mindfold-ai/Trellis

An out-of-the-box engineering framework for AI coding.
_{AI writes code fast, but every session it starts from scratch — no memory of your project, your conventions, or your team's requirements. Trellis persists specs, tasks, and memory into your repo, so any coding agent works to your engineering standards.}

简体中文 • Docs • Quick Start • Supported Platforms • Use Cases

Why Trellis?

Capability	What it changes
Auto-injected specs	Write conventions once in .trellis/spec/, then let Trellis inject the relevant context into each session instead of repeating yourself.
Task-centered workflow	Keep PRDs, implementation context, review context, and task status in .trellis/tasks/ so AI work stays structured.
Project memory	Journals in .trellis/workspace/ preserve what happened last time, so each new session starts with real context.
Team-shared standards	Specs live in the repo, so one person's hard-won workflow or rule can benefit the whole team.
Multi-platform setup	Bring the same Trellis structure to 17 AI coding platforms instead of rebuilding your workflow per tool.

Prerequisites:

• Node.js >= 18
• Python >= 3.9

Quick Start

# 1. Install Trellis
npm install -g @mindfoldhq/trellis@latest

# 2. Initialize in your repo
trellis init -u your-name

# 3. Or initialize with the platforms you actually use
trellis init --cursor --opencode --codex -u your-name

See the Quick Start and Supported Platforms guides for setup details.

How to Use

The workflow is simple:

1. Describe what you want in natural language.
2. Brainstorm with the AI one question at a time until the PRD is clear, then implementation begins.
3. Let it run — the AI calls Trellis Implement and auto-checks the result against specs, lint, type-check, and tests.
4. Type /trellis:finish-work when the work is done or the session context fills up. Trellis archives the task and updates journals.

How It Works

Trellis runs a 4-phase loop with auto-invoked skills and sub-agents:

1. Plan — trellis-brainstorm walks through requirements one question at a time and writes prd.md. Research-heavy items go to a trellis-research sub-agent. The result is curated specs + research files referenced from implement.jsonl / check.jsonl.
2. Implement — a trellis-implement sub-agent writes code from the PRD with the curated context auto-injected, no git commit.
3. Verify — a trellis-check sub-agent reviews the diff against specs and runs lint, type-check, and tests, self-fixing where it can.
4. Finish — a final check runs, then trellis-update-spec promotes new learnings back into .trellis/spec/ so the next session starts smarter.

Resources

Need	Link
Install Trellis in a repo	Quick Start
Understand platform differences	Supported Platforms
See the workflow in practice	Real-World Scenarios
Start from spec templates	Spec Templates
Track releases	Changelog

FAQ

How is Trellis different from CLAUDE.md, AGENTS.md, or .cursorrules?

Those files are useful entry points, but they tend to become monolithic. Trellis adds scoped specs, task PRDs, workflow gates, workspace memory, and platform-aware generated files around them.

Is Trellis only for Claude Code?

No. Trellis is a project layer that works across multiple coding agents and IDEs.

Is Trellis for solo developers or teams?

Both. Solo developers use it for memory and repeatable workflow. Teams get the larger benefit: shared standards, task boundaries, reviewable context, and platform portability.

Do I have to write every spec file manually?

No. Many teams start by letting AI draft specs from existing code and then tighten the important parts by hand. Trellis works best when you keep the high-signal rules explicit and versioned.

Can teams use this without constant conflicts?

Yes. Personal workspace journals stay separate per developer, while shared specs and tasks stay in the repo where they can be reviewed and improved like any other project artifact.

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Community & Resources

• Official Docs
• GitHub Issues
• Discord
• Tech Blog

Official Repository • AGPL-3.0 License • Built by Mindfold