Skill UI

This skill is intended for Perl testing functionalities. Currently, the Japanese translation and content are under development, serving as a placeholder. Users should refer to the original English skill file for the core functionality and detailed implementation.

This skill provides sophisticated project estimation by adapting to human-only, hybrid, or agent-first development modes. It uses research-backed PERT statistics and confidence bands (P50, P75, P90) to forecast development timelines for tasks, backlogs, and capacity planning. It continuously calibrates estimates based on actual completion data, ensuring accuracy in AI-assisted and hybrid development environments.

This skill orchestrates the entire coding lifecycle as a state machine, ensuring tasks proceed from issue intake through implementation, structured review, deployment, and final acceptance verification. It enforces a rigorous 'Definition of Done' (DoD) proof, moving beyond simple code changes. Ideal for complex feature development requiring structured handoffs and explicit verification evidence.

This skill manages complex coding tasks from initial implementation through testing, review incorporation, deployment, and final runtime verification. It ensures the task meets explicit Definition of Done (DoD) criteria iteratively, minimizing the need for manual prompting across multiple development stages.

This skill enforces a mandatory, structured gate for starting new implementation tasks. It mandates that all work must be tracked via a GitHub issue, requiring explicit, testable acceptance criteria before any execution can begin. It defines a strict workflow (Problem, Goal, Scope, Acceptance Criteria, Status) to manage task readiness, ensuring that development only proceeds when criteria are fully defined and verifiable.

A structured methodology for de-risking product ideas and validating market opportunities. This framework guides teams through defining measurable outcomes, building Opportunity Solution Trees (OST), mapping critical assumptions (Desirability, Viability, Feasibility), and planning rigorous experiments (prototyping, interviews, usability tests) before committing to full development resources. Ideal for discovery sprints and pre-build validation.

This skill enforces rigorous governance and discipline when AI modifies systems, debugs code, or makes root-cause claims. It mandates safety gates (e.g., backup, blast radius check), demands evidence for every diagnosis, and prevents common AI shortcuts like guessing or deflecting. Use this for critical code modifications, complex debugging, and high-stakes deployments to ensure thoroughness and safety.

Guides building secure production-grade Electron desktop apps with isolated renderer processes, hardened IPC via preload bridges, packaging through electron-builder, code signing, and full auto-update workflows.

Coordinates Exa MCP-driven web_search_exa and get_code_context_exa calls for up-to-date web results, code snippets, and company or people intel whenever the user requests research, competitive analysis, or development context.

A forensic tool for performing post-mortem analysis on AI-assisted coding sessions. It goes beyond simply stating what was done, determining *why* work deviated (e.g., spec ambiguity, repo friction, agent error), identifying scope creep, and classifying rework patterns. It provides evidence-backed recommendations to improve prompts, codebase health, and future development workflows.

Opinionated Docker development assistant that analyzes Dockerfiles, docker-compose setups, multi-stage builds, and container security to shrink images, speed builds, and enforce production-ready practices when users need optimized containers.

A comprehensive strategy for regression testing in AI-assisted development. It focuses on identifying systematic blind spots, such as schema inconsistencies between sandbox and production paths, and integrating automated bug checks into the development workflow. This guide teaches how to build robust, database-free API tests to ensure code quality when AI models contribute to the codebase.