「pipeline」の検索結果

Bootstrap any new project: identity, design system, staging+production, i18n, SEO, test infrastructure, 8-gate deploy pipeline. Prevents tech debt from day 0.

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Use to run the full sprint pipeline: brainstorm → plan → design → tdd → build → review → qa → security → ship → monitor → retro.

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Detect documentation and architecture-model drift by comparing source-of-truth code (CLI commands, flags, env vars, GH Action inputs/outputs, package structure, pipeline stages, providers, adapters, platforms) against web docs, README, and LikeC4 architecture model (.c4 files). Use when files that define the user-facing interface or internal structure change, when the user asks to check docs, or when mentioning doc drift, architecture drift, stale docs, or documentation sync.

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 smartcrop when an agent needs to choose a sensible crop automatically instead of center-cropping every image. The workflow scores visual saliency, returns crop coordinates for a target aspect ratio, and hands those coordinates to an image pipeline that renders the final thumbnail or social card.

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。

Extract structured intelligence from audio using the AssemblyAI API with sentiment analysis, entity detection, topic modeling, and auto-chapter generation. Uses the assemblyai Python SDK for transcript processing 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。

Constructs Kubernetes-native workflow DAGs using Argo Workflows CRDs with configurable retry strategies, artifact passing via S3/MinIO, and template composition through WorkflowTemplates and ClusterWorkflowTemplates.

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Orchestrates deployment pipelines using the Argo Workflows Engine API and Argo CD ApplicationSet controller. Implements progressive delivery with Argo Rollouts canary and blue-green strategies.

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Airbyte is the leading open-source data integration platform providing 600+ pre-built connectors for ELT pipelines from APIs, databases, and files to data warehouses, lakes, and lakehouses. It supports both self-hosted and cloud deployments with a no-code connector builder.

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Converts Advanced Custom Fields field groups into native Gutenberg blocks using the ACF Block API v2 and @wordpress/scripts build pipeline. Maps ACF repeaters, groups, and flexible content to InnerBlocks and block attributes with server-side rendering via acf_register_block_type().

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Expert sales operations and analytics guidance for revenue teams. Use when designing CRM workflows, building sales dashboards, optimizing pipeline analytics, creating lead routing rules, designing territories, calculating commissions, managing data quality, building forecasting models, or integrating sales tech stack. Covers Salesforce, HubSpot, Outreach, Gong, and RevOps best practices.

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Expert sales strategy and operations guidance for B2B SaaS companies. Use when designing sales processes, implementing qualification frameworks (MEDDIC, BANT, SPICED), territory planning, forecasting, quota setting, compensation design, or optimizing pipeline velocity. Covers sales methodology, org structure, tech stack selection, win rate analysis, and deal acceleration strategies.

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Expert full-cycle enterprise sales strategist for B2B SaaS. Use when planning sales strategy, pipeline management, deal progression, account planning, competitive displacement, or territory optimization. Covers multi-threading, executive engagement, champion development, buying committee navigation, and complex deal orchestration. Use for enterprise selling, account expansion, land-and-expand, and quota attainment.

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Expert renewal management guidance for maximizing retention and expansion revenue. Use when building renewal playbooks, forecasting renewal pipeline, structuring multi-year deals, developing pricing strategies, mitigating churn risk, defending against competitive displacement, negotiating contracts, attaching expansion to renewals, or optimizing renewal operations. Covers high-touch and tech-touch renewal motions, early renewal strategies, and save plays.

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Orchestrates translation of motion designer video specifications into working Remotion code by coordinating specialized agents. Acts as pipeline coordinator that delegates to remotion-scaffold, remotion-animation, remotion-composition, and remotion-component-gen. Use when you have a complete VIDEO_SPEC.md and need full Remotion implementation.

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 that chains all Remotion video creation skills together in a single automated pipeline. Takes a creative brief and produces a complete, production-ready Remotion video project. Use when starting a new video from scratch, when asked to "create a video", "make a video", "build a complete video", or "video from idea to code".

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Architect a feature with the Carmack Council before writing code. Use when explicitly asked to plan a feature, do a "council plan", "carmack plan", or invoke /council-plan. Carmack's philosophy chairs a council of domain experts — Troy Hunt (security), Martin Fowler (refactoring), Kent C. Dodds (frontend), Matteo Collina (Node.js), Brandur Leach (Postgres), Vercel Performance, Simon Willison (LLM pipeline), Karri Saarinen (UI quality), Vitaly Friedman (UX quality). Interactive feature discovery followed by parallel subagent dispatch. Produces a sequenced, attributed implementation plan with no code. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.

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。

Perform a rigorous Carmack Council code review. Use when explicitly asked to review code, do a "council review", "carmack review", or invoke /council-review. Carmack's philosophy chairs a council of domain experts — Troy Hunt (security), Martin Fowler (refactoring), Kent C. Dodds (frontend), Matteo Collina (Node.js), Brandur Leach (Postgres), Vercel Performance, Simon Willison (LLM pipelines), Karri Saarinen (UI quality), Vitaly Friedman (UX quality), Kent Beck (test quality). Uses parallel subagents for deep, independent review. Produces prioritised P1/P2/P3 findings. Stack: Next.js App Router / React / TypeScript / tRPC / Prisma / Neon / Clerk.

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。

Orchestrates the tdmcp Anthropic Connectors Directory (Desktop Extension / MCPB) submission prep — designs the package, writes the privacy page, migrates .dxt→.mcpb, drafts all form answers, and QAs the result. Use for ANY work on the tdmcp directory/marketplace submission, including re-runs after a rejection ("update the submission", "redo the form answers", "re-migrate the bundle", "fix the privacy page", "the directory rejected us"). Drives a 4-agent pipeline (architect → docs-author ∥ bundle-engineer → QA).

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。

Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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。

Study a merged or shipped tdmcp implementation and extract reusable learnings: code improvements, runtime/UX lessons, test gaps, docs updates, roadmap items, and harness changes. Use when the user asks to learn from a completed feature/project/PR/build, analyze what can be improved from an implementation, or turn a real installation experience into actionable tdmcp improvements. This is a post-implementation learning harness; it does not replace tdmcp-pipeline for building or tdmcp-quality-audit for broad repo audits.

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。

Scout the TouchDesigner community for what's HYPED right now — community showcases, recent tutorials, generative-AI bridges, hardware interaction trends, visual-aesthetic trends of 2025-2026 — then propose tdmcp tools that ride those trends AND are easy to build. Use whenever the user wants to brainstorm new feature ideas based on what's trending in TouchDesigner, asks for 'hype' or 'trending' features, asks 'what are people doing in TD right now / what's hot / what's hype', wants tools inspired by community trends, asks to scout TD trends/aesthetics/integrations, or says things like 'ideias hype', 'novas ideias', 'o que está em alta', 'criar ferramentas para o que está bombando', 'tendências do TouchDesigner'. Also for follow-ups: refresh, rescout one surface, re-rank under another profile, deepen a trend, or filter for buildable-easy items. This is an EXTERNAL trend ideation harness — complementary to tdmcp-feature-discovery (which is INTERNAL gap analysis). It produces `_workspace/hype-scout/HYPE_TOOL_BACKLOG.md` ranked by Hype × Build-Ease; it does NOT build. Once a feature is chosen from the backlog, hand it to tdmcp-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。

Orchestrates the AI-Controlled Party mixer/Soundcraft Ui24R design team. Use whenever the user wants to design, update, refine, re-run, or implement-plan the Soundcraft Ui24R scene-arming expansion, mixer-aware AI party, operator-approved mixer cues, snapshots/cues/show control, Companion bridge, or any follow-up based on prior mixer design results. Produces a spec for tdmcp-pipeline; does not implement runtime code.

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Consolidate the five tdmcp surveyor reports into one prioritized FEATURE_BACKLOG.md — dedupe cross-surface overlaps, reconcile every candidate against the roadmap (NEW vs planned vs extension), rank by a selectable impact×effort weighting profile into P0/P1/P2 (high-confidence first), and emit per-surface tables + a recommended-next shortlist that hands straight to the build pipeline. Use when a td-synthesizer agent is consolidating surveys during the feature-discovery harness.

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 and implement native RayTK (t3kt/raytk) raymarching/SDF toolkit integration into tdmcp — beyond the existing package-manager staging, into AI-driven RayTK operator (ROP) node graphs. Use whenever the user wants to build, plan, design, implement, continue, fix, QA, or ship RayTK support: create/wire RayTK ROPs, a RayTK operator knowledge catalog, a RayTK-native raymarch/SDF scene tool, RayTK recipes, or the RayTK setup/doctor flow — including follow-ups (re-run, continue, refresh after a new RayTK release, re-QA, docs). Also triggers on 'raytk', 'raymarching toolkit', 'SDF toolkit', 't3kt', 'montar um build para o raytk', 'integrar o raytk'. This owns RayTK integration specifically; use it before the generic tdmcp-pipeline for this feature. Simple questions about RayTK can be answered directly.

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。