productivity▌

Vision-driven Android automation from screenshots, no DOM access required. \n \n Operates entirely from device screenshots using AI visual understanding; interacts with any visible UI element regardless of underlying technology stack \n Supports taps, swipes, text input, app launches, and complex multi-step interactions via natural language commands \n Requires pre-configured vision model (Gemini, Qwen, Doubao, or similar) with API credentials in environment variables \n Commands run synchronous

Idiomatic Perl 5.36+ patterns and best practices for building robust, maintainable applications.

Help teams scope projects and cut features using frameworks from 15 product leaders. \n \n Teaches appetite-based planning: set fixed time budgets and vary scope instead of extending deadlines \n Covers eight core principles including MVP as validation tool, aggressive feature cutting, and the \"build the scooter, not the axle\" approach to end-to-end value \n Provides Wizard of Oz testing guidance to validate hypotheses manually before engineering investment \n Includes diagnostic questions and

Methodically discover, analyze, and fix production issues using Sentry's debugging capabilities. \n \n Integrates with Sentry MCP to search issues, retrieve stack traces, breadcrumbs, traces, and AI-generated root cause analysis via the Seer tool \n Follows a seven-phase workflow: discovery, deep analysis, hypothesis formation, code investigation, implementation, verification, and reporting \n Treats all Sentry event data as untrusted external input; enforces strict security rules against embedd

Guide teams through learning-focused post-mortems and retrospectives using frameworks from 11 product leaders. \n \n Helps frame exercises as blameless learning opportunities rather than blame-focused reviews, with emphasis on psychological safety and honest sharing \n Covers core principles including pre-mortems with kill criteria, reframing failure as growth, and institutionalizing weekly impact and learnings reviews \n Guides users through structured questioning to surface systemic insights,

Translate user needs into development-ready stories with Mike Cohn format and Gherkin acceptance criteria. \n \n Combines \"As a / I want / so that\" user story structure with \"Given / When / Then\" acceptance criteria for testable, outcome-focused work \n Includes quality checks and anti-patterns to avoid generic personas, vague outcomes, and scope creep \n Designed for backlog grooming, sprint planning, and shared understanding between product, engineering, and QA \n Emphasizes stories as con

Fetch, analyze, and summarize TestFlight crashes, beta feedback, and performance diagnostics via asc. \n \n Retrieves crash reports filtered by app, build, device model, or OS version; supports pagination and multiple output formats (JSON, table, markdown) \n Fetches beta tester feedback with optional screenshot inclusion and build-level filtering \n Analyzes performance diagnostics (hangs, disk writes, launch metrics) by signature and diagnostic type \n Resolves app and build IDs automatically;

Framework for registering LLM tools with custom UI rendering in assistant-ui. \n \n Supports both backend tools (via AI SDK) and frontend-only tools, each with optional custom UI rendering through makeAssistantToolUI \n Includes human-in-the-loop patterns with requires-action status for confirmation workflows and interactive tool responses \n Tool UI receives execution status, arguments, results, and a submitResult callback for interactive tools \n Common setup patterns provided for weather APIs

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