2,287 件 ・ 関連度順
概要と使いどころ
Use when an agent needs to drive a real Chrome window via opencli — inspect a page, fill forms, click through logged-in flows, or extract data ad-hoc. Covers the selector-first target contract, compound form fields, stale-ref handling, network capture, and the agent-native envelopes the CLI returns. Not for writing adapters — see opencli-adapter-author for that.
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 task requires automating a real browser from the terminal (navigation, form filling, snapshots, screenshots, data extraction, UI-flow debugging) via `playwright-cli` or the bundled wrapper script.
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 repeatable data-to-Docs pipelines from Sheets and Drive sources. Use for automated status reports, template-based document assembly, and scheduled publishing workflows.
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。
GStack engineering-manager plan review for architecture, data flow, APIs, migrations, edge cases, tests, performance, release risk, and implementation 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。
Conduct reproducible web searches and decision-oriented research across official sites, search engines, news, papers, forums, social platforms, and public datasets. Use when Codex must investigate a person, organization, product, market, technical resource, reputation, career choice, or other current topic; compare candidates; verify claims; collect dated citations; distinguish facts from inference and anecdote; or write an evidence-backed research report.
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 machine learning, data mining, and iterative HPC with Exasol. Covers end-to-end ML pipelines (DISTRIBUTE BY + SET scripts + BucketFS), per-entity federated training with partial_fit and ctx.reset(), batch inference, map-reduce ensemble training, distributed ensemble and SON algorithm for frequent itemset mining (Apriori, FP-Growth, association rules, market-basket analysis), Lua execute script orchestration for iterative algorithms (k-means, SGD, gradient descent), scikit-learn model training, parallel hyperparameter search, per-entity forecasting, anomaly detection, model lifecycle in BucketFS (pickle/joblib/ONNX versioning), GPU acceleration via CUDA SLCs (PyTorch/TensorFlow/RAPIDS), and ML-specific performance tuning (skew, OOM, multi-pass chunking).
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 Exasol document-file virtual schemas for federated read-only access to object and file storage — S3, Google Cloud Storage, Azure Blob Storage, and Azure Data Lake Storage Gen2 document files. Covers document-file adapter family selection, the EDML `MAPPING` definition, `CREATE VIRTUAL SCHEMA` usage, refresh workflows, and query-side troubleshooting.
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 Exasol Cloud Storage Extension workflows for moving structured object-storage files through extension UDFs. Covers setup prerequisites, the fixed `CLOUD_STORAGE_EXTENSION.IMPORT_PATH` and `CLOUD_STORAGE_EXTENSION.EXPORT_PATH` entrypoints used with `FROM SCRIPT` and `INTO SCRIPT`, Parquet, Avro, ORC, and Delta files on S3, Google Cloud Storage, Azure Blob Storage, Azure Data Lake, HDFS, and Alluxio, connection-object credential patterns, and routing away from native IMPORT or EXPORT when the extension path is the right fit.
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 Exasol `EXPORT` and `EXPORT INTO` SQL plus `exapump export` local file workflows for moving data out of Exasol. Covers native CSV/FBV export, local CSV/Parquet exports with exapump, `CREATE CONNECTION` connection objects for export, FTP, SFTP, HTTP, HTTPS, and cloud targets on S3, Azure Blob Storage, and Google Cloud Storage (GCS), reject limits, and export credential 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。
Catalog and selection guide for Exasol tools, extensions, connectors, integrations, and architecture patterns. Use when the primary intent is comparison, discovery, support-status research, or architecture selection rather than executing a workflow; hand operational work to the selected dedicated skill. Covers deploy, load, explore, enrich, surface, and scale capability families, including named products with no dedicated skill of their own such as Lakehouse Turbo, the Exasol MCP Server, Governed SQL / Text-to-SQL MCP Server, Agent Control Plane, Extension Manager, Terraform and Ansible tooling, and BI, ETL, and warehouse integrations such as Databricks, SAP, Kafka, Power BI, and Tableau.
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, install, validate, and debug custom Exasol virtual schema adapters. Covers source-specific JDBC dialect implementation with `virtual-schema-common-jdbc` including `SqlDialect`, metadata reader behavior, type mapping, and pushdown capabilities, custom document-file adapter code boundaries, custom adapter properties, JAR packaging, BucketFS deployment, adapter script setup, `EXPLAIN VIRTUAL` validation, and adapter-side remote debugging workflows.
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。
Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code.
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。
Audit the project's documentation (CLAUDE.md, .claude/rules, skills, README, CONTRIBUTING, SECURITY, .env.example, app/data/*.md, learn articles) against the actual code and config, and fix drift. Use after refactors, dependency or workflow changes, or on a schedule.
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。
Exploratory data-migration validation and QA ideation workflow. Use when you need to discover hidden risks, generate new validation angles, and converge mapping specs/validation reports into a prioritized QA backlog for CRM migrations. Version 2.0, updated 2025-12-28.
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。
Refine an existing skill in place with minimal diffs. Trigger when asked to improve a skill's trigger description/frontmatter, workflow text, metadata, scripts/references/assets; also for requests to iterate, refactor, rename, or fix a skill using usage/session-mining evidence (for example from /tw-seq).
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。
Systematically explore an unfamiliar codebase and build a reusable architecture summary. Use for repo onboarding, understanding legacy or inherited code, mapping architecture before a migration or refactor, tracing a bug or feature path, or answering "what does this project do?". Documentation-first, then data flow — ends in a concise technical architecture summary with file:line evidence, not a raw file dump.
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。
Plan a cyclist's training week as a standalone coaching package with integrated workout building. Use whenever the athlete asks to plan a week or the upcoming week ("plan my week", "build my training week", "week schema", "plan a week workout schedule", or any localized equivalent), or states a goal for the week. The flow reads a per-athlete config, pulls recent Strava data, classifies rider type from the athlete's own power curve, then produces one weekly markdown plan and structured bike workouts (ZWO for Zwift) per structured bike session without relying on another skill. Works for any cyclist, not one fixed person.
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。
Hosting-Muster, um eine lokal gebaute Anwendung (eigene Datenbank, eigener API-Key, In-Process-State) sicher hostbar zu machen, ohne Nutzerverwaltung zu bauen. Kernzug — der Host speichert nichts, der Browser des Besuchers speichert alles — sodass Pro-Besucher-Accounts, Zugriffsprüfungen und Löschfristen gegenstandslos werden, statt gelöst werden zu müssen. Nutzen, wenn eine lokal gebaute App für mehrere Besucher gehostet werden soll, bei "mach das ohne Login hostbar", "keine Nutzerkonten bauen", "Datenschutzerklärung für ein gehostetes Tool verkleinern", "wer ist verantwortlich, wenn wir nichts speichern" oder beim Wählen/Implementieren eines Server-Modus (`local`, `huckepack-gift`, `huckepack-only-host`, `pay-membership`). Liefert eine Data-Flow-Plan-Vorlage, eine Datenschutz-Vorlage und eine rechtliche Ersteinschätzung (DSGVO/TDDDG/UWG, deutsch) als Referenzen.
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。
Reviews a proposal, business case, deck, plan or contract summary in character as a General Counsel archetype and returns a DRAFT review in the chat with a verdict, findings that cite the exact passage or clause, legal risks, what would change the verdict and five interrogation questions. Meeting preparation, not legal advice. Use when the user asks to "run a general counsel review", "what would legal say about this", "pressure-test the legal exposure in this deal" or "prepare me for the deal committee". Do not use for price, term, renewal or vendor leverage, use procurement-reviewer instead; for lawful basis and data flows, use cdo-reviewer; for security controls, use ciso-reviewer. Drafts for human review; never approves, authorises or signs 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。
Query and mutate Linear issues, projects, cycles, teams, workflow states, and comments via its GraphQL API. Use when the user wants to read or change Linear data programmatically — without the MCP server, and without hand-navigating the Linear UI.
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 explaining or writing TBQ3 language syntax, including Params, Vars, Events, data types, variables, sequence variables, arrays, Map variables, control flow, operators, assignment, functions, and formula execution rules.
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 workflow to establish where and when a photo or video was captured and whether it is authentic — evidentiary handling, metadata extraction, reverse image search for provenance, visual geolocation, chronolocation from shadows, and manipulation… Use when asked to verify where an image was taken, confirm or refute a claimed location or date, or authenticate media before relying on 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。
Query the user's local and synced-device data via the screenpipe REST API at localhost:3030 — recordings, audio, UI, meetings, connected services, and memory. Use for screen activity, other-device or cross-device history, productivity, media export, connections, durable memory, or discovering and automating saved workflows.
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。
Senior engineer CLI expertise for AI agents — workflows, safety guardrails, gotchas, and anti-patterns across cloud, IaC, containers, databases, dev tools, and platforms
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。