Automated dependency update execution with breaking change detection and rollback capability
Skills(SKILL.md)は、AIエージェント(Claude Code、Cursor、Codexなど)に特定の能力を追加するための設定ファイルです。
詳しく見る →Automated dependency update execution with breaking change detection and rollback capability
Generate documentation for migrated systems including API docs, architecture docs, and runbooks
Extract domain models from monolithic codebases using DDD principles for microservices decomposition
Generate Infrastructure as Code from existing infrastructure with Terraform/CloudFormation support
Extract tribal knowledge from code, documentation, and commit history to preserve institutional memory
Understand and document legacy code behavior through deep analysis and behavior characterization
Automated license compliance verification for dependencies to ensure legal compliance during migration
Validate functional equivalence after migration with side-by-side comparison and behavioral verification
Generate OpenAPI specifications from code or legacy APIs with schema inference and documentation
Capture performance baselines before migration for regression comparison and SLA verification
Suggest and guide application of refactoring patterns to improve code quality
Automate rollback procedures with state snapshots, rollback scripts, and verification automation
Compare database schemas between source and target environments for migration planning
Convert SOAP web services to REST APIs with WSDL parsing and resource modeling
Deep static analysis of codebases for quality, complexity, and migration readiness assessment
Orchestrate strangler fig pattern implementation for gradual migration with traffic routing and cutover management
Measure, categorize, and prioritize technical debt for migration planning and remediation
Security vulnerability scanning for dependencies and code, with CVE database checking and risk assessment
Cross-chain bridge and multi-chain development expertise. Supports LayerZero, Chainlink CCIP, Wormhole, and Axelar for omnichain messaging, token bridging, and cross-chain state verification.
Advanced gas optimization techniques for EVM smart contracts. Covers storage packing, memory vs calldata optimization, assembly/Yul, efficient data structures, batch operations, and benchmark-driven optimization strategies.
Expert usage of Hardhat for smart contract development, testing, and deployment. Includes TypeChain generation, plugin ecosystem, network forking, and deployment management.
Symbolic execution analysis using Mythril for deep vulnerability detection in smart contracts. Supports configurable transaction depth, timeout settings, and proof-of-concept exploit generation.
Expert usage of OpenZeppelin Contracts library for secure smart contract development. Covers access control, token standards, governance, upgrades, and security utilities.
Expert integration with Slither static analyzer for smart contract vulnerability detection, code quality analysis, and security reporting. Supports all Slither detectors and custom analysis configurations.
Deep expertise in Solidity language features, patterns, and best practices for secure smart contract development. Covers ERC standards, gas optimization, upgradeable contracts, and security patterns.
Subgraph development for The Graph protocol. Includes manifest configuration, GraphQL schema design, AssemblyScript handlers, entity relationships, and deployment to hosted and decentralized networks.
Token economics simulation and analysis. Supports supply modeling, staking mechanisms, liquidity mining, governance dynamics, agent-based simulations, and cadCAD integration.
Wallet connection and transaction management for dApps using wagmi and viem. Supports multiple connectors, chain switching, EIP-712 signing, and hardware wallet integration.
Zero-knowledge circuit development using Circom and Noir languages. Supports constraint optimization, ZK-friendly cryptographic primitives, proof generation (Groth16, PLONK), and Merkle tree implementations.
Performs statistical analysis for A/B testing experiments
Analyzes, validates, and optimizes Apache Airflow DAGs for reliability, performance, and best practices adherence.
Analyzes and optimizes Apache Spark jobs for performance, cost, and resource utilization
Generates semantic layer definitions for BI tools from dimensional models
Implements Change Data Capture patterns for real-time data integration
Extracts and maps data lineage from various sources including SQL, dbt, Airflow, and Spark, generating comprehensive lineage graphs for impact analysis.
Profiles data assets to assess quality dimensions, detect anomalies, and generate comprehensive data quality reports with actionable recommendations.
Analyzes dbt projects for best practices, performance, maintainability, and generates actionable recommendations for improvement.
Validates dimensional models against Kimball methodology best practices
Generates Great Expectations suites from data profiles and business rules
Designs and optimizes Apache Kafka topics and configurations
Designs and optimizes One Big Table (OBT) patterns
Generates Slowly Changing Dimension implementations across platforms
Manages schema evolution and compatibility across data systems
Analyzes and optimizes SQL queries across different data warehouse platforms (Snowflake, BigQuery, Redshift, Databricks) with platform-specific recommendations.
Data quality validation skill using Great Expectations for schema validation, expectation suites, data documentation, and automated data quality checks in ML pipelines.
MLflow integration skill for experiment tracking, model registry, and artifact management. Enables LLMs to log experiments, compare runs, manage model lifecycle, and retrieve artifacts through the MLflow API.
Model documentation skill for generating model cards following Google's model card framework.
Optuna integration skill for automated hyperparameter optimization with advanced search strategies, pruning, multi-objective optimization, and visualization capabilities.
Scikit-learn model training skill with cross-validation, hyperparameter tuning, pipeline construction, and model serialization. Enables automated ML model development using scikit-learn's comprehensive toolkit.
Configure electron-updater with code signing verification, delta updates, staged rollouts, and multiple update channels for Electron applications