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概要と使いどころ
Build UI with Core's shadcn/Base UI system. Use for selecting, installing, composing, or customizing shadcn/ui, registry items, themes, and wrappers. Always preserve exact style `base-maia`, Zinc-oriented semantic CSS-variable tokens, and Base UI primitives. Do not use to switch styles, run `shadcn init`, or introduce Radix/React Aria. Pair with `packages/ui/AGENTS.md`.
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。
Open, inspect or configure an Arch Studio workspace and register projects. Use for studio administration; direct domain skills and tools-only use remain available.
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。
Search for EPDs by product category, CSI division, or material type — finds EPDs from EC3, program operator registries, and manufacturer sites. Use when the user asks to "find EPDs for...", locate declarations for a material, or source low-carbon product 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。
Draft and register project proposals, protect issued terms, and record explicitly evidenced commercial lifecycle changes. Sending messages is separate authorization.
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。
Maintain sourced agreement context and scope in PROJECT.md, cite registered contracts/proposals, and record amendments without imposing a commercial workflow.
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。
GWAS and population genetics tool. Processes PLINK (.bed/.bim/.fam), VCF, and BGEN; runs QC (MAF, HWE, missingness), IBD estimation, PCA, and linear/logistic regression GWAS. Outputs Manhattan-ready summary stats. Use regenie or SAIGE for biobanks (>100k samples) needing mixed models.
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 scRNA-seq cell type annotation via pre-trained logistic regression. 45+ models: immune, gut, lung, brain, fetal, cancer microenvironments. Input normalized AnnData; outputs per-cell labels, majority-vote cluster labels, confidence scores. Use for fast, reference-backed annotation without manual marker inspection.
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 COSMIC for cancer somatic mutations, gene census, mutational signatures, drug resistance variants. REST API v3.1 supports gene/sample/variant queries; free registration. For germline use clinvar-database; for drug-target data use opentargets-database or chembl-database-bioactivity.
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。
BRENDA Enzyme DB SOAP/REST queries: kinetic parameters (Km, Vmax, kcat, Ki), EC classes, substrate specificity, inhibitors, cofactors, organism data. 80K+ enzymes, 7M+ values. Free academic registration. For metabolic modeling use cobrapy-metabolic-modeling; metabolites use hmdb-database.
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。
Process-based discrete-event simulation. Model queues, shared resources, timed events: manufacturing, service ops, network traffic, logistics. Processes are Python generators yielding events. Resources: capacity-limited (Resource/Priority/Preemptive), bulk (Container), objects (Store, FilterStore). For continuous use SciPy ODEs; for agent-based use Mesa.
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。
Geospatial vector analysis extending pandas. Read/write spatial formats (Shapefile, GeoJSON, GeoPackage, Parquet, PostGIS), CRS handling, geometric ops (buffer, simplify, centroid, affine), spatial analysis (joins, overlays, dissolve, clipping, distance), visualization (choropleth, interactive maps, basemaps). Use for spatial joins, overlays, CRS transforms, area/distance, maps.
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。
Bayesian modeling with PyMC 5: priors, likelihood, NUTS/ADVI sampling, diagnostics (R-hat, ESS), LOO/WAIC comparison, prediction. Hierarchical, logistic, GP variants; predictive checks.
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。
Benchling R&D Python SDK: CRUD on registry entities (DNA, RNA, proteins, custom), inventory, ELN, workflow automation. Needs Benchling account and API key. Use biopython for local sequence analysis; pubchem for chemical DBs.
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。
MCP server optimization and transport layer enhancement for claude-flow v3. Implements connection pooling, load balancing, tool registry optimization, and performance monitoring for sub-100ms response times.
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。
Comprehensive best practices for robot perception systems covering cameras, LiDARs, depth sensors, IMUs, and multi-sensor setups. Use this skill when working with RGB image processing, depth maps, point clouds, sensor calibration (intrinsic, extrinsic, hand-eye), object detection, semantic segmentation, 3D reconstruction, visual servoing, or perception pipeline optimization. Trigger whenever the user mentions OpenCV, Open3D, PCL, RealSense, ZED, OAK-D, camera calibration, AprilTags, ArUco markers, stereo vision, RGBD, point cloud filtering, ICP registration, coordinate transforms, camera intrinsics, distortion correction, image undistortion, sensor streaming, frame synchronization, or any computer vision task in a robotics context. Also covers multi-camera rigs, time synchronization across sensors, perception latency budgets, and production deployment of perception 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。
Provides and generates LangChain4j tool and function calling patterns: annotates methods as tools with @Tool, configures tool executors, registers tools with AiServices, validates tool parameters, and handles tool execution errors. Use when building AI agents that call tools, define function specifications, manage tool responses, or integrate external APIs with LLM-driven applications.
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 before running any confirmatory analysis or looking at outcome data, when testing a hypothesis, computing a p-value, or about to claim an effect - locks predictions and decision rules before results are seen
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 starting analysis work that needs isolation, or before executing a pre-registered plan - ensures an isolated, reproducible workspace with pinned environment, fixed seeds, and immutable raw 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。
Use when executing a pre-registered analysis plan with mostly independent steps in the current session
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 your human partner has explicitly opted into exploratory or feasibility mode - a compute-heavy simulation, an unproven pipeline, an unbenchmarked solver, an untested cluster job - or when whether the work can run at all is still unknown, when a plan's largest configuration has never been executed, when a memory or wall-clock ceiling is estimated rather than measured, or when someone proposes pre-registering over runs that have never happened
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 you have a pre-registered analysis plan to execute inline in this session with review checkpoints, on a platform without subagents
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。
Track material deliveries, manage inventory, and coordinate logistics. Monitor delivery schedules and site storage.
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。
Optimize prefabrication and modular construction workflows. Plan module sequencing, factory scheduling, transportation logistics, and on-site assembly for maximum efficiency.
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。
Databricks Model Serving endpoint lifecycle and ops. Use when asked to: CRUD serving endpoints (CLI or MLflow Deployments client); configure traffic routing for A/B / canary deploys and zero-downtime version swaps; retrieve OpenAPI schemas; inspect logs, metrics, or permissions; manage legacy AI Gateway rate limits (not Unity Gateway); discover Foundation Model API endpoints at runtime; integrate endpoints into Databricks Apps; or stream from off-platform clients (Vercel AI SDK v6, standalone Node.js). NOT for: Unity Gateway CRUD and management (databricks-unity-gateway), training, MLflow autologging, UC registration, custom PyFunc/ResponsesAgent authoring (databricks-ml-training); Knowledge Assistants/Supervisor Agents (databricks-agent-bricks); MLflow evaluation (databricks-mlflow-evaluation).
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。