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概要と使いどころ
scikit-learn compatible Python toolkit for time series ML: classify, cluster, regress, segment, transform with 30+ algorithms (ROCKET, InceptionTime, KNN-DTW, HIVE-COTE, WEASEL). Handles panel, multivariate, and unequal-length series. Maintained successor to sktime. Alternatives: sktime (larger ecosystem), tslearn (fewer algorithms), catch22 (features only).
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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.
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Domain-Driven Design architecture for claude-flow v3. Implements modular, bounded context architecture with clean separation of concerns and microkernel pattern.
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Core module implementation for claude-flow v3. Implements DDD domains, clean architecture patterns, dependency injection, and modular TypeScript codebase with comprehensive testing.
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15-agent hierarchical mesh coordination for v3 implementation. Orchestrates parallel execution across security, core, and integration domains following 10 ADRs with 14-week timeline.
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Best practices, design patterns, and common pitfalls for ROS1 (Robot Operating System 1) development. Use this skill when building ROS1 nodes, packages, launch files, or debugging ROS1 systems. Trigger whenever the user mentions ROS1, catkin, rospy, roscpp, roslaunch, roscore, rostopic, tf, actionlib, message types, services, or any ROS1-era robotics middleware. Also trigger for migrating ROS1 code to ROS2, maintaining legacy ROS1 systems, or building ROS1-ROS2 bridges. Covers catkin workspaces, nodelets, dynamic reconfigure, pluginlib, and the full ROS1 ecosystem.
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Project memory workflow for init/upgrade, profile-aware setup, end session, normal/full close, file organization, document archiving, language switching, versioned update logs, rule review, status, wiki sync, and context recovery. Use for /project-butler, setup/初始化, foundation setup, profile setup, end session/收工, normal close, full close, foundation repair, organize files/整理文件, change language/切换语言, continue/接着上次, continue full context/全面回顾, review claude, sync wiki, status. Maintains project memory files. Runs version freshness check before trigger routing.
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Provides AWS CloudFormation patterns for CloudFront distributions, origins (ALB, S3, Lambda@Edge, VPC Origins), CacheBehaviors, Functions, SecurityHeaders, parameters, Outputs and cross-stack references. Use when creating CloudFront distributions with CloudFormation, configuring multiple origins, implementing caching strategies, managing custom domains with ACM, configuring WAF, and optimizing performance.
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Provides patterns for unit testing mappers, converters, and bean mappings. Validates entity-to-DTO and model transformation logic in isolation. Generates executable mapping tests with MapStruct and custom converter test coverage. Use when writing mapping tests, converter tests, entity mapping tests, or ensuring correct data transformation between DTOs and domain objects.
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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。
Provides and generates complete CRUD workflows for Spring Boot 3 services. Creates feature-focused architecture with Spring Data JPA aggregates, repositories, DTOs, controllers, and REST APIs. Validates domain invariants and transaction boundaries. Use when modeling Java backend services, REST API endpoints, database operations, web service patterns, or JPA entities for Spring Boot applications.
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Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.
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Evaluate WooAIAssistant against a structured scenario suite with hard invariants + LLM-as-judge rubric scoring. Runs live against the demo store + gpt-5.1 via the woo-mobile-ai backend wrapper, writes a JSONL run record, compares against stored baselines, and surfaces regressions. Always delegated to a subagent so the main context only sees the markdown report.
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Add or revise source-level Rustdoc for Shift Rust APIs. Use before writing or editing documentation comments in `.rs` files, especially for public or crate-visible APIs, domain structures, compiler adapters, persistence boundaries, trait implementations, unsafe code, or any API where ownership, units, coordinate spaces, mutation, I/O, sparse behavior, failure modes, panics, or concurrency are easy to misunderstand.
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Add or revise source-level JSDoc for Shift APIs. Use this skill before writing or editing documentation comments for exported classes, methods, constructors, domain data structures, render frames, reactive state, or any API where caller intent, side effects, lifetime, ownership, or nullability are easy to misunderstand.
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.
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Surface the user's unknown unknowns before work starts. Use when the user is entering an unfamiliar codebase area, an unfamiliar domain (design, video, infra), or explicitly asks for a "blindspot pass" or to find their "unknown unknowns."
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。
Curated few-shot examples for construction AI tasks: classification, extraction, analysis. Domain-specific examples for improved LLM performance.
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。
Validate construction data inputs before processing: cost estimates, schedules, BIM data, field reports. Catch errors early with domain-specific 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。
Manage construction change orders from request to approval. Track costs, schedule impacts, and maintain audit trail for dispute prevention.
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Manage construction unit price databases: update prices, track vendors, apply location factors, maintain historical records. Essential for accurate estimating.
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Level and optimize construction resource allocation across project schedule. Balance labor, equipment usage, and avoid overallocation while maintaining critical path.
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。
Manage construction equipment fleet. Track utilization, maintenance, and assignments.
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 value engineering analysis using CWICR data. Identify cost-saving alternatives while maintaining function and quality.
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。