Lab Name

Onboarding Diagnostics Lab

Short Description

A layered onboarding validation and failure analysis toolchain that improves developer onboarding reliability by detecting, classifying, and resolving common setup and configuration issues in decentralized systems.

Scope of Lab

This lab focuses on building practical developer tooling to reduce onboarding friction and improve contributor success rates in decentralized environments.

The lab introduces a layered execution model:

Preflight Layer (zero-dependency)
A minimal bootstrap check that can run before project dependencies are installed, intended to validate core local prerequisites such as Node.js, npm, PATH availability, and basic environment readiness.
CLI Diagnostics Layer (idoa doctor)
A Node.js-based CLI for deeper onboarding validation, including dependency checks, configuration checks, local environment verification, failure classification, and actionable remediation guidance.
Adapter Layer
System-specific onboarding checks that build on the shared diagnostics core. The initial reference adapter will target Hyperledger Fabric onboarding.

The lab will develop a working toolchain that:

validates local development environments
detects common onboarding failures during setup
classifies errors into structured categories
provides actionable remediation steps
outputs machine-readable JSON for automation and analysis

Initial implementation will include:

a zero-dependency preflight script for baseline system readiness checks
a core failure classification schema
environment and configuration validation checks in the CLI
a reference adapter for Hyperledger Fabric onboarding

The lab is designed as a reusable onboarding reliability layer that can be extended across multiple decentralized systems.

This work aligns with LF Decentralized Trust’s focus on developer experience, operational resilience, and sustainability by reducing early-stage contributor drop-off caused by opaque or inconsistent onboarding failures.

The lab aims to define a reusable failure classification model for onboarding across decentralized systems.

The structured JSON output enables integration with CI pipelines, automated diagnostics, and support tooling.

AI-assisted workflows (optional)

The structured output format is designed to be compatible with AI-assisted workflows, enabling:

transformation of diagnostic results into human-readable explanations
automated generation of remediation steps
integration with AI-based developer support tools

This is not a dependency of the system, but an optional extension enabled by the deterministic output format.

Initial Committers

https://github.com/Mateja3m

TBD (seeking alignment within LF Decentralized Trust community)

Pre-existing repository

https://github.com/Mateja3m/idoa