How does this improve AI agent performance?

By keeping the workspace clean and documentation updated, it ensures that autonomous agents have a smaller, more accurate context window, leading to fewer hallucinations and errors.

When should I run a Workspace Health Check?

It is best invoked during SDLC phase transitions, after major feature completions, or whenever your documentation feels out of sync with your source code.

What is 'architecture drift' in this context?

Architecture drift occurs when your source code implementation diverges from your documented Software Architecture Document (SAD); this skill flags these discrepancies for manual review.

Does this skill automatically delete my files?

No, the skill identifies issues and suggests cleanup commands like /workspace-prune-working, but it requires user confirmation before any destructive actions are taken.

Workspace Health Check

Name: Workspace Health Check
Author: jmagly

byjmagly

•

Productivity & Workflow

Monitors and optimizes the integrity of agentic coding environments by identifying stale artifacts and documentation drift.

About

The Workspace Health Check skill ensures your AI-driven development environment remains organized and aligned with your project's current state. It automatically audits the .aiwg directory for stale files, orphaned drafts, and outdated documentation, providing actionable recommendations to realign the workspace after phase transitions or major feature additions. By maintaining high workspace hygiene, it reduces context noise and prevents technical debt in autonomous coding workflows, ensuring Claude always operates on the most relevant project data.

Key Features

67 GitHub stars
Requirement traceability and artifact coverage verification
Documentation alignment checks for SDLC phase transitions
Actionable health reports with suggested cleanup commands
Automated working directory audits for stale or orphan files
Proactive architecture drift detection and risk register monitoring

Use Cases

Realigning the workspace after transitioning from planning to construction phases
Cleaning up temporary draft artifacts and lock files after intensive coding sprints
Identifying architecture drift where implementation has diverged from documented design

About

Key Features

67 GitHub stars
Requirement traceability and artifact coverage verification
Documentation alignment checks for SDLC phase transitions
Actionable health reports with suggested cleanup commands
Automated working directory audits for stale or orphan files
Proactive architecture drift detection and risk register monitoring

Use Cases

Realigning the workspace after transitioning from planning to construction phases
Cleaning up temporary draft artifacts and lock files after intensive coding sprints
Identifying architecture drift where implementation has diverged from documented design