What dependencies are needed for this skill?

The FABER Core skill requires Bash, jq for JSON processing, and a Python environment with the TOML library for configuration parsing.

How does the Core skill track progress?

It uses a standardized JSON state file to record the status and metadata associated with each phase, allowing agents to resume work or report progress accurately.

Can I customize the status cards posted to my tracking system?

Yes, the skill supports markdown templates for status cards, allowing you to include custom variables like work IDs, timestamps, and context references for better team visibility.

Where is the configuration file stored?

By default, the skill looks for a .faber.config.toml file in the project root or .fractary/plugins/faber/config.json for version 2.0 setups.

What is the FABER framework?

FABER is a structured development framework comprising five phases: Frame, Architect, Build, Evaluate, and Release, designed to guide AI agents through a complete software development lifecycle.

FABER Core Workflow Engine

Name: FABER Core Workflow Engine
Author: fractary

byfractary

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Productivity & Workflow

Manages core lifecycle states, configuration, and status reporting for the FABER automated development workflow.

About

The FABER Core skill provides the foundational infrastructure for the FABER development framework, enabling Claude to manage complex workflows across multiple phases including framing, architecture, and deployment. It handles essential tasks such as parsing configuration files, tracking state transitions between development phases, and generating interactive status cards for external work-tracking systems. By centralizing state management and template substitution, this skill ensures consistency and observability throughout the entire software development lifecycle (SDLC) managed by AI agents.

Key Features

Dynamic status card generation and posting to external project management tools
2 GitHub stars
Configuration loading and parsing from TOML files for project-specific customization
Automated state management for development phases including frame, architect, build, evaluate, and release
Pattern-based string substitution for consistent environment and branch naming
Standardized error handling and exit codes optimized for AI agent consumption

Use Cases

Managing transitions between planning, coding, and testing phases in an automated pipeline
Synchronizing AI agent progress with external project management systems like Jira or Linear
Generating consistent branch names and deployment metadata based on project templates

About

Key Features

Dynamic status card generation and posting to external project management tools
2 GitHub stars
Configuration loading and parsing from TOML files for project-specific customization
Automated state management for development phases including frame, architect, build, evaluate, and release
Pattern-based string substitution for consistent environment and branch naming
Standardized error handling and exit codes optimized for AI agent consumption

Use Cases

Managing transitions between planning, coding, and testing phases in an automated pipeline
Synchronizing AI agent progress with external project management systems like Jira or Linear
Generating consistent branch names and deployment metadata based on project templates