Skill UI

Analyzes module coupling using the three-dimensional model from Balancing Coupling in Software Design to evaluate dependency strength, distance, and volatility, pinpoint architectural smells, and guide decoupling or integration decisions.

Guides CTOs and tech leaders through strategy, architecture governance, team scaling, and crisis response, with workflows for technical debt assessment, ADR creation, and vendor evaluation to keep engineering healthy.

A comprehensive guide for selecting and optimizing embedding models for advanced vector search and retrieval-augmented generation (RAG) applications. This guide covers model comparison, chunking strategies, dimension reduction techniques, and provides runnable Python templates for both OpenAI and local Sentence Transformer implementations.

Formal evaluation framework for Claude Code sessions that applies evaluation-driven development to define capability and regression checks, run deterministic and model graders, and track pass@k/pass^k reliability metrics before shipping.

A comprehensive toolkit for evaluating the performance of code generation models. It supports benchmarking against industry standards like HumanEval, MBPP, and MultiPL-E across multiple languages. Use this tool to quantitatively compare different LLM coding abilities and measure code synthesis quality using pass@k metrics.

Evaluates large language models across 60+ academic benchmarks (MMLU, HumanEval, GSM8K, TruthfulQA, HellaSwag) with standardized prompts and metrics, making it easy to benchmark HuggingFace or API models, compare releases, and track training progress.

Provides detailed evaluation of machine learning models using plugin metrics to assess accuracy, precision, recall, F1, and other key indicators, helping users compare models and validate performance before deployment.

Provides comprehensive guidance on building robust evaluation frameworks for complex agent systems. It addresses the inherent challenges of non-determinism and multiple valid paths, enabling systematic performance testing, validating context engineering choices, and measuring continuous improvements across multiple quality dimensions (e.g., factual accuracy, tool efficiency).

A comprehensive guide to diagnosing and resolving common errors encountered when using the Evernote API. It covers three main exception types—User, System, and Not Found—and provides practical solutions, including ENML validation, rate limit retry mechanisms, and safe resource retrieval patterns, ensuring robust application integration.

A comprehensive workflow for implementing advanced search and retrieval features within Evernote. It allows users to build complex queries using search grammar, filter notes by tags, notebooks, dates, and completion status, and paginates through large result sets. The service also enriches results by mapping GUIDs to human-readable names for notebooks and tags.

A comprehensive diagnostic tool designed for deep debugging of complex Exa system issues. It systematically checks multiple layers (DNS, Authentication, Neural Search, Content Retrieval, etc.) and provides detailed latency profiling (p50, p95) to pinpoint performance bottlenecks or intermittent failures. Use this when standard troubleshooting steps fail or when preparing detailed evidence for support escalation.

A comprehensive workflow for executing advanced, semantic web searches using Exa. This tool moves beyond simple keyword matching, enabling deep context retrieval by understanding the intent behind the query. Key features include RAG context retrieval, content extraction (summaries, highlights, full text), and robust filtering by date range, specific domains, and content categories. Ideal for building knowledge base search features and complex research pipelines.