Why does the skill avoid certain words like 'robust' or 'seamless'?

To ensure documentation is objective and technical, the skill is programmed to remove marketing jargon and 'AI-isms' in favor of specific metrics and honest descriptions.

Does it require manual input for the tech stack?

No, the skill automatically detects the tech stack by reading package manifests and browsing the source tree structure.

How does the skill handle private repositories?

The skill identifies git visibility during the scan and automatically sets the 'private' flag to true in the metadata to prevent sensitive projects from being exposed in public manifests.

What is the purpose of the .atlas directory?

The .atlas directory acts as a standardized container for project metadata (project.yaml) and MDX-based documentation, allowing for consistent indexing and portfolio generation.

What is the difference between flagship and lab tiers?

Flagship projects are complex systems with meaningful architecture and multiple components, while lab projects are experiments, utilities, or learning projects.

Atlas Repository Documenter

Name: Atlas Repository Documenter
Author: CortaLabs

byCortaLabs

0•

콘텐츠 관리

Automates the creation of standardized project metadata and technical documentation for repository portfolios using batched AI agents.

The Atlas Cards skill streamlines the process of cataloging software repositories by generating structured .atlas/ directories. Using a swarm of concurrent Claude Haiku agents, it analyzes source code, manifest files, and READMEs to produce accurate project.yaml files and MDX documentation. It enforces a strict 'no-fluff' writing style to ensure documentation remains technical, honest, and metric-driven, making it an essential tool for developers building automated portfolios or documentation hubs for large codebases.

주요 기능

01Concurrent batch processing of up to 10 repositories simultaneously using Haiku agents

02Enforced technical writing standards that eliminate corporate jargon and AI-isms

03Deep source analysis of manifests like package.json, pyproject.toml, and Cargo.toml

04Automated project tiering into 'flagship' or 'lab' categories based on code depth

05Automatic generation of MDX documentation pages and technical architecture highlights

060 GitHub stars

사용 사례

01Preparing repositories for automated portfolio generation or internal developer portals

02Auditing codebases to extract technical metrics and key architecture decisions automatically

03Standardizing documentation across a large portfolio of personal or company projects

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add cortalabs/atlas atlas-cards

For use in Claude.ai and ChatGPT