Skill UI

Automates the conversion of meeting notes from Granola into structured development artifacts. This pipeline uses Zapier and GitHub Actions to automatically extract action items and decisions, creating corresponding issues in GitHub, tasks in Linear, and summarizing updates in Slack. It ensures that meeting outcomes are immediately tracked and integrated into the development workflow.

Enhances screenshots and digital photos by analyzing quality, upscaling resolution, sharpening edges, and reducing artifacts so they stay presentation-ready for docs, slides, or social media.

Automates survey creation within business workflows, offering guidance, best practices, and production-ready artifacts whenever a request mentions survey creator patterns or related automation needs.

This skill aggregates a user's entire year's work—including GitHub PRs, Notion documents, and Linear tickets—into a single, structured Notion document. It automatically groups these artifacts by project, providing a comprehensive and organized summary ideal for professional reviews or annual performance documentation (e.g., SRED preparation).

This tool functions as a post-task epilogue, designed to audit and record the entire research journey. It scans the conversation history at the end of a coding or research session to extract critical research artifacts, including decisions, experiments, dead ends, claims, heuristics, and major pivots. It systematically writes this provenance data into the structured `ara/` directory, ensuring a faithful and auditable trace of how the project truly evolved.

This skill summarizes the current conversation into a structured handoff document, ideal for passing work to a fresh AI agent or starting a new session. It avoids content duplication by referencing existing artifacts (PRDs, plans, issues, commits) using their paths or URLs, ensuring the receiving agent has a concise and actionable overview of the project's state, open decisions, and next steps.

This skill converts raw Markdown or JSON artifacts (such as academic reports, literature reviews, or progress dashboards) into a single, polished, and highly readable HTML file. It is designed to take machine-parseable source material and present it in a professional web view for human consumption, while strictly maintaining the source MD/JSON as the canonical truth. Supports various templates (academic, dashboard) and features XSS sanitization.