Skill UI

A comprehensive toolkit for Product Owners practicing Agile methodologies. This guide covers generating INVEST-compliant user stories, defining testable Acceptance Criteria using Given-When-Then format, planning sprints, prioritizing the backlog using weighted scoring, and systematically breaking down large epics. Use it when defining requirements, managing the product roadmap, or preparing for sprint ceremonies.

This tool collects comprehensive diagnostic evidence for troubleshooting issues with the Apollo.io API. It generates a structured JSON bundle containing environment details (Node.js version, platform), connectivity test results, rate limit status, key type verification, and detailed reports on multiple core API endpoints. Essential for preparing detailed support tickets.

Prepares codebases for Trail of Bits security reviews by setting goals, running static analysis, expanding test coverage, pruning dead code, documenting build scope/access, and generating flowcharts, user stories, glossaries, and other reviewer-ready artifacts.

Prepares codebases for Trail of Bits security reviews by setting goals, running static analysis, expanding test coverage, pruning dead code, documenting build scope/access, and generating flowcharts, user stories, glossaries, and other reviewer-ready artifacts.

This comprehensive checklist guides users through validating and preparing Clay enrichment pipelines for a production launch. It covers critical areas including table schema finalization, data quality gates (validation, deduplication), credit cost management, webhook reliability testing, and establishing robust monitoring and alerting procedures before going live.

Collects comprehensive, sanitized debug evidence for Deepgram support and troubleshooting. This bundle gathers detailed system environment information (Node.js, Python), performs live API connectivity tests, analyzes audio file metadata using ffprobe, and logs request/response data. Essential for preparing detailed support tickets and reproducing bugs related to Deepgram integration.

This skill orchestrates a comprehensive, multi-stage Git development workflow, simulating the process from code commit to PR preparation. It leverages specialized AI agents (code reviewers, test automators, prompt engineers) to systematically enforce best practices. Key functions include code quality assessment, breaking change analysis, running unit/integration/E2E tests, and generating Conventional Commit messages, ensuring code readiness and high quality before merging.

This utility automatically collects comprehensive diagnostic data for the Granola application, including system specifications, audio configurations, network connectivity tests, and application metadata. It packages all findings into a single ZIP file for support teams. Use this when preparing a detailed support ticket or troubleshooting complex, persistent issues. The process is designed to collect diagnostic context without exposing sensitive meeting content, transcripts, or private API keys.

A comprehensive checklist for ensuring LangChain applications are robust, secure, and scalable for production environments. It covers critical areas including configuration management, advanced error handling, observability setup (e.g., LangSmith), performance optimization, security best practices (like prompt injection prevention and PII handling), rigorous testing, and zero-downtime deployment strategies. Use this guide when preparing for a major launch or auditing existing LLM systems.

Provides a comprehensive checklist for planning, scoping, executing, and wrapping up penetration tests, covering objectives, authorization, environment prep, scans, tester selection, methodologies, reporting, and remediation tracking.

Automates environment prep, service orchestration, and cleanup for executing integration test suites, covering database setup, migrations, and optional coverage reports.

Automates splitting datasets into training, validation, and test subsets by generating and running Python code so you can quickly prepare data for machine learning model training and evaluation.