895 件 ・ 関連度順
概要と使いどころ
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。
End-to-end vendor procurement workflow orchestrating RFQ creation, email sending, vendor response tracking, and client-facing estimate generation. Use this skill when managing the full quote-request-to-client-estimate pipeline, coordinating between vendor-rfq-creator and vendor-estimate-creator skills with email automation and status tracking.
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 a look-alike audience from a seed audience and a candidate population dataset. Classifies Rosetta Stone attributes, generates the same materialized-view scoring pipeline Lookalike Studio emits (Naive-Bayes categorical weights + Gaussian continuous similarity), gates on approval, submits via `narrative_workflows_create`, and monitors the build to completion. Use when: "create a lookalike audience", "find more users like this segment", "expand my seed audience to 500k similar users", "score the population against my customers", "build a look-alike of dataset X". (narrative-audience)
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。
Operating Roblox Studio for game development — the visual editor in which the 3D scene is built, tested, and published. Use this skill for: Studio basics (Explorer, Workspace, play-test, saving the place as .rbxl), the interplay with Rojo (Connect, scene-vs-code mode), AI control of Studio via the Roblox-Studio-MCP (execute_luau, insert_from_creator_store, generate_material, screen_capture, Play/Stop, reading the Console), the complete asset-pipeline workflow (Creator Store → clean up → kit → scene → .rbxl → Rojo brings it to life), and above all the MANDATORY malware scan for marketplace assets. Also trigger on "embed an asset from the Store", "Studio MCP not working", "studios: []", "generate material", "save scene", "is this Roblox asset safe", "scripts disappear after Play".
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 a verified correction or repeated workflow into a reusable local learning.
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。
Operational guide for WordPress.org submission readiness of NV oOS standalone plugins (nvoos-docs-hub live at 0.5.3; nvoos-content-graph live at 1.0.10 in the re-submission loop; nvoos-design-system next in the pipeline — issue #6811). Covers the 18 wp.org guidelines, the Plugin Check (PCP) gate in CI and Docker, PCP finding triage, the reviewer-reply loop (findings taxonomy from the real 0.4.3 review, related-issue sweep checklist, reply-email template), packaging exclusion tri-sync, readme.txt standards, .wordpress-org listing assets, and the NV oOS Complete checkout-flow disclosure discipline (docs-hub 0.5.3, content-graph). Use when preparing a wp.org submission, responding to a reviewer email, fixing the plugin-check CI job, triaging PCP findings, or capturing listing screenshots.
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。
Designs event-driven architectures, webhook systems, API chaining flows, ETL pipelines, and integration patterns between services.
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。
Handles containerization, CI/CD pipelines, and deployment setup.
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。
Orchestrate autonomous AI development pipelines through your Kanban board (Asana, GitHub Projects, Linear). Manages multi-worker Claude Code dispatch, deterministic quality gates, adversarial review,
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。
When the user wants to create, generate, or produce video content using AI tools or programmatic frameworks. Also use when the user mentions 'video production,' 'AI video,' 'Remotion,' 'Hyperframes,' 'HeyGen,' 'Synthesia,' 'Veo,' 'Sora,' 'Runway,' 'Kling,' 'Seedance,' 'Hailuo,' 'MiniMax,' 'Pika,' 'Hunyuan,' 'Wan,' 'video generation,' 'AI avatar,' 'talking head video,' 'programmatic video,' 'video template,' 'explainer video,' 'product demo video,' 'video pipeline,' 'copy this edit,' 'match this video style,' 'reverse-engineer this video,' 'edit like this reference,' or 'make me a video.' Use this for video creation, generation, and production workflows. For video content strategy and what to post, see social. For paid video ad creative, see ad-creative.
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。
データ準備からモデル学習、検証、本番デプロイメントまでのエンドツーエンドMLOpsパイプラインを構築します。MLパイプラインの作成、MLOpsプラクティスの実装、またはモデル学習とデプロイメントワークフローの自動化時に使用します。
Automate CircleCI tasks via Rube MCP (Composio): trigger pipelines, monitor workflows/jobs, retrieve artifacts and test metadata. Always search tools first for current schemas.
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。
Cross-session progress tracking, pattern extraction, and session resumption. Gives agents memory across sessions using markdown files in `.memory/pipeline/progress/`. Subsumes self-improving-agent — handles both implementation tracking AND experiential learning in one skill. Workflows invoke specific protocols by name.
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。
Document templates for all pipeline output documents — specs, indexes, reports, and strategy docs. Use when any workflow needs to produce a structured document.
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 working with Gcp Cloud Deploy — google Cloud Deploy delivery pipeline management, release tracking, rollout status monitoring, approval workflows, and deployment diagnostics via gcloud CLI.
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。
NVIDIA DeepStream SDK development guidance for Python pyservicemaker pipelines, video analytics, TensorRT integration, and streaming inference 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。
Automate doc generation with JSDoc/TSDoc, linters, and pre-commit hooks. Use when setting up markdownlint, configuring doc linting pipelines, integrating JSDoc/TSDoc, or building automated documentation 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。
[Extended thinking: This workflow implements a sophisticated debugging and resolution pipeline that leverages AI-assisted debugging tools and observability platforms to systematically diagnose and res
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。
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。
Build scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data 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。
Apache Airflow, dbt, Prefect, Dagster, and modern data orchestration 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。
Use when running the autonomous multi-agent build pipeline after /bdbrainstorm or /grill-me, when the user types /startcycle-graph or "run a startcycle", or when a task needs durable state, a review repair loop and an automated quality gate. Routes to the startcycle-dispatch Dynamic Workflow, which reads production_artifacts/state.json and invokes Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, and Shipping in turn per .agents/graph.md's edge table — the agents never invoke each other.
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。