Skill UI

This skill automates the creation of comprehensive documentation for relational databases (PostgreSQL/MySQL). By introspecting live schemas, it extracts detailed metadata including table structures, column descriptions, primary/foreign key relationships, indexes, views, and stored procedures. The output provides a data dictionary, entity-relationship map, and schema statistics, crucial for knowledge transfer and database lifecycle management.

A comprehensive, production-ready toolkit for robustly debugging and monitoring Linear API integrations. This bundle includes features like an instrumented client wrapper for detailed request/response logging, a performance request tracer with summary statistics (avg, p95, max latency), a health check utility, and an environment validator. Ideal for developers setting up logging, performance monitoring, or debugging utilities in a live SaaS environment.

Automates Postmark transactional email workflows via Rube MCP’s Composio toolkit, handling templated batch sends, template edits, delivery statistics, bounce and complaint monitoring, and server configuration while enforcing current schema discovery.

Basic Text Processor provides word/character statistics, simple text transformations, batch file handling, and dual JSON or human-readable outputs to demonstrate a CLAUDE skill template.

Use Wireshark to capture and filter live traffic or PCAP files, inspect protocols, follow streams, and generate statistics for incident response, performance tuning, and protocol learning.

Offers MATLAB and GNU Octave guidance for matrix math, data import/export, visualizations, statistics, signal processing, differential equations, and general scripting—helpful when writing or translating MATLAB/Octave code and running scripts under either interpreter.

This skill provides a comprehensive automation solution for conducting red team phishing simulations using the Python gophish library. It streamlines the entire campaign lifecycle, including creating sophisticated email templates with tracking pixels, configuring SMTP profiles, importing user groups from CSV, launching targeted attacks, and generating detailed reports on open rates, click-through rates, and credential submission statistics for robust security awareness assessments.

A comprehensive tool supporting rigorous, evidence-bounded, and structured peer-review of scientific manuscripts, research protocols, and academic proposals. It guides reviewers through critical assessments of methods, statistics, ethical compliance, reproducibility, and claim-evidence alignment, while strictly enforcing confidentiality and human accountability throughout the review process.

This utility collects comprehensive diagnostic evidence for Algolia support tickets or internal troubleshooting. It gathers index statistics, API key access control lists (ACLs), recent query logs, and network diagnostic information using API endpoints and shell scripting. Use it when diagnosing search performance or unexpected service behavior.

A comprehensive tool designed to collect all diagnostic evidence for troubleshooting failed or problematic Apify Actor runs. This bundle gathers run metadata, detailed logs, dataset samples, usage statistics, and environment information, making it essential for preparing detailed support tickets or performing in-depth debugging.

This mechanism is designed to optimize Qdrant's performance when handling queries with very large limits or when paginating results across multiple shards. Instead of forcing every shard to transfer the full requested limit (which is bandwidth-intensive), it uses Poisson distribution statistics to calculate smaller, optimized limits for each shard. This significantly reduces inter-shard data transfer and improves the efficiency of large-scale vector search, while maintaining a very high degree of accuracy.

This tool analyzes Git diffs to provide comprehensive insights for Pull Requests (PRs) and large code changes. It calculates risk scores based on churn history and sensitive areas, classifies changes (e.g., feature, bugfix), recommends required reviewers, and provides detailed statistics. Use this before merging to assess code quality and minimize deployment risk.