「pip」の検索結果

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 dedicated Kinect wall harp implementation team. Use whenever the user asks to build, implement, continue, fix, QA, document, or ship the Kinect wall harp, Kinect v2 wall-depth harp, FreenectTD depth-blob hand tracking, projected wall strings, pluck synth harp, or this feature's Layer 1 tool/recipe/docs. Use this before the generic tdmcp-pipeline for this feature.

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。

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。

Babel internal issue protocol — create a labeled handoff between pipeline agents. Writes designs/<name>/.handoff/<label>.md and appends to handoff_log.jsonl; best-effort gh issue create if gh available. Triggered by every guru/architect agent at handoff time.

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。

DevOps patterns: containerization, CI/CD, deployment strategies, monitoring. Use when containerizing apps, setting up pipelines, or deploying 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。

Cloudflare Workers CLI for deploying, developing, and managing Workers, KV, R2, D1, Vectorize, Hyperdrive, Workers AI, Containers, Queues, Workflows, Pipelines, and Secrets Store. Load before running wrangler commands to ensure correct syntax and best practices. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.

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。

Blender MCP expert for scene inspection, Python scripting, GLTF export, and material/animation extraction. Activate when: (1) using Blender MCP tools (get_scene_info, execute_python, screenshot, etc.), (2) writing Blender Python scripts for extraction or manipulation, (3) exporting scenes to GLTF/GLB for web (Three.js, R3F), (4) debugging material or texture export losses, (5) optimizing GLB files with gltf-transform, (6) using asset integrations (PolyHaven, Sketchfab, Hyper3D Rodin, Hunyuan3D). Covers critical export gotchas, material mapping survival, texture optimization pipeline, headless CLI patterns, and known failure modes.

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。

Read GitHub Actions CI for the current branch or open PR, triage failing checks, reproduce locally, and fix scoped issues until green. Use whenever the user mentions CI failures, red checks, failed pipeline, GitHub Actions, merge blocked, fix the build, babysit CI, gh run failed, ruff/ty errors, failing pytest, example run failed, or asks to check CI on this branch — even if they only paste a log snippet. Also use after implementing a feature when tests, lint, or typecheck might have broken.

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 for analytics and data pipelines. Triggers: ETL, ELT, data quality, SQL optimization, schema drift, backfill, 데이터 파이프라인, 데이터 품질, 백필.

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。

Set up or refine open-source .NET code-quality gates for CI: formatting, `.editorconfig`, SDK analyzers, third-party analyzers, coverage, mutation testing, architecture tests, and security scanning. Use when a .NET repo needs an explicit quality stack in `AGENTS.md`, docs, or pipeline YAML.

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 custom AI agents in Claude Code from a user's problem statement. This skill analyzes the user's use case, asks smart clarifying questions, researches the internet for similar agents (GitHub repos, blogs, Claude Code community patterns), and then architects and builds production-ready Claude Code agents — including subagents, skills, hooks, slash commands, MCP integrations, and CLAUDE.md configuration. Use this skill whenever the user wants to create an agent, build an automation workflow, set up a Claude Code subagent, design a multi-agent system, or says things like 'build me an agent for...', 'automate this with Claude Code', 'I want a subagent that...', 'help me create a workflow', 'set up an agent pipeline', or any variation of wanting Claude Code to do something specialized. Also trigger when the user mentions agent architecture, agent SDK, agentic workflows, or task delegation in Claude Code — even if they don't use the word 'agent' explicitly.

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。

Evaluates SPAC de-SPAC transaction economics with sponsor dilution, redemption risk, PIPE analysis, and warrant impact. Use when analyzing SPAC deals, evaluating sponsor economics, or modeling de-SPAC outcomes.

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。

Evaluates ML applications in investment with feature engineering, model selection, and implementation considerations for alpha generation. Use when evaluating ML for investing, designing ML pipelines, or assessing ML strategy feasibility.

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。

Evaluates midstream assets with throughput analysis, fee-based vs commodity-exposed revenue, and contract structure assessment. Use when analyzing midstream investments, evaluating pipeline assets, or assessing gathering systems.

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。

Evaluates sales efficiency with CAC payback, sales cycle analysis, channel economics, and pipeline conversion metrics. Use when assessing GTM efficiency, analyzing sales productivity, or evaluating distribution strategy.

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。

Autonomous AI Sales Development Rep for B2B export — handles lead discovery, BANT qualification, multi-channel outreach (WhatsApp/Email/Telegram), CRM management, and deal pipeline tracking. Built for cross-border trade.

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。

Automated security scanning pipeline covering SAST (Semgrep), SCA (OWASP dependency-check), SBOM generation (Syft), vulnerability scanning (Grype), and secrets detection (detect-secrets). Invoked by security-architect for pre-commit gates, CI integration, and finding triage.

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。

Semantic search over global agent memory. Use to retrieve previously learned patterns, decisions, gotchas, and workarounds. Prevents stale-context errors across long sessions and multi-agent 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。

Start Telegram monitoring. Auto-enables voice pipeline if ElevenLabs or OpenAI TTS keys are detected.

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 ML expert covering PyTorch, TensorFlow, Hugging Face, scikit-learn, LLM integration, RAG pipelines, MLOps, and production ML systems

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。

Check voice message pipeline config — verify Whisper, ElevenLabs/OpenAI TTS keys and dependencies.

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 DevOps integration covering pipelines, boards, repos, artifacts, and work item management via Azure CLI and REST API

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。

Scaffolds CI/CD pipeline definitions using GitHub Actions for build, test, lint, and deploy 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。