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 14 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

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.

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

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

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.

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.

Star History

Community & Resources

• Official Docs
• GitHub Issues
• Discord
• Tech Blog