What specifically does the health check audit?

It audits the .aiwg directory for stale working files, ensures documentation matches the current project phase, and verifies that code changes align with your documented architecture.

When is the best time to run a workspace health check?

It is most effective during phase transitions, after completing major features, or when documentation appears to be out of sync with the actual code.

Does this skill automatically delete files?

No, the skill generates a detailed health report with recommendations. It suggests specific commands like /workspace-prune-working, which the user must confirm before execution.

How does this help with agentic coding?

By maintaining a clean 'context stack,' it reduces the risk of AI agents processing outdated or conflicting information, leading to more accurate code generation and fewer errors.

Workspace Health & Alignment

Name: Workspace Health & Alignment
Author: jmagly

byjmagly

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Productivity & Workflow

Monitors and maintains workspace integrity by assessing file structure, documentation alignment, and artifact freshness throughout the development lifecycle.

About

The Workspace Health Check skill ensures that autonomous coding environments remain organized and synchronized by auditing the .aiwg directory structure. It proactively identifies stale files, documentation drift, and missing traceability links, providing actionable recommendations to realign the project at key transition points. By maintaining a clean and well-documented source of truth, it prevents the accumulation of technical debt and context pollution often found in long-running AI coding sessions.

Key Features

Stale artifact detection to prevent documentation drift
Phase transition validation for SDLC stage management
Traceability auditing to ensure requirements are linked to artifacts
Real-time alignment checks between documentation and code state
Automated directory cleanup suggestions for stale working files
67 GitHub stars

Use Cases

Validating project state before transitioning from design to implementation phases
Cleaning up temporary drafts and stale lock files after intensive coding sessions
Detecting architecture drift where code has diverged from documented designs

About

Key Features

Stale artifact detection to prevent documentation drift
Phase transition validation for SDLC stage management
Traceability auditing to ensure requirements are linked to artifacts
Real-time alignment checks between documentation and code state
Automated directory cleanup suggestions for stale working files
67 GitHub stars

Use Cases

Validating project state before transitioning from design to implementation phases
Cleaning up temporary drafts and stale lock files after intensive coding sessions
Detecting architecture drift where code has diverged from documented designs