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概要と使いどころ
Therapeutics Data Commons (TDC) AI-ready drug discovery datasets. Curated ADME, toxicity, DTI, DDI with scaffold/cold splits, standardized metrics, molecular oracles, and ADMET benchmarks for therapeutic ML and property prediction. For chemical database queries use chembl-database-bioactivity; for featurization use molfeat.
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。
Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
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。
Parse HMDB (Human Metabolome Database) local XML for metabolite info, chemical properties, biological context, disease links, spectra, and cross-DB mapping. No REST API — uses ~6 GB XML download. Use drugbank-database-access for drugs; pubchem-compound-search for live lookups.
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Testing strategies, patterns, and tools for robotics software. Use this skill when writing unit tests, integration tests, simulation tests, or hardware-in-the-loop tests for robot systems. Trigger whenever the user mentions testing ROS nodes, pytest with ROS, launch_testing, simulation testing, CI/CD for robotics, test fixtures for sensors, mock hardware, deterministic replay, regression testing for robot behaviors, or validating perception/planning/control pipelines. Also covers property-based testing for kinematics, fuzz testing for message handlers, and golden-file testing for trajectories.
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AWS Bedrock AgentCore comprehensive expert for deploying and managing AI agents at scale. Use when working with any AgentCore service including Gateway, Runtime, Memory, Identity, Code Interpreter, Browser, Observability, Agent Registry, or Evaluations. Covers agent deployment, MCP tool integration, credential management, agent discovery, governance workflows, and automated quality assessment. Essential when user mentions AgentCore, agent runtime, agent registry, agent evaluation, MCP gateway, deploy agent, register MCP server, discover agents, evaluate agent quality, agent credentials, or wants to build, deploy, catalog, or monitor AI agents on AWS.
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。
AWS Cloud Development Kit (CDK) expert for building cloud infrastructure with TypeScript/Python. Use when creating CDK stacks, defining CDK constructs, implementing infrastructure as code, or when the user mentions CDK, CloudFormation, IaC, cdk synth, cdk deploy, or wants to define AWS infrastructure programmatically. Covers CDK app structure, construct patterns, stack composition, and deployment 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。
SST v4 (Ion) expert for managing AWS resources as code with the Pulumi-backed framework. Use when writing or editing sst.config.ts, building infra/ modules (sst.aws.Function/Bucket/Dynamo/Cron/Service/Router, sst.Secret, sst.Linkable, raw aws.* Pulumi resources), wiring resource links, scoping IAM, or running sst deploy/dev/diff/remove. Essential when the user mentions SST, sst.config.ts, $config, $transform, $interpolate, sst.aws.*, sst.Secret, Pulumi/Ion, "sst deploy", a failed SST deploy (ConflictException on a resource-type change, "Identifier '__filename' has already been declared", MalformedPolicyDocument on an Output<T>), or wants to scaffold/troubleshoot AWS infrastructure with SST. Also use when a request to "deploy my AWS stack" or "add a Lambda/bucket/table" is made in a repo that already contains an sst.config.ts (using $config) or an sst dependency. Do NOT use when the task is primarily AWS CDK, Terraform, raw CloudFormation, or SAM with no SST present — those have their own tooling.
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。
AWS serverless and event-driven architecture expert based on Well-Architected Framework. Use when building serverless APIs, Lambda functions, REST APIs, microservices, or async workflows. Covers Lambda with TypeScript/Python, API Gateway (REST/HTTP), DynamoDB, Step Functions, EventBridge, SQS, SNS, and serverless patterns. Essential when user mentions serverless, Lambda, API Gateway, event-driven, async processing, queues, pub/sub, or wants to build scalable serverless applications with AWS best practices.
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。
AWS cost optimization, monitoring, and operational excellence expert. Use when analyzing AWS bills, estimating costs, setting up CloudWatch alarms, querying logs, auditing CloudTrail activity, or assessing security posture. Essential when user mentions AWS costs, spending, billing, budget, pricing, CloudWatch, observability, monitoring, alerting, CloudTrail, audit, or wants to optimize AWS infrastructure costs and operational 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。
Expert Next.js performance optimization skill covering Core Web Vitals, image/font optimization, caching strategies, streaming, bundle optimization, and Server Components best practices. Use when optimizing Next.js applications for Core Web Vitals (LCP, INP, CLS), implementing next/image and next/font, configuring caching with unstable_cache and revalidateTag, converting Client Components to Server Components, implementing Suspense streaming, or analyzing and reducing bundle size. Supports Next.js 16 + React 19 patterns.
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 expert guidance for building GraalVM Native Image executables from Java applications. Use when converting JVM applications to native binaries, optimizing cold start times, reducing memory footprint, configuring native build tools for Maven or Gradle, resolving reflection and resource issues in native builds, or implementing framework-specific native support for Spring Boot, Quarkus, and Micronaut. Triggers include "graalvm native image", "native executable java", "java cold start optimization", "native build tools", "ahead of time compilation java", "reflection config graalvm", "native image build failure".
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Use when auditing a page for E-E-A-T signals. The agent reads the page and scores Experience, Expertise, Authoritativeness, and Trustworthiness — then tells you exactly what to add to each dimension.
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。
MLflow 3 GenAI agent evaluation. Use when writing mlflow.genai.evaluate() code, creating @scorer functions, using built-in scorers (Guidelines, Correctness, Safety, RetrievalGroundedness), building eval datasets from traces, setting up trace ingestion and production monitoring, aligning judges with MemAlign from domain expert feedback, or running optimize_prompts() with GEPA for automated prompt improvement.
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。
Convert DGN files (v7-v8) to Excel databases. Extract elements, levels, and properties from infrastructure CAD files.
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。
Push Excel data back to BIM models. Update parameters, properties, and attributes from structured spreadsheets.
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。
Convert IFC files (2x3, 4x1, 4x3) to Excel databases using IfcExporter CLI. Extract BIM data, properties, and geometry without proprietary software.
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。
Convert RVT/RFA files to Excel databases. Extract BIM element data, properties, and quantities.
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 data from IFC (Industry Foundation Classes) files using IfcOpenShell. Parse BIM models, extract quantities, properties, spatial relationships, and export to various formats.
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Run a go-to-market war room for a launch, repositioning, price change, or new product. Convenes opposing expert and customer-persona viewpoints to debate the plan, surfaces the risks that kill launches, and returns a go/no-go call plus a phased rollout plan with owners, sequencing, and kill criteria. Use before any launch you can't easily walk back.
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Deep-scan any tools you connect (CRM, email, support, reviews, analytics, billing, database) to produce a data-grounded Ideal Customer Profile and a reusable persona library. Read-only by default. Use when you need to define or refresh your ICP, build buyer personas from real data instead of guesses, or generate the persona inputs that the customer-panel-of-experts and prospect-panel-simulator skills consume.
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。
Spin up 2-3 expert personas to debate any topic from different angles. Structure: Opening statements, rebuttals, cross-examination, closing arguments.
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。
Audit and sync design tokens between the source of truth (tokens.json, Figma variables export) and the code that should consume them (Tailwind config, CSS custom properties, theme files). Finds drift -- hardcoded values, orphan tokens, out-of-date copies -- and fixes it in the right direction.
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 a panel of your real buyer personas (from a deep scan of any tools you allow it to connect to) and have them debate any decision you bring — a marketing launch, a price increase, a new product, a positioning change, a feature cut. Returns a structured debate, the strongest objections, and a clear recommendation. Use when you want your actual customers in the room before you commit.
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。
Convenes a multi-LLM expert panel to pressure-test hard-to-reverse decisions. Use when reversibility score is low and adversarial review is warranted.
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。