PMID, arXiv id, OpenAlex id, or title. Returns title, author list, first author, corresponding author(s), publication date, journal name + ISO-4 abbreviation, impact factor, volume/issue/pages, DOI/PMID, citation count, and abstract in one record. Triggers on "paper metadata", "who is the corresponding author", "first author of", "what journal / impact factor for this DOI/PMID", "cite this paper", "文献元数据", "通讯作者", "第一作者", "影响因子". PROACTIVELY USE when the user pastes a DOI/PMID/arXiv id or paper title and asks about its authors, venue, or impact.
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。
Provides Hatchling build backend guidance for Python packaging — use when configuring pyproject.toml metadata, build targets (wheel, sdist, binary), file selection with git-style globs, build hooks, metadata hooks, version management (code/regex/env sources), editable installs, the hatch-vcs plugin, plugin development, build environment setup (UV/pip/Cython), setuptools migration, or troubleshooting Hatchling errors. Covers PEP 517/518/621/660 standards and context variable interpolation.
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。
Validates and corrects Python shebangs and PEP 723 inline script metadata by applying four shebang-selection rules. Use when auditing or fixing shebangs in Python files — choosing between plain python3 and the uv shebang for standalone scripts with external dependencies, adding or removing PEP 723 metadata blocks to match actual import requirements, checking execute bit presence, or avoiding redundant transitive dependencies when typer is declared.
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。
Extracts structured text, metadata, and embedded objects from PDFs, Office documents, and 1000+ file formats using the Apache Tika REST API. Outputs clean Markdown or JSON with XMP metadata preservation.
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。
Download a supported video URL with yt-dlp and upload the preserved file plus metadata to archive.org as a repeatable preservation job.
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。
Next.js project structure, App Router conventions, file-based routing primitives, layouts, error boundaries, metadata, image and font optimization, configuration. Apply when implementing or modifying Next.js features. Use this skill to: - Pick the correct file convention (page/layout/loading/error/not-found/route/template/default). - Structure a feature within the App Router tree. - Set up metadata for SEO and OpenGraph. - Configure next.config.js for headers, redirects, image domains. - Use built-in fonts and images correctly. Do NOT use this skill for: - Server vs Client component boundaries (see server-component-patterns). - Data fetching patterns (see nextjs-data-fetching). - Complex routing (parallel/intercepting routes — see nextjs-routing). - Testing (see nextjs-testing).
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。
Framework-agnostic SQLAlchemy 2.0 core shared by fastapi-plugin and flask-plugin: declarative mapped classes with Mapped/mapped_column, column type selection, relationships with explicit lazy loading and cascades, 2.0-style select() querying, transaction/flush discipline, and Alembic-agnostic migration metadata rules. Framework plugins layer their delta skills (async sessions for FastAPI, Flask-SQLAlchemy integration for Flask) on top of this skill. Use this skill to: - Write SQLAlchemy 2.0 declarative models with Mapped[T] annotations and mapped_column(). - Pick correct column types (String(N), Numeric, DateTime(timezone=True), Uuid, Enum). - Define relationships with explicit lazy loading strategy and cascade settings. - Query with 2.0-style select() statements and manage flush vs commit boundaries. - Keep model metadata visible to Alembic autogenerate. Do NOT use this skill for: - FastAPI async engine/session lifecycle — see fastapi-plugin:sqlalchemy-patterns. - Flask-SQLAlchemy extension setup and Flask-Migrate — see flask-plugin:sqlalchemy-patterns. - Python idioms — see python-foundation:python-conventions.
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。