データ・AIのスキル

Build a feature store using Feast for centralized feature management, configure offline and online stores for batch and real-time serving, define feature views with transformations, and implement point-in-time correct joins for ML pipelines. Use when managing features for multiple ML models, ensuring training-serving consistency, serving low-latency features for real-time inference, reusing feature definitions across projects, or building a feature catalog for discovery and governance.

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Defines the structure and implementation of service classes, enforcing the use of interfaces, ServiceImpl classes, DTOs for data transfer, and transactional management.

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Creates JSON Schema validation files for skills, agents, hooks, workflows, and data structures. Ensures type safety and input validation across the framework.

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 scientific research toolkit with 139 specialized skills for biology, chemistry, medicine, data science, and computational research. Transforms Claude into an AI research assistant with access to scientific databases, analysis tools, and domain-specific workflows.

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Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.

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Medusa v2 rules and best practices for modular commerce architecture, Workflow SDK, data models, services, and admin customizations.

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Use when you need to auto-log all tool interactions into a self-organizing vector DB with instant recall. Intercepts interactions, extracts key information, generates embeddings, stores in LanceDB, auto-categorizes, and builds a retrieval index. Inspired by OpenClaw SQLite LCM + summary DAG pattern.

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Autonomous fixed-budget ML experiment loop — setup, iterative hypothesis testing, git-based keep/discard tracking, and indefinite autonomous execution. Implements the karpathy/autoresearch protocol.

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Orchestrate multi-LLM parallel debate and synthesis. Dispatches prompts to available omega CLI wrappers in parallel, collects independent responses, runs anonymized peer review ranking, and synthesizes via a chairman model. Supports collaboration templates, idle watchdog, inter-agent messaging, worktree isolation, and multi-turn sessions. No server required.

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Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.

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Independent LLM judge evaluates task completion separately from the executing agent, catching false success claims by reviewing task goal, actions taken, final state, and evidence. Produces PASS/FAIL with confidence score and reasoning.

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Transforms vague UI/feature requests into structured, optimized prompts with design system awareness. Use when generating prompts for UI implementation, feature specification, or design-to-code translation. Triggers on tasks requiring prompt refinement, UI specification, or design system integration.

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Sets standards for Data Transfer Objects (DTOs), typically records, including parameter validation in compact canonical constructors.

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。

Data processing expert including parsing, transformation, and validation

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Generates architecture, database, and system diagrams using Mermaid syntax. Creates visual representations of system architecture, database schemas, component relationships, data flows, and standalone HTML exports.

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Background orchestrator that drains the system-wide cron-actions-queue.jsonl queue safely, preventing LLM context pollution in the primary router.

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Specifies best practices, including following RESTful API design principles, implementing responsive design, using Zod for data validation, and regularly updating dependencies. This skill promotes modern coding standards and helps developers write consistent, maintainable code.

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Benchmark external agent frameworks, auto-detect source type, scan for prompt injection, and convert findings into a concrete TDD upgrade backlog for agent-studio evolution.

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AI and ML expert covering PyTorch, TensorFlow, Hugging Face, scikit-learn, LLM integration, RAG pipelines, MLOps, and production ML systems

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Research-backed workflow to refresh existing agent prompts/frontmatter with diff-based risk scoring, TDD gates, and ecosystem validation.

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LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations

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Pure-Python knowledge base statistics dashboard. Reads shelf-index and log.md; emits Inventory, Layer distribution, Domain distribution, Recent Activity, and Staleness sections. No agent dispatch. Read-only.

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Health-check the project knowledge base. Looks for contradictions between files, stale claims that newer sources have superseded, orphan files with no inbound cross-references, important concepts mentioned but lacking their own page, missing cross-references, and data gaps. Returns a structured report; does not auto-fix.

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Manage the project's layer vocabulary — list allowed layers with file counts, add new layers, or safely remove unused layers. Reads the shelf-index for usage data; writes CLAUDE.md atomically. No agent dispatch.

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。