2,428 件 ・ 関連度順
概要と使いどころ
Pythonic RDKit wrapper with sensible defaults for drug discovery. SMILES parsing, standardization, descriptors, fingerprints, similarity, clustering, diversity selection, scaffold analysis, BRICS/RECAP fragmentation, 3D conformers, and visualization. Returns native rdkit.Chem.Mol. Prefer datamol for standard workflows; use RDKit directly for advanced control.
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。
Deep learning for drug discovery. 60+ models (GCN, GAT, AttentiveFP, MPNN, ChemBERTa, GROVER), 50+ featurizers, MoleculeNet benchmarks, HPO, transfer learning. Unified load-featurize-split-train-evaluate API. For fingerprints use rdkit-cheminformatics; for featurization-only 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。
Search the PRIDE Archive v3 REST API for proteomics datasets: discover projects by keyword + faceted filters (organism, instrument, disease, software), fetch project metadata, list and download RAW/PEAK/RESULT/FASTA files (with FTP/Aspera URLs), look up which projects mention a UniProt accession, and find similar projects. PRIDE v3 no longer exposes peptide/PSM-level identification endpoints — for spectrum-level data download the project's RESULT files. Use uniprot-protein-database for protein sequences; interpro-database for domain architecture.
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 Metabolomics Workbench REST API (4,200+ NIH studies) for metabolite ID, study discovery, RefMet standardization, m/z precursor searches, and gene/protein annotations. Quirks: compound input_item rejects `name` (use pubchem_cid/kegg_id/inchi_key/etc.); free-text → compound is a two-step refmet/match→refmet/name flow; moverz endpoint returns TSV text, not JSON. Use hmdb-database for local XML; pubchem-compound-search for general compound lookup.
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。
MaxQuant + Perseus proteomics pipeline: run MaxQuant for LFQ and SILAC; parse proteinGroups.txt in Python; filter contaminants/decoys; log2 + median-normalize; impute MNAR; t-test with FDR; volcano plot; GO/pathway enrichment. Use Proteome Discoverer for Thermo-native processing; FragPipe/MSFragger for GPU-accelerated DB search.
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。
Best practices for Docker-based ROS2 development including multi-stage Dockerfiles, docker-compose for multi-container robotic systems, DDS discovery across containers, GPU passthrough for perception, and dev-vs-deploy container patterns. Use this skill when containerizing ROS2 workspaces, setting up docker-compose for robot software stacks, debugging DDS communication between containers, configuring NVIDIA Container Toolkit for GPU workloads, forwarding X11/Wayland for rviz2 and GUI tools, or managing USB device passthrough for cameras and serial devices. Trigger whenever the user mentions Docker with ROS2, docker-compose for robots, Dockerfile for colcon workspaces, container networking for DDS, devcontainer for ROS2, multi-stage builds for ROS2, or deploying ROS2 in containers. Also trigger for CI/CD with Docker-based ROS2 builds, CycloneDDS or FastDDS configuration in containers, or shared memory in Docker. Covers Humble, Jazzy, and Rolling distributions across Ubuntu 22.04 and 24.04 base images.
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。
Discover and boot an iOS simulator. Use before any command that needs a simulator UDID.
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 AWS CloudFormation patterns for ECS clusters, task definitions, services, container definitions, auto scaling, blue/green deployments, CodeDeploy integration, ALB integration, service discovery, monitoring, logging, template structure, parameters, outputs, and cross-stack references. Use when creating ECS clusters with CloudFormation, configuring Fargate and EC2 launch types, implementing blue/green deployments, managing auto scaling, integrating with ALB and NLB, and implementing ECS 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。
Provides SonarQube and SonarCloud integration patterns via the Model Context Protocol (MCP) server. Enables quality gate monitoring, issue discovery and triaging, pre-push code analysis, and rule education directly in the agent workflow. Use when the user wants to check quality gates, search for Sonar issues, analyze code snippets before committing, or understand SonarQube rules. Triggers on "sonarqube", "sonarcloud", "quality gate", "sonar issues", "analyze with sonar", "check sonar", "sonar rule", "pre-push analysis".
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 automated documentation updates by analyzing git changes between the current branch and the last release tag. Performs git diff analysis to identify modifications, then updates README.md, CHANGELOG.md following Keep a Changelog standard, and discovers documentation folders for contextual updates. Use when preparing a release, maintaining documentation sync, or before creating a pull request. Triggers on "update docs", "update changelog", "sync documentation", "update readme", "prepare release 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。
Canonical rules for preparing, opening, and updating Shift pull requests. Use whenever the user asks to create, open, draft, update, or review a pull request. Enforces issue discovery and linkage, Conventional Commit titles, release-note quality, complete validation evidence, and safe pushing.
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。
When a founder needs demo scripts, discovery call frameworks, objection handling, RFP/RFI responses, competitive feature matrices, POC planning, or closing playbooks. Activate for sales calls, demo prep, talk tracks, bid responses, competitor comparisons, or pre-sales engineering.
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。
When the user wants to find Slack groups, Discord servers, Reddit communities, forums, or online communities where their target audience hangs out. Also use when the user mentions "where to promote", "find communities", "community marketing", or "distribution channels".
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。
Keep a running implementation-notes.md during a build, logging every deviation from the plan and every discovered edge case. Use whenever implementing against an agreed plan or spec, especially in long autonomous sessions.
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。
Create, manage, and query Databricks Genie Agents — curated, per-data natural-language agents (formerly Genie Spaces): build, export/import, migrate across workspaces, and ask questions of a *specific* Agent via the Conversation API. For general data questions or finding data across your workspace, use databricks-data-discovery (Genie One) 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。
Databricks CLI operations and the parent/entry-point skill for Databricks CLI use: authentication, profile selection, and bundles. Load this first for CLI, auth, profile, and bundle tasks, then load the matching product skill. For finding or exploring data, answering questions about the data, or generating SQL, load the databricks-data-discovery skill (it routes to Genie One). Contains up-to-date guidelines for Databricks-related CLI tasks.
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。
How to query a Well workspace correctly — discover the schema first, then query the right root. Use whenever the user asks about their invoices, companies, contacts, bank transactions, accounts, or accounting ledger in Well, or before writing any well_query_records call.
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。
Generate pre-call research briefs with company news, stakeholder backgrounds, and custom discovery question sets.
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。
The 100th skill! Your intelligent guide to all 99 other skills. Recommends the perfect skill for any task, creates skill combinations, and helps you discover capabilities you didn't know you had.
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。
Find out which clients and jobs actually make money -- allocate real costs (labor hours, materials, subs, overhead) against what each job billed, and rank every client by true margin. Most service businesses discover their biggest client is their least profitable.
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。
Designs and deploys custom agent teams for specific business workflows. Interactive discovery of business processes, then generates complete team configurations with specialized agent roles, tool access, communication protocols, and handoff rules.
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 project skill library in .claude/skills/ via discovery, parallel authoring, and review. Use when packaging tribal knowledge. Do not use for one skill.
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。
Routes tasks for discovering NLP datasets in the CLUEDatasetSearch catalogue, including Chinese NLP classification, QA, matching, NER, generation, translation, corpus, and reading-comprehension resources.
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。