productivity▌

End-to-end WhatsApp integration with onboarding, messaging, templates, interactive messages, and native Flows. \n \n Onboard customers via hosted setup links with CLI or direct API; detect connections through webhooks or redirect URLs \n Send text messages, templates (with named parameters and buttons), interactive messages, and media; read inbox data via CLI, SDK, or API \n Build native WhatsApp Flows (forms) with static or dynamic endpoints; manage encryption, versioning, and testing \n Receiv

Write correct, performant SQL across all major data warehouse dialects. \n \n Covers five major dialects: PostgreSQL, Snowflake, BigQuery, Redshift, and Databricks with dialect-specific syntax for date/time, string functions, arrays, and JSON handling \n Includes common analytical patterns: window functions, CTEs, cohort retention, funnel analysis, and deduplication with ready-to-use examples \n Provides performance optimization tips per dialect, such as clustering keys in Snowflake, partition p

Establish bidirectional links between Figma design components and code implementations using Code Connect. \n \n Requires Figma MCP server connection and published components on Organization or Enterprise plans \n Automatically identifies unmapped components in Figma selections, then scans your codebase for matching code components by name, structure, and props \n Presents matched components to you for review before creating mappings, allowing selective acceptance or rejection \n Supports multip

Analyze images using Gemini's vision capabilities for OCR, UI analysis, and visual understanding. \n \n Supports PNG, JPEG, GIF, and WebP images including screenshots, diagrams, charts, and code snippets \n Built-in analysis templates for common tasks: text extraction, code recovery, UI/UX feedback, error diagnosis, and data extraction from charts \n Handles single and multiple image comparisons in a single request \n Requires Google Generative AI library and valid GEMINI_API_KEY environment var

Framework for evaluating emerging technologies using insights from 22 product leaders. \n \n Start by clarifying the problem being solved, then assess technology maturity and stability for your specific use case \n Adopt a \"build and buy\" mindset: purchase tools for standard 90% functionality, build custom solutions for your unique 10% \n Prioritize mental bandwidth and core competencies over cost savings; constantly re-test assumptions about what new tools can actually do \n Design for modula

Scans dependency lockfiles for known vulnerabilities and generates severity-ranked findings with remediation guidance. \n \n Discovers and scans all common lockfile formats (package-lock.json, yarn.lock, go.sum, Gemfile.lock, and others) across your repository \n Identifies CVEs in dependencies and assigns severity levels to help prioritize remediation \n Analyzes exploitability of each vulnerability candidate to distinguish high-risk from low-risk findings \n Generates a structured scan report

Multi-agent DAG execution framework for decomposing large features into independently verifiable work units with quality gates. \n \n Structures complex features as directed acyclic graphs with explicit dependencies, complexity tiers (isolated edits to schema/security changes), and per-unit acceptance criteria \n Enforces a seven-stage pipeline: RFC intake, DAG decomposition, unit assignment, implementation, validation, merge queue processing, and final system verification \n Implements merge qu

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Search, install, update, and publish agent skills via the ClawHub CLI. \n \n Commands cover skill discovery, installation with version pinning, bulk updates with hash-based matching, and publishing to the registry \n Supports authentication for publishing via clawhub login and workspace configuration through environment variables or CLI flags \n Default registry is clawhub.com; configurable via CLAWHUB_REGISTRY or --registry flag \n Update command intelligently matches local file hashes to resol

Interactive step-by-step Medusa development bootcamp where you build a brands feature while learning architecture patterns. \n \n Three progressive lessons (2–3 hours total) covering modules, workflows, API routes, module links, workflow hooks, and admin UI customization \n Checkpoint verification after each major component tests conceptual understanding, code quality, and functionality before proceeding \n Treats errors as teaching opportunities; debugs together with diagnostic questions and ro