productivity▌

Fast web search for current information, research, and fact-finding across the internet. \n \n Executes single objective-based queries or multiple keyword searches in parallel, returning up to 10 results with excerpts and metadata \n Supports time-sensitive filtering via --after-date and domain-specific searches with --include-domains \n Outputs structured JSON with titles, URLs, publish dates, and excerpts for easy parsing and follow-up queries \n Requires inline citations for every claim using

Create, test, and iteratively improve AI agent skills with structured evaluation and benchmarking. \n \n Guides you through the full skill development lifecycle: intent capture, draft writing, test case creation, evaluation, and iteration based on user feedback \n Runs quantitative benchmarks comparing skill-enabled vs. baseline performance, with timing and token usage analysis across multiple test cases \n Generates an interactive browser-based review tool for qualitative evaluation — side-by-s

Repository-grounded threat modeling that maps trust boundaries, assets, and abuse paths to concrete code evidence. \n \n Enumerates entry points, data flows, and trust boundaries anchored to actual repository structure and configuration \n Derives realistic attacker goals tied to specific assets (credentials, PII, integrity-critical state, compute resources) rather than generic checklists \n Prioritizes threats using likelihood and impact reasoning, with explicit assumptions about deployment, au

You are a senior returns operations manager with 15+ years handling the full returns lifecycle across retail, e-commerce, and omnichannel environments. Your responsibilities span return merchandise authorization (RMA), receiving and inspection, condition grading, disposition routing, refund and credit processing, fraud detection, vendor recovery (RTV), and warranty claims management. Your systems include OMS (order management), WMS (warehouse management), RMS (returns management), CRM, fraud det

Help teams build cultures of intense internal product usage to uncover real user pain points. \n \n Focuses on two core practices: requiring all team members to become active users of their own product, and using it intensely every day for real work rather than demo testing \n Provides a four-step implementation framework: assess current usage levels, identify gaps in firsthand experience, design systems that make dogfooding natural and required, and measure impact on product decisions \n Flags

Build strong engineering culture using frameworks from 19 product leaders. \n \n Diagnose current state across team size, practices, and pain points, then identify bottlenecks in developer experience, org structure, talent, or process \n Core principle: Conway's Law means organizational structure directly dictates architecture and product quality; align teams to desired outcomes \n DevEx is foundational; optimize for flow state, cognitive load, and feedback loops rather than toolchain selection

4-tier autonomous self-healing system for OpenClaw Gateway with Claude Code as AI emergency doctor. \n \n Escalates through watchdog monitoring, HTTP health checks with retries, AI-powered diagnosis via Claude Code, and Discord/Telegram alerts for human intervention \n Captures persistent learning documentation (symptom to solution mappings) and reasoning logs for explainable AI decision-making \n Includes metrics dashboard for tracking recovery success rates, timing, and trends across incidents

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;

Guides implementation of Schema.org structured data (JSON-LD) for rich snippets, enhanced search results, and Generative Engine Optimization (GEO).

Frameworks for developing and coaching product managers using proven practices from top-company leaders. \n \n Establishes competency models and definitions of \"good\" before attempting to develop any PM, ensuring coaching is contextual to company stage and needs \n Uses Bloom's Taxonomy to diagnose where PMs fall on the critical thinking spectrum (knowledge through evaluation) and targets development accordingly \n Provides a five-step coaching framework: define success, assess current state,