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Automated coordination, formatting, and learning from Claude Code operations using intelligent hooks with MCP integration. Includes pre/post task hooks, session management, Git integration, memory coordination, and neural pattern training for enhanced development 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。

Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.

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。

Foundational software design principles applied specifically to robotics module development. Use this skill when designing robot software modules, structuring codebases, making architecture decisions, reviewing robotics code, or building reusable robotics libraries. Trigger whenever the user mentions SOLID principles for robots, modular robotics software, clean architecture for robots, dependency injection in robotics, interface design for hardware, real-time design constraints, error handling strategies for robots, configuration management, separation of concerns in perception-planning- control, composability of robot behaviors, or any discussion of software craftsmanship in a robotics context. Also trigger for code reviews of robotics code, refactoring robot software, or designing APIs for robotics libraries.

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。

Architecture patterns, design principles, and proven recipes for building robust robotics software. Use this skill when designing robot software architectures, choosing between behavioral frameworks, structuring perception-planning-control pipelines, implementing state machines, designing safety systems, or architecting multi-robot systems. Trigger whenever the user mentions behavior trees, finite state machines, subsumption architecture, sensor fusion, robot safety, watchdogs, heartbeats, graceful degradation, hardware abstraction layers, real-time constraints, or software architecture for robots. Also applies to sim-to-real transfer, digital twins, and robot fleet management.

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Provides patterns for unit testing `@ConfigurationProperties` classes with `@ConfigurationPropertiesTest`. Validates property binding, tests validation constraints, verifies default values, checks type conversions, and mocks property sources for Spring Boot configuration properties. Use when testing application configuration binding, validating YAML or application.properties files, verifying environment-specific settings, or testing nested property structures.

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Audit the instructions an agent already carries — CLAUDE.md, AGENTS.md, skills, tool descriptions — for contradictions, over-constraint, and duplication, then propose a cut list. Use when an agent ignores its own instructions, when a CLAUDE.md has grown bloated, or when the user asks to audit or rightsize their agent context.

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Generate rolling look-ahead schedules for construction. Create 2-week, 3-week, or 6-week look-aheads with constraint analysis and crew coordination.

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Predict construction project costs using Machine Learning. Use Linear Regression, K-Nearest Neighbors, and Random Forest models on historical project data. Train, evaluate, and deploy cost prediction 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。

Build ML models for construction predictions. Train and evaluate custom models for cost, duration, and risk prediction.

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 AI Runtime, the `databricks air` CLI commands for submitting and managing GPU training workloads on Databricks serverless compute. Use for: writing and submitting `databricks air` workload YAML, passing hyperparameters and secrets, checking run status, listing/cancelling runs, streaming a run's logs and watching its progress, custom Docker image setup, and environment configuration.

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 workouts, stats, progress over time. Identify improvement areas, plateaus, rest/recovery needs, peak performance timing, injury risk.

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。

Custom training plans by goal (strength, cardio, flexibility). Progressive overload programming, rest day optimization, home vs gym adaptations, deload weeks.

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。

Implement proven sales methodologies (MEDDIC, BANT, Sandler, Challenger, SPIN) across your team. Generate framework-specific questions, score deals, train reps, and enforce consistent qualification. Use when implementing or optimizing sales processes.

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Designs relational and document database schemas from requirements or an existing app - tables and collections, keys, relationships, constraints, indexes driven by real query patterns, ERDs, and reversible migration scripts - with PostgreSQL (including Supabase) as the default and MySQL, SQLite, and MongoDB supported. Use when the user wants to model data for a new app or feature, review or fix an existing schema, choose keys or indexes, plan multi-tenancy, normalize or denormalize, or write CREATE TABLE statements or migrations, even if they just describe their app and ask "how should I store this". For moving data between database providers, use database-migrator.

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SwiftData patterns — autosave, relationships, dangerous predicates, CloudKit constraints, indexing, class inheritance. Use when writing, reviewing, or debugging SwiftData code.

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Establishes CPU/GPU baselines before resource-intensive operations. Use before builds, training runs, or any task that pins cores or GPUs for over a minute.

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Use when a trained medical-imaging model needs its documentation. Fills a Model Card and a Datasheet for its dataset from facts you supply, adds a METRIC-informed data-quality pass and gates that no required section is empty. Never invents numbers, provenance or licence.

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Use when validating or evaluating a trained medical-imaging model. Audits split leakage and validation design, computes task-correct held-out metrics (Dice + HD95, AUROC + AUPRC, FROC, calibration), and covers uncertainty/OOD and Grad-CAM explainability, each with a gate.

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。

Route GeoAI geospatial data, inference, training, foundation-model, and integration 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。

Repository operating skill for using Flow Forecast's flood_forecast package for time-series forecasting, data preparation, training, inference, evaluation, and hydrology-oriented advanced 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。

Router for FunASR speech workflows: local transcription, subtitles, services, vLLM, normalization, and training/export.

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。

Guides FastVideo video, image, and audio generation, config-first inference, serving, training, distillation, evaluation, and performance 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。

Guides Researchers through Faiss installation, dense and binary similarity search, index training and composition, persistence and evaluation, and explicitly gated CPU/GPU interoperability 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。

Routes official 3D Gaussian Splatting setup, data preparation, training, rendering, evaluation, and viewer 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。