multi-agents — Cursor AI Agent
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    multi-agents

    multi-agents

    JahnelGroup February 26, 2026
    1 copies 0 downloads

    A three-tier training curriculum and ready-to-copy pipeline for building multi-agent AI workflows in Cursor and Claude Code.

    multi-agents

    Shared multi-agent pipeline setup for Cursor. Provides standard agents, rules, and pipeline tooling that teams copy into their projects. Claude Code support is documented alongside Cursor conventions.

    Prerequisites

    • Cursor with models enabled for the agents in the pipeline. Some models (e.g. gpt-5.1-codex-max) are hidden by default and must be toggled on in Cursor Settings > Models. See Models | Cursor Docs for the full list and visibility defaults.
    • Python 3.10+, Node.js 20+, and git for tutorials and the sandbox project. See TESTING.md for details.

    Getting started

    Can you explain what agents, rules, and pipelines are? If not, start with Foundation.

    Can you set up a pipeline and build a feature end-to-end? If not, go to Practitioner.

    Ready to design multi-agent systems and lead others? See Expert.

    The .cursor-foundation/, .cursor-practitioner/, and .cursor-expert/ directories are the canonical source bundles in this repo. Use them for training and for copying into your project.

    Tier directory layout

    Each .cursor-<tier>/ directory follows this structure (not all tiers include every subdirectory):

    PathPurpose
    agents/*.mdOne file per agent defining its model, role, inputs, outputs, and behavioral instructions. Cursor discovers these as subagent_type targets.
    rules/*.mdcAlways-on or file-triggered behavioral guardrails injected into agent context automatically. YAML frontmatter sets description, alwaysApply, and optional globs.
    skills/*/SKILL.mdOn-demand capabilities agents pull in when relevant (unlike rules, not auto-injected). Each skill is a subdirectory with a SKILL.md. Foundation has none.
    pipeline/Runtime tooling: schema.py (validates artifact JSON), check.py (stage-gate invariants), `R

    Tags

    agentsaiartifactsautomationclaude-codecode-reviewcost-optimizationcurriculumcursordebugging

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