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onboard

Onboard developers to the Trellis workflow with a three-part interactive curriculum.

1|Updated Apr 13, 2026
Search Tags:#workflow#onboarding#guidelines#trellis#ai-memory#developer-onboarding
One-click install
npx skills add https://github.com/SDDKKK/Trellis_Hiskens_backup_20260413 --skill onboard-sddkkk
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: onboard
Source: https://github.com/SDDKKK/Trellis_Hiskens_backup_20260413/tree/main/.agents/skills/onboard
Command: npx skills add https://github.com/SDDKKK/Trellis_Hiskens_backup_20260413 --skill onboard-sddkkk

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This Skill eliminates the friction of bringing new contributors up to speed on the Trellis AI-assisted workflow by teaching the system's core philosophy, how project-specific knowledge is injected, and how to prevent context drift while ensuring human oversight before commits.

Core Features & Use Cases

  • Three-part interactive curriculum: Part 1 explains core philosophy (AI memory, project-specific specs, context drift) and the system structure; Part 2 walks through five real-world workflows step-by-step; Part 3 guides customizing and verifying .trellis/spec/ guidelines.
  • Practical verification and handoff: Demonstrates when to run $start, $before-dev, $check-*, $finish-work, and $record-session, and emphasizes that humans validate and commit changes.
  • Use Cases: New developer onboarding, preparing project-specific guidelines for AI, debugging sessions, code review fixes, and large refactors that require cross-layer checks.

Quick Start

Ask the onboard skill to walk me through Trellis core concepts, demonstrate the five example workflows, and help customize or verify the .trellis/spec/ guidelines for this repository.

Frequently Asked Questions about onboard

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What is the Trellis AI-assisted workflow and how does it handle project-specific specs?▼

The Trellis AI-assisted workflow uses project-specific specs to restore AI memory and prevent context drift. It injects guidelines from `.trellis/spec/` files to ensure the AI operates with accurate, project-specific knowledge during development and refactoring.

How do I onboard new developers to an AI-assisted workflow with project-specific guidelines?▼

To onboard developers, use an interactive curriculum that teaches core philosophy, demonstrates real-world workflows, and guides the customization of `.trellis/spec/` guidelines. This ensures new contributors understand AI memory injection and context drift prevention.

When do I need to run Trellis workflow commands like $before-dev and $finish-work?▼

You run `$before-dev` before starting development, `$check-*` during code review fixes or refactors, and `$finish-work` or `$record-session` when ending a task. These commands verify context drift and restore AI memory for the next session.

Does the Trellis onboarding workflow support debugging sessions and large refactors?▼

Yes, the Trellis workflow supports debugging sessions and large refactors by guiding developers through cross-layer checks. It uses interactive examples to demonstrate how to maintain human oversight and validate changes before committing.

How do I customize and verify `.trellis/spec/` files for my repository?▼

You customize and verify `.trellis/spec/` files by following the third part of the interactive onboarding curriculum. This process guides you through populating project-specific guidelines and verifying context drift prevention for your repository.

Why does AI context drift happen during development and how is human oversight maintained?▼

AI context drift happens when the AI loses track of project-specific constraints during long sessions. Human oversight is maintained by requiring developers to validate changes and manually commit, using commands like `$check-*` to verify alignment before finishing.

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