Visual, example-driven guide for using Claude Code: structured learning path, copy‑paste templates, and diagrams that show how to combine slash commands, hooks, subagents and MCP into production workflows.
Provides adaptive workflow steering rules for AI coding agents to guide development across Inception, Construction, and Operations phases. Includes opt-in extensions (security, testing), IDE/agent integrations (Cursor, Kiro, Amazon Q, Copilot, Claude), and human-in-the-loop approval points.
Runs recurring workplace tasks across 100+ tools (Slack, GitHub, Gmail, Notion, Linear) as scheduled sub-agents that triage errors, draft outreach, and compile daily briefs. Each run executes in an isolated Firecracker microVM with scoped permissions.
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.
Orchestrates multi-model LLM agents and developer workflows as an OpenCode plugin — runs background specialists, LSP/AST-aware refactors, hash-anchored edits, and built-in MCPs. Designed for agent-driven code automation and multi-model orchestration.
Coordinates about a dozen role-based AI agents — analyst, architect, developer, QA, scrum master — through a CLI, taking a feature from PRD and architecture docs into an automated dev cycle. Runs inside Claude Code, Cursor, Codex, or Gemini.
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.
Generates natively editable PPTX from PDFs, DOCX, URLs, or Markdown — producing real PowerPoint shapes, text boxes, and charts (not images). An open-source, model-agnostic, local-first pipeline that integrates with multiple AI editors while keeping your data on-device.
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.
Drives AI coding agents through a five-phase loop — discuss, plan, execute, verify, ship — offloading heavy work to fresh-context subagents to fight context rot. The main session stays lean while parallel waves do the building.
Coordinates multiple AI coding agents and persists work state in git-backed hooks; provides convoy-based work tracking, an AI coordinator (Mayor), agent lifecycle/watchdog tooling, and a merge/refinery workflow for reliable multi-agent code work.
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.