3,167 件 ・ 関連度順
概要と使いどころ
[Code Quality] Use when you need to perform a security review or audit on any scope — application code (OWASP Top 10 2025), secrets exposure, dependency/supply-chain malware, third-party repository vetting before install, infrastructure/config, CI/CD pipeline, AI-agent risks, and host/VPS compromise detection.
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。
[Codex] Use when you need to run the full Codex mirror sync + verify pipeline (migrate → hooks → context → verify) standalone, no npm/package JSON needed.
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 on an existing project to decide KEEP / MERGE / ARCHIVE — never limbo. Forces a call on tools that already exist and may have stopped earning their keep. Trigger on "is this still worth keeping", "audit this repo", "tech-debt cleanup", "sweep the folder", "what's dead here", or any signal that something hasn't been touched in months. Judged from both the agent lens (still legible, still drivable, still safe) and the human lens (do I still reach for it, does it nag). Includes a non-negotiable footgun check for stale pipelines that can overwrite live data. Outputs a backed AUDIT.md and a one-line verdict note in the project root docs. Do NOT load for greenfield projects (use new-project-gate), one-off bug fixes, or when the answer is already obvious.
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。
Cross-disease market sizing entry skill. Triggered by user one-liner like "做 X 市场调研" or "Run market sizing for X". Auto-runs 5 phase pipeline: contract → stratify → recall → compose-audit → bundle. Hard-gates on Cite-or-Block: every fact claim must carry citation anchor verifiable against sources/. Resumable mid-run.
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 any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .xltx, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
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。
GodoMaster — Godot 4.x game development intelligence: GDScript, nodes/scenes, 2D/3D rendering, physics, animation, UI, audio, input, shaders, networking, performance, export, file I/O, testing, localization, AI behavior, asset pipeline, and architecture tooling. Complete workflow for indie and professional game development based on official Godot 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。
Turn one task into a tree of GitHub issues worked by agents. Creates (or reuses) an epic issue, then launches the recursive orchestrator → decomposer → worker pipeline. Use when asked to "decompose a task", "break this down into issues and work it", "spawn agents to build X", or run via /task-decompose.
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 router for how a change legally happens in this repo — which sign-off gate fires for which kind of change, what counts as approval, how the packages-edit guard works, and how work is allowed to reach main. Use when starting ANY change and unsure which rules apply, when an Edit to packages/* gets BLOCKED, when a pipeline issue sits on status:blocked and you need to know what unblocks it, or when asked "do I need sign-off for this", "how do I get this merged", "can I deploy this", "who approves X", "why won't my approval label/reaction do anything".
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。
Verified lookup pack for crouton's schema→CRUD generation domain — the fieldsFile JSON format, the complete field-type catalog with aliases, the naming contract, the exact artifact list one generate run produces, the seed/default-layout pipeline, dialect defaults, and rollback semantics. Use when writing or reviewing a collection schema, when asking "what field types exist / what does `crouton config` actually generate / where did this file name come from / why did I get a pg schema", when a generated file's name or path doesn't match the docs, or when removing/regenerating a collection. Trigger phrases — "what field types are supported", "schema JSON format", "what files does crouton generate", "naming convention for collections", "rollback a collection", "dialect default". The generation WORKFLOW (steps + sign-off gates) lives in the `crouton` skill — this is the reference it doesn't carry.
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, revise, and validate publication-ready academic paper figures and tables. Use for LaTeX tables, related-work comparison tables, result tables, notation/dataset/taxonomy tables, precise source-data-driven experiment plots from CSV/JSON/logs, generated conceptual figures such as system overviews/pipelines/architectures/threat models, captions, artifact specs, source-data traceability, and paper-ready PDF/SVG/PNG/LaTeX exports. Do not use for prose-only paper writing, self-review, reviewer response, rebuttal drafting, or external literature-management 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。
Use when auditing UI code for interface-guideline compliance (accessibility, forms, animation, images, performance, UX copy) and/or validating a deployed page against Lighthouse 95+ targets. Produces evidence-based findings (file:line + Lighthouse JSON) and turns confirmed pendencies into Draft SPECs via write-specs. Do NOT use to design/build UI from scratch (use design) or to fix findings inline — fixes go through the SPEC 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。
Cloudflare WorkersのCI(自動検査)とCD(自動デプロイ)を、Workers Buildsまたは外部CI/CDから案件に合う1経路だけ選び、無料枠に収まる最小構成で構築・運用するためのスキル。 「CIを入れて」「CDを組んで」「自動デプロイしたい」「GitHub Actions」「ワークフロー」「テストを自動で回したい」 「マージしたら勝手に公開されるようにして」「デプロイを自動化して」「CIが落ちた」「Actionsが失敗する」 「デプロイの手作業をなくしたい」「Cloudflareの設定が分からない」「非エンジニア向け手順を作って」などの文脈で必ず使用する。 デプロイ先が Cloudflare Workers の場合は Skill cloudflare-secure-deploy を併用する(D1マイグレーションの扱いが最重要)。ブランチ・PR・タグの規約は Skill solo-git-flow に従う。手動デプロイを繰り返している状況を見つけたら、依頼されていなくても このスキルの §0 を読んで導入可否を判断すること。
Track Two / internal innovation system for running small, fast experiments outside the core business. Runs in three modes — Diagnostic for designing the full Protoloop from scratch (intake channels, loop structure, team, exit paths), Review for running the monthly heartbeat meeting (loop-by-loop decisions, forward-loading, portfolio health), and Alert Triggers for defining tripwires that protect, feed, and maintain momentum in the system. Updates company-context.md after every run. Triggers on questions about innovation pipelines, intrapreneurship, Track Two strategy, experimentation systems, handling ideas that don't fit the core business, or building systematic exploration alongside exploitation. Based on 20 years of practice by Eterdis (eterdis.com).
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。
Deploy and use the Exasol Text AI Extension (TXAIE) with notebook-connector. Covers `deploy_license`, `initialize_text_ai_extension`, the `PYTHON3_TXAIE` language container, the Extraction API classes `StandardExtractor`, `NamedEntityExtractor`, `TopicClassifierExtractor`, `PipelineExtractor`, and `BranchExtractor`, named-entity, zero-shot classification, and feature extraction runs, default-model installation, and result-table querying such as `TXAIE_AUDIT_LOG`.
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。
Create an atomic PR closing a GitHub issue end-to-end via a 6-step pipeline. Use to resolve, fix, or implement issue #N. Don't use for analysis without fixing (/issue-analysis), reviewing a PR (/issue-pr-review), or bulk backlog work (/auto-pilot).
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 Azure architectures for startups and enterprises. Use when asked to design Azure infrastructure, create Bicep/ARM templates, optimize Azure costs, set up Azure DevOps pipelines, or migrate to Azure. Covers AKS, App Service, Azure Functions, Cosmos DB, 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。
Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines. Triggers: "azure-ai-ml", "MLClient", "workspace", "model registry", "training jobs", "datasets".
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 Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.
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 automated AI workflows combining multiple models and services. Patterns: batch processing, scheduled tasks, event-driven pipelines, agent loops. Tools: inference.sh CLI, bash scripting, Python SDK, webhook integration. Use for: content automation, data processing, monitoring, schedul...
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。
AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.
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。
6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
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。
Audits GitHub Actions workflows for security vulnerabilities in AI agent integrations including Claude Code Action, Gemini CLI, OpenAI Codex, and GitHub AI Inference. Detects attack vectors where attacker-controlled input reaches. AI agents running in CI/CD 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。
This skill should be used when the user asks to "implement LLM-as-judge", "compare model outputs", "create evaluation rubrics", "mitigate evaluation bias", or mentions direct scoring, pairwise comparison, position bias, evaluation pipelines, or automated quality assessment.
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。