Provides a frontend-design skill plus 20 steering commands and curated anti-patterns to steer LLMs toward clearer, accessible UI designs. Designed to plug into AI harnesses (Cursor, Claude/Gemini CLI, code agents) for auditing, critiquing, and polishing interfaces.
Defines a vendor-neutral JSON/YAML semantic model specification and tooling to exchange metrics, dimensions, lineage and other business semantics across analytics, AI and BI platforms; includes a core spec, validators, converters (dbt, GoodData, Salesforce) and example models.
Wraps LangGraph in an opinionated harness giving an agent planning, file read/write, sub-agent delegation, and persistent memory out of the box. Aimed at long-horizon work where plain ReAct loops exhaust their context window.
Forecasts how social scenarios might unfold by running multi-agent simulations: thousands of LLM agents with memory and personalities, seeded from real data, that you steer by injecting variables to 'rehearse the future' in a digital sandbox.
Normalizes wearable data — heart rate, sleep, activity, steps — from Garmin, Whoop, Apple Health and more behind one self-hosted API, so you write one integration instead of one per provider. Natural-language AI health automations are planned.
Cross-platform GTD task manager for desktop, mobile, and web that covers the full Capture→Clarify→Organize→Reflect→Engage workflow with a local-first data model and flexible sync backends. Optional BYOK AI copilot and automation (CLI, REST API, MCP) help clarify and break down tasks.
Provides a CLI-first framework to orchestrate autonomous AI agents and development workflows. Includes role-based agents, the ADE execution pipeline, IDE hooks and an NPX installer for quick setup—best for teams automating planning→development→QA.
Provides a Plan→Work→Review→Release harness for Claude Code agent workflows that enforces spec-driven tasks, TDD-backed implementation, independent review, and packaged evidence for PRs/releases. Exposes plugin/CLI commands and a Go-native guardrail engine to keep agent-driven code delivery reproducible and auditable.
Packages reusable agent capabilities as lightweight 'skills' (folders with a SKILL.md) that capture procedural knowledge and workflows; uses progressive disclosure so agents load minimal metadata at discovery and fetch full instructions and resources only when needed.
Equips AI coding assistants like Claude Code and Cursor with 75+ executable tools, an MCP server, reusable skills, and a Python library to build on Databricks—Spark pipelines, jobs, dashboards, Unity Catalog resources, and ML workflows—from your editor.
Runs untrusted AI-agent code, commands, and file operations inside isolated sandboxes that scale from local Docker to Kubernetes. One Sandbox Protocol unifies both runtimes, with gVisor, Kata, and Firecracker isolation and SDKs across five languages.
Defines a standardized commerce protocol so platforms, merchants, PSPs and credential providers can declare capabilities, discover services, manage checkouts, exchange payment tokens, and enable AI agents to perform end-to-end purchase flows.