productivity▌

Behavioral interview framework drawn from 75 product leaders' hiring experience. \n \n Emphasizes behavioral-based questioning to reveal actual capability over rehearsed answers; drill six levels deep into the \"how\" to uncover technical expertise \n Guides role-specific competency definition, interview structure design, and question crafting that surface genuine insight rather than confident delivery \n Flags common mistakes: performative STAR responses, insufficient probing, hypothetical ques

Analyze screen recordings of manual processes and generate working automation scripts at multiple complexity tiers. \n \n Extracts frames and audio narration from video files, reconstructs the step-by-step workflow, and proposes automation tailored to the user's installed tools \n Offers three automation tiers: quick wins (shell aliases, one-liners), standalone scripts (bash, Python, Node), and full scheduled automation with logging and error handling \n Includes application-specific strategies

Create, manage, and upload evaluation datasets to LangSmith for testing and validation. \n \n Supports four dataset types: final_response (full conversations), single_step (individual node behavior), trajectory (tool call sequences), and RAG (question/chunks/answer/citations) \n CLI commands for dataset lifecycle management: create, list, get, delete, export, and upload from local JSON files \n SDK-based dataset creation in Python and JavaScript with programmatic example addition \n Example mana

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You are a senior energy procurement manager at a large commercial and industrial (C&I) consumer with multiple facilities across regulated and deregulated electricity markets. You manage an annual energy spend of $15M–$80M across 10–50+ sites — manufacturing plants, distribution centers, corporate offices, and cold storage. You own the full procurement lifecycle: tariff analysis, supplier RFPs, contract negotiation, demand charge management, renewable energy sourcing, budget forecasting, and

Collect and analyze real user feedback from Reddit, X, and GitHub to generate demand research reports. \n \n Scrapes feature requests, complaints, and questions across three platforms with filtering by topic, category, platform, and time range \n Includes search with real-time expansion, topic browsing, and sorting by popularity to identify top user demands \n Provides structured workflow from scope definition through data collection to formatted Markdown report generation \n Rate-limited API wi

Build evaluation pipelines for LangSmith with LLM-as-Judge and custom code evaluators. \n \n Three core components: creating evaluators (LLM-as-Judge or custom code), defining run functions to capture agent outputs and trajectories, and running evaluations locally or auto-running via uploaded evaluators \n Supports both offline evaluators (comparing run outputs to dataset examples) and online evaluators (real-time quality checks on production runs) \n Requires LangSmith API key and project confi

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Design and scale internal platforms by abstracting common capabilities and planning infrastructure before hitting limits. \n \n Focuses on understanding platform purpose, assessing organizational readiness, identifying leverage points, and designing for actual developer adoption rather than theoretical needs \n Emphasizes invisible infrastructure qualities like reliability, performance, and privacy as critical success factors alongside visible features \n Recommends proactive infrastructure scal

Run a read-only scan over repositories in a workspace root and rank cleanup chores by severity.