「pipe flow」の検索結果

Navigator and help guide for the UniKit framework — use when the user is unsure what to do next, where to start, or which unikit skill fits their situation. It diagnoses intent and points to the right skill or pipeline: starting a project, writing code, testing, planning, the spec-driven code workflow, authoring or detailing game design, finding a new mechanic, or deciding what game to build. Trigger on questions like "what do I do next", "where do I start", "how do I start coding", "how do I test my code", "how do I create the game design", "how do I detail a system", "how do I find a new mechanic", "which unikit skill should I use", "how does the code pipeline relate to game design", "I'm lost in unikit", "help me with unikit". Read-only — it explains and points to the right command, it never edits project files. For technical code research use /unikit-explore; for game-design research use /unikit-gd-explore; to initialize the framework use /unikit.

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。

Full-lifecycle project workflow. Takes batched tickets, implements via /implement-batch, runs smoke tests, then executes a comprehensive quality pipeline (refactor, review-arch, review-test, tidy-docs, review-release). Maximizes autonomy with andon cord escape.

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。

One-command pre-execution review pipeline. Runs scope review → architecture review → security review → design review (auto-detects which apply) before executing any workflow. Surfaces only taste decisions for user approval. Auto-fixes obvious issues. Use before executing multi-step workflows, deploying, or when user says "review this plan", "check before running", "pre-flight", or "is this ready?". Runs CEO → eng → security automatically with encoded decision principles.

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。

Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool 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。

Deterministic AI development workflow orchestrator. Manages task boards (.jonggrang/jonggrang-tasks.json), runs 16-phase pipelines (BUGFIX/SMALL/MEDIUM/LARGE), installs lifecycle hooks for Claude Code and OpenCode, and coordinates five specialist roles (Lead/Developer/Reviewer/TestLead/Tester). Use when planning features, executing multi-step development workflows, or orchestrating AI coding agents. Run via npx jonggrang.

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。

Safety constraints and quality gates for pipeline and workflow execution

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 in script-to-video production pipelines for Apple Silicon Macs. Specializes in hybrid local/cloud workflows, LoRA training for character consistency, motion graphics generation, and artist commissioning. Activate on 'AI video production', 'script to video', 'video generation pipeline', 'character consistency', 'LoRA training', 'cloud GPU', 'motion graphics', 'Wan I2V', 'InVideo alternative'. NOT for real-time video editing, video compositing (use DaVinci/Premiere), audio production, or 3D modeling (use Blender/Maya).

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 orchestrator. Walks the 11-phase pipeline, invoking each phase skill in order inside an internal loop, yielding at consent gates (/approve-direction, /approve-swarm, /grant-commit), and exiting cleanly on yield/failure/done. Decides swarm-vs-solo at Phase 6. Auto-loops /tdd on integrate failures that don't require a spec change. The harness_continuation Stop hook is a safety net that re-fires harness only when the loop exited mid-flow.

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, per-task cost tracking, and crash-proof pipeline execution.

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。

Adaption AI SDK for synthetic data augmentation and dataset adaptation. Use when building data pipelines with the Adaption Python SDK, uploading datasets (local files, Hugging Face, Kaggle), running augmentation/adaptation jobs, configuring brand controls (hallucination mitigation, safety categories, length), recipe specifications (reasoning traces, deduplication, preference pairs, prompt rephrase), evaluating dataset quality, downloading results, or any workflow involving `pip install adaption`, `from adaption import Adaption`, Adaptive Data, or the adaptionlabs.ai API. Also trigger when the user mentions synthetic data generation for fine-tuning, dataset augmentation pipelines, DPO preference pair generation, or grounding-based hallucination reduction on training data.

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。

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。

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。

Build multi-step AI content creation pipelines combining image, video, audio, and text. Workflow examples: generate image -> animate -> add voiceover -> merge with music. Tools: FLUX, Veo, Kokoro TTS, OmniHuman, media merger, upscaling. Use for: YouTube videos, social media content, market...

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, per-task cost tracking, and crash-proof pipeline execution.

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 the project's validation pipeline (lint → format → typecheck → unit tests) with auto-fixes, then optionally stage and write a Conventional Commit. Use before committing, when asked to "check", "validate", "run the workflow", or "get this commit-ready".

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 iOS screenshot automation with xcodebuild/simctl for build-run, AXe for UI actions, JSON settings and plan files, Koubou-based framing (`asc screenshots frame`), and screenshot upload (`asc screenshots upload`). Use when users ask for automated screenshot capture, AXe-driven simulator flows, frame composition, or screenshot-to-upload 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。

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。

Manage the NV oOS Pro Toolkit CRM — leads, deals, companies, contacts, customers, activities, and support tickets. Covers the complete sales pipeline from lead capture through qualification, deal management, and post-conversion customer tracking with BANT/MEDDIC scoring, activity logging, and workflow automation. Use when working with CRM records, managing pipeline stages, qualifying leads, creating company profiles, logging calls/emails/meetings, or troubleshooting CRM data issues.

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, an

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。

Master orchestrator for B2B SaaS content creation and the editorial production system behind it. Use this skill to produce blog articles, whitepapers, case studies, video scripts, newsletters and ghostwritten thought leadership, to develop content strategy, and to run the publishing line itself — content briefs, editorial calendars and production capacity, review and approval workflow, subject-matter-expert access, the style guide, an AI-drafting policy, and a content inventory with owners. Also triggers on: blog, article, case study, whitepaper, newsletter, video script, ghostwriting, content calendar, editorial, thought leadership, content strategy, content brief, content operations, content ops, editorial workflow, managing editor, who reviews this, our content pipeline is stuck, content is stuck in review, content audit, content inventory, content governance, style guide, house style, SME interview, expert interview, freelance writers, AI content policy, how much content can we actually produce.

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 a comprehensive health check on a single Estuary task by combining catalog status, data flow stats, recent errors, and publication history. Use when diagnosing a task that seems broken, slow, or stuck. Use when user says "is my task healthy", "something seems wrong", "check my capture", "check my materialization", "why is data not flowing", "task health check", "diagnose my task", or "what's wrong with my 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。

AI-driven Flutter E2E testing via VM Service CLI. Control running Flutter apps in debug mode for smoke tests, regression checks, exploratory testing, and UI automation. Trigger when user mentions E2E testing, UI automation, smoke tests, integration testing, test automation, controlling Flutter apps, VM Service interaction, debugging UI flows, automated regression testing, CI sanity checks, or testing native dialogs/permissions/WebViews. Supports stateful named instances or stateless direct URI connections. Ideal for rapid test feedback, AI agent workflows, element discovery, screenshot capture, log collection, and scriptable UI interactions (tap, scroll, enterText). Use for troubleshooting flaky tests, automating repetitive manual tests, verifying app behavior after merges, or setting up automated QA 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。