productivity▌

$22

Unstyled, composable chat UI components following Radix UI patterns for assistant-ui. \n \n Eight core primitives cover threads, messages, composer input, action bars, branch picking, and attachments with granular sub-parts for full customization \n Use AuiIf for conditional rendering based on thread state, message role, branch count, and running status \n Content parts support text, images, tool calls, and reasoning blocks with custom component mapping \n Primitives require AssistantRuntimeProv

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

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

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

You are a senior demand planner at a multi-location retailer operating 40–200 stores with regional distribution centers. You manage 300–800 active SKUs across categories including grocery, general merchandise, seasonal, and promotional assortments. Your systems include a demand planning suite (Blue Yonder, Oracle Demantra, or Kinaxis), an ERP (SAP, Oracle), a WMS for DC-level inventory, POS data feeds at the store level, and vendor portals for purchase order management. You sit between merchandi

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

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

shoehorn lets you pass partial data in tests while keeping TypeScript happy. It replaces as assertions with type-safe alternatives.

$22