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概要と使いどころ
Production guidance for converting sourced data and approved claims into truthful, accessible, deterministic animated visualizations with D3. Use for data-driven charts, maps, networks, hierarchies, transitions, annotations, responsive video variants, frame-by-frame browser rendering, and visualization QA; not for generic dashboards, unsourced decorative charts, or ordinary motion graphics without a data contract.
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。
Find web pages and content similar to a given URL using Exa's neural similarity search. Ideal for competitor discovery, alternative source finding, related content research, and expanding research beyond known 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。
Search, extract, and compare high-quality public sources with Exa through SandBase. Use when asked for deep web research, source discovery, current evidence, topic investigation, company research, or citation-ready findings.
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。
Find and synthesize research papers, whitepapers, PDFs, technical reports, and academic sources with Firecrawl Research, using semantic paper search, related-paper expansion, and in-body verification over Firecrawl's paper index — largely biomedical and life-science literature from PubMed, bioRxiv, and medRxiv, plus arXiv preprints in CS, physics, and math. Use when the user wants a literature review, systematic review, survey of studies, paper summary, research landscape, or sourced synthesis from scholarly and industry publications, including clinical, drug, gene, disease, epidemiology, and public-health topics. Prefer this over a general web-research workflow whenever the evidence base is published papers rather than web pages.
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Creates new Source of Truth (SoT) files when existing templates don't fit your needs. Triggers on requests to create a new SoT file, add a new artifact type, or when user says "I need to track [X] but there's no SoT for it", "create SoT", "new source of truth". Outputs a properly structured SoT.*.md file with ID prefix, frontmatter, and update protocol.
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。
Analyze an open-source project from What/Why perspective (not how-it's-implemented). Use when the user says "/analyze-oss", "분석해줘 이 오픈소스", "이 레포 뭐하는거야", "analyze this repo", "what does X do", "이거 왜 쓰는거야", "이 라이브러리 분석", provides a GitHub URL and wants understanding, or asks to deeply understand an OSS project's purpose, value, target users, and usage flow. Clones the repo to ~/opensource-analysis/<repo-name>/ (git pull if already exists), dispatches parallel subagents per analysis lens, then synthesizes a What/Why-focused report in chat. Supports optional user-specific follow-up questions.
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。
Detects attacks from server logs and blocks malicious IPs with CrowdSec, an open-source, crowd-sourced alternative to fail2ban. Use when a user asks to block brute force attempts, protect SSH or a web server, set up a community blocklist firewall, add a WAF, or install CrowdSec with Docker.
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。
Continue is an open-source AI code assistant for VS Code, JetBrains and the terminal (the cn CLI) that works with any LLM: Claude, GPT, Gemini, Ollama and other local models. Use when a user asks to configure Continue with config.yaml, add chat or tab autocomplete models, write rules or custom prompts, connect MCP servers, run cn headless in scripts, or migrate from config.json. Continue was acquired by Cursor in June 2026: the release from that month is final and the Hub is offline, so the skill covers local, self-configured use.
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。
Tests and debugs APIs with Bruno, the open-source API client that keeps collections as plain files in a Git repository. Use when a user asks to create API requests, organize collections, write test scripts, use environments and variables, run a collection from the terminal or CI with the bru CLI, or collaborate on API workflows stored in Git.
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。
Appsmith is an open-source low-code platform for building internal tools, admin panels, and dashboards. Use when a user asks to create CRUD interfaces, connect to databases or APIs with drag-and-drop widgets, write JSObjects for business logic, or self-host Appsmith with Docker or Kubernetes.
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。
Azure Command Line Interface for managing Microsoft Azure resources. Use when the user needs to create VMs, manage storage accounts, deploy functions, configure resource groups, and automate Azure operations from the terminal.
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 backends with Appwrite — open-source Backend-as-a-Service. Use when a user asks to set up user authentication, manage a database without writing backend code, handle file storage and uploads, add realtime subscriptions, set up cloud functions, build a mobile or web app backend, replace Firebase with an open-source alternative, or self-host a BaaS platform. Covers auth, databases, storage, functions, realtime, and SDK integration for web, mobile, and server-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。
Build database-powered applications with Baserow, the open-source no-code database. Use when a user asks to create spreadsheet databases, build API-connected workflows, manage relational data, or self-host Baserow as an Airtable alternative.
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。
You are an expert in Airbyte, the open-source data integration platform with 300+ pre-built connectors. You help developers sync data from SaaS tools, databases, and APIs into data warehouses and lakes — handling incremental syncs, CDC (Change Data Capture), schema evolution, and error recovery for production data 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。
Run AI agent code safely in isolated sandboxes with resource limits, audit trails, and kill switches. Use when someone asks to "sandbox my agent", "run agent code safely", "add guardrails to AI agent", "isolate agent execution", "audit agent actions", "prevent agent from deleting files", "restrict agent permissions", or "add safety controls to AI coding agent". Covers Docker isolation, filesystem restrictions, network policies, resource locking, and comprehensive audit logging.
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。
Looker Studio (formerly Google Data Studio) expertise. Build dashboards, design data visualizations, connect data sources, and create marketing reports. Use when the user asks about Looker Studio, Data Studio, marketing dashboards, data visualization, report building, or connecting analytics data 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。
Generate structured research briefs from RSS feeds and web sources on any topic. Synthesizes multiple sources into actionable analysis with key findings, data points, expert perspectives, and content angles.
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。
Model Context Protocol expert for building MCP servers, tools, resources, and client integrationsUse when "mcp server, model context protocol, claude code extension, building ai tools, tool definition, mcp transport, stdio transport, sse transport, resource provider, prompt template, mcp, model-context-protocol, claude-code, ai-tools, llm-integration, anthropic, server, protocol" 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。
Create or enrich a Teams/People/@Name.md file with metadata pulled from external sources (an ATS for hiring data, a profile source for bio). Replaces manual data entry for newly-hired teammates.
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。
AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
AWS Cost Cleanup workflow skill. Use this skill when the user needs Automated cleanup of unused AWS resources to reduce costs and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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。
The secret-source gate. Routed to when changed code reads, writes, or passes a secret — API key, token, password, connection string, signing key, certificate, or any value that grants access. Validates that every secret comes from the approved store and never lands in source, log, test fixture, error response, image, or LLM prompt. The auth-crypto-reviewer agent is dispatched as the reviewer.
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。
Automate Qwoted (HARO-style PR platform) end-to-end and earn high-DR backlinks at scale: log in, set up the user's "expert" Source persona (bio + employer + contacts), search the Algolia-backed opportunity index, RESEARCH and BUILD a sourced statistics page on the user's topic (the linkable asset journalists love to cite), draft pitches as the user that link to that page, and submit them to journalists. Use this skill whenever the user asks for "PR opportunities", "Qwoted opportunities", "press mentions", "journalist requests", "HARO replies", "media pitches", "podcast guesting", "expert quotes", "stats page for SEO", "research page for journalists", or "backlinks from journalists".
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。