What is the benefit of atomic file writes in this skill?

Atomic writes ensure that a file is fully and successfully written to a temporary location before replacing the target file, preventing data loss or corruption if the process is interrupted.

Does this skill handle Markdown metadata?

Yes, it includes specialized functions to parse, generate, and update key-value metadata blocks within Markdown files, supporting complex structures via dot notation.

What is the ID-based access pattern?

It is a best practice where scripts reference resources by unique IDs rather than file paths, allowing the internal library to resolve the correct path and ensuring modularity.

Can I integrate these file operations into my own Python scripts?

Absolutely. The skill is structured as a reusable Python module (file_ops.py) that can be imported into any script within your AI coding workflow.

Where does this skill store its data?

By default, all workflow artifacts and configuration files are managed within a .plan/ directory at the root of your project.

Atomic File Operations for Plan Marshall

Name: Atomic File Operations for Plan Marshall
Author: cuioss

bycuioss

Productivity & Workflow

Provides a foundational library for atomic file operations and metadata management within AI-driven workflows.

About

This skill serves as the core infrastructure layer for the Plan Marshall orchestration system, enabling Claude Code to manage workflow artifacts with high reliability. It offers essential utilities for atomic file writing, automatic directory creation, and structured Markdown metadata parsing. By standardizing path management within the .plan directory and enforcing ID-based resource access, it ensures that AI-generated coding plans, lessons learned, and session states are consistent, preventing data corruption and reducing coupling between different workflow domains.

Key Features

Advanced Markdown metadata parsing and generation with dot-notation support
Standardized path construction for .plan workflow directories and subfolders
Built-in ID-based resource resolution to decouple cross-domain script dependencies
Atomic file writing using temporary files and rename patterns to prevent corruption
Standardized JSON success and error output helpers for CLI script integration
0 GitHub stars

Use Cases

Building custom orchestration scripts that require reliable state management
Managing knowledge capture and lessons learned files during complex coding tasks
Structuring multi-step AI task plans with consistent metadata and directory hierarchies

About

Key Features

Advanced Markdown metadata parsing and generation with dot-notation support
Standardized path construction for .plan workflow directories and subfolders
Built-in ID-based resource resolution to decouple cross-domain script dependencies
Atomic file writing using temporary files and rename patterns to prevent corruption
Standardized JSON success and error output helpers for CLI script integration
0 GitHub stars

Use Cases

Building custom orchestration scripts that require reliable state management
Managing knowledge capture and lessons learned files during complex coding tasks
Structuring multi-step AI task plans with consistent metadata and directory hierarchies