Skill UI

This skill provides a comprehensive command-line interface (CLI) for managing issues, projects, and teams within the Linear platform. Users can create, update, and track the status of issues, set project phases (e.g., backlog, in-progress), and manage complex workflows directly from the terminal, ensuring better traceability and project organization.

Set up comprehensive CI/CD pipelines for PostHog integrations using GitHub Actions. This guide covers mocked unit tests (no API key needed), robust integration tests against a development PostHog project, and automated deployment annotations to mark releases in your PostHog timeline. Ideal for developers ensuring data quality and traceability across all environments.

A comprehensive framework guiding feature planning and implementation through adaptive phases: Specify, Design, Tasks, and Execute. It enforces highly structured development practices by using EARS notation, atomic tasks, deterministic Conventional Commits, and mandatory code validation scripts. Features include requirement traceability, automatic verification, and a self-improving lessons layer, ensuring quality and compliance throughout the lifecycle.

This skill answers technical questions about a codebase by grounding all responses strictly within the provided source files. It is ideal for users who need to understand how a specific function works, locate definitions, or gain an overview of a component's implementation. All answers include inline citations to ensure accuracy and traceability.

Generates consulting-grade research reports in markdown, guiding users through framework creation and final synthesis across market, finance, brand, and industry domains with strict data traceability.

This guide establishes the Conventional Branch specification for naming Git branches. It defines standardized prefixes (e.g., feature/, bugfix/, hotfix/) and enforces strict rules (lowercase, kebab-case, no underscores) to ensure consistency across development teams. Use it to validate, create, or understand the correct naming structure for any new branch, improving project traceability and collaboration.

This tool facilitates comprehensive research by pulling relevant information from multiple Notion sources. It synthesizes scattered data into highly structured documentation, such as detailed briefs, comparative analyses, or executive reports. It ensures all claims are supported by accurate inline citations and links, making it ideal for market research, project documentation, or academic reporting where source traceability is critical.

This skill enables the creation and maintenance of highly structured, configurable research wikis. It manages source provenance, compiling raw source materials into organized concept pages, index maps, and derived artifacts. Ideal for systematically documenting complex knowledge bases, such as research notes, product documentation, or domain studies, ensuring full traceability and consistency.

A comprehensive, adaptive framework for developing and implementing features, adhering to a strict, multi-phase workflow: Specify, Design, Tasks, and Execute. This skill ensures feature quality through granular task breakdown, atomic Git commits, requirement traceability, and mandatory, independent verification. It automatically adjusts its depth based on feature complexity (Small to Complex), providing a robust mechanism for managing technical debt and capturing project lessons.

This skill executes the final two phases of the SPARC methodology: Refinement and Completion. It systematically reviews the implemented feature through rigorous code reviews, expands test coverage (unit, integration, edge cases), validates performance, generates a traceability matrix, creates comprehensive documentation, and confirms deployment readiness. It ensures the feature is robust, tested, and production-ready.

This workflow guides developers through Test-Driven Development (TDD) principles. It mandates writing tests (unit, integration, E2E) before writing implementation code, ensuring minimum 80% test coverage. The structured process (Red -> Green -> Refactor) guarantees robust code quality, effective bug fixing, and safe feature addition, while integrating Git checkpoint commits for full traceability.

This skill helps researchers strengthen AAAI paper submissions by ensuring reproducibility standards are met. It covers experimental traceability, seed reporting, compute disclosure, and dataset licensing. Users learn to map claims to evidence, audit checklists, and prepare artifacts for Phase-1 reviewers to validate rigor across AI scopes.