Skill UI

Manage microscopy data with the OMERO Python API: access projects/datasets/images, retrieve and analyze pixel data, handle ROIs and annotations, store results in tables, and automate batch workflows for high-content screening.

Apify Actorization is a process that transforms existing, standalone software or scripts (like CLI tools) into standardized, reusable serverless applications called Actors. Actors are packaged as Docker images, accepting structured JSON inputs and producing structured JSON outputs. This skill guides developers on migrating projects to the Apify platform, ensuring compatibility, and enabling scalable, event-driven data processing pipelines.

This skill helps users create a new Git branch that strictly adheres to established project naming conventions (e.g., Sentry standards). It automatically determines the appropriate branch type (such as `feat`, `fix`, `chore`, etc.) and generates a descriptive, standardized branch name by analyzing the provided work description or local code differences (diff). It guides the entire branching workflow, ensuring proper lineage and classification for code submissions.

This skill assists developers in understanding, writing, and validating EAS workflow YAML files for Expo projects. It provides comprehensive guidance on CI/CD pipelines, job definitions, triggers, and expression syntax, ensuring configurations are accurate and up-to-date by referencing official Expo schemas. Use this when automating builds or deployments via EAS.

This guide outlines a systematic process for transforming any recurring technical pattern, complex debugging solution, or workflow into a standalone, portable skill. It details the multi-step extraction workflow, including pattern identification, structured documentation generation (SKILL.md), and quality control checks to ensure the skill can be reused across different projects.

This skill outlines a comprehensive methodology for designing, building, and deploying robust LLM-powered applications. It covers assessing task-model fit, designing modular pipelines (acquire -> prepare -> process -> parse -> render), and managing state using file systems to ensure scalability and reliability in AI systems.

This skill provides a robust and safe mechanism for updating SpecKit templates and configurations from GitHub. It is designed to automatically apply the latest changes while meticulously preserving any existing project customizations, ensuring a non-disruptive workflow. It features a conversational, two-step approval process, guiding the user through the update process, reporting conflicts, and handling fresh installations gracefully. Ideal for developers needing reliable, version-controlled updates to their SpecKit environment.

Automates project-aware diary maintenance by recording status from conversations, refreshing context, extracting global and project progress, and syncing to Notion/Obsidian while enforcing strict workflow steps.

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.

Vibers provides a human code review workflow for AI-generated GitHub projects; add the collaborator, GitHub Action, and commit guidance to receive spec- and security-focused feedback plus follow-up PR fixes.

This skill systematically audits a given project by analyzing its historical memory, rollout summaries, and local skill definitions. It identifies repeated complex workflows, recurring operational patterns, or areas where existing skills are stale. The audit process guides the user to recommend the highest-value skills to either add (if a new pattern emerges) or update (if current skills are outdated or incomplete), ensuring the AI agent operates with the most accurate and context-specific knowledge base for optimal project execution.

This tool serves as a persistent memory and state manager for complex coding projects within the Claude environment. It allows users to initialize new projects with structured metadata, track detailed session progress (including summaries, decisions, and next steps), and save the entire context reliably. By ensuring project continuity across disconnected sessions, it drastically improves the flow and consistency of the development workflow.