すべてのClaudeスキルを閲覧

Review Comment Prioritizer

Streamlines code reviews by helping developers distinguish between actionable feedback and out-of-scope noise.

Google Workspace Connector

Integrates Google Workspace APIs directly into Claude's terminal to manage Gmail, Drive, Sheets, and Docs.

ClickHouse Analytics Patterns

Optimizes high-performance analytical workloads and data engineering using ClickHouse-specific design patterns and best practices.

GitHub Copilot Review Reporter

Extracts and organizes GitHub Copilot code review comments into structured markdown reports for streamlined PR management.

Catch Expected Errors Pattern

Implements resilient exception-handling strategies for search algorithms and data processing by gracefully skipping expected errors.

Verify with Inline Tests

Implements Norvig-style inline testing patterns to co-locate tests with code for instant verification and documentation.

Statistical Data Reporting

Generates human-readable, aligned tables and statistical summaries for reporting results and data comparisons.

Claude Skill Scaffolding Template

Generates standardized skill documentation and configuration files to streamline the creation of custom Claude Code extensions.

Deep Research

Conducts multi-step, iterative web investigations to produce comprehensive, structured research reports on any topic.

Python NamedTuple Records

Implements immutable, memory-efficient data structures using Python's namedtuple for cleaner and more readable code.

Python Operator Overloading DSL

Enables the creation of expressive domain-specific languages in Python by overloading arithmetic and logical operators.

Adversarial Testing Patterns

Provides a curated library of realistic adversarial attack vectors and edge cases to harden code logic against semantic failures.

Edge Case Handler

Implements robust error handling and boundary checks to prevent crashes and ensure code reliability.

Python Memoization Patterns

Optimizes Python functions by implementing memoization and dynamic programming patterns to eliminate redundant recursive computations.

Iterate with Itertools

Implements memory-efficient combinatorial iteration patterns in Python using the itertools library.

Table-Driven Rules Pattern

Replaces complex conditional logic with extensible data structures to improve code maintainability and readability.

Python Comprehension Chain Patterns

Implements elegant, idiomatic data transformations using Pythonic list, dictionary, and set comprehensions inspired by Peter Norvig.

Pass Function as Argument Pattern

Implements generic algorithms by passing functions as parameters to enable the strategy pattern and configurable behavior.

Adversarial Code Analysis

Analyzes codebases to identify realistic boundary conditions and vulnerability surfaces for targeted adversarial testing.

Function Decomposition Pattern

Decomposes complex functions into smaller, testable helper functions based on Peter Norvig’s clean code patterns.

Parallel Workflow Retrospective

Analyzes completed parallel git workflows to identify bottlenecks, evaluate planning accuracy, and provide actionable process improvements.

Norvig Sparse Set Pattern

Implements memory-efficient sparse data structures using Python sets for infinite grids and large coordinate spaces.

Local LLM Management

Manages local Ollama LLM models for development, testing, and VRAM optimization within Claude Code workflows.

Local Search Optimizer

Solves NP-hard optimization problems using greedy construction and iterative local improvement patterns.

Fallback Compatibility Patterns

Implements robust Python patterns for cross-version compatibility and graceful degradation of optional dependencies.

Propagate-Then-Search Logic Pattern

Optimizes constraint satisfaction problem-solving by eliminating impossibilities through inference before initiating recursive search operations.

Norvig Grid & Maze Solver

Implements elegant dictionary-based coordinate patterns for solving complex 2D grid problems and spatial simulations.

Documentation Testing & Audit

Identifies gaps, broken instructions, and missing prerequisites in project documentation to ensure seamless developer onboarding.

Stable Matching & Gale-Shapley

Implements the Gale-Shapley algorithm to solve stable matching problems for two-sided markets like residency and admissions.

Capture State Closure Pattern

Implements persistent state using closures and factory functions as a lightweight alternative to classes.