qiaomu-content-interpreter

Automate long-text interpretation with two-pass refinement and visual generation.

Updated Apr 8, 2026

Search Tags:#obsidian #workflow-automation #illustrations #ai-writing #style-transfer #content-processing #draft-management

Ready to run

npx skills add https://github.com/joeseesun/qiaomu-content-interpreter --skill qiaomu-content-interpreter

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: qiaomu-content-interpreter
Source: https://github.com/joeseesun/qiaomu-content-interpreter/tree/main
Command: npx skills add https://github.com/joeseesun/qiaomu-content-interpreter --skill qiaomu-content-interpreter

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires requests, pillow, and includes scripts (resource) components.

What problem does it solve?

This skill automates end-to-end long-text interpretation and two-pass refinement of content with automatic visual generation.

Core Features & Use Cases

True two-pass refinement: Pass 1 rewrites with the Qiaomu style, Pass 2 compares with the original to fill in missing content.
Automatic illustration generation: Visuals are produced and aligned with major sections, ready for publishing.
Fully automated workflow: From workspace setup to final Markdown output, it runs without user prompts and preserves original content.

Quick Start

Paste your long text to start the two-pass interpretation workflow and automatically generate visuals.

Frequently Asked Questions about qiaomu-content-interpreter

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate long-text interpretation and illustration generation for Obsidian?▼

Automate long-text interpretation by pasting your content to trigger a fully automated workflow. It generates a stylized narrative and accompanying visuals, outputting a ready-to-publish Markdown file.

What is two-pass content refinement and how does it work for long-form articles?▼

Two-pass content refinement first rewrites text into a specific style, then compares it against the original to fill in missing content. This ensures the final narrative remains readable without losing core information.

Do I need Python to run the automated content processing and visual generation workflow?▼

Yes, you need Python 3.8 or higher to run the internal scripts. The workflow relies on the requests and pillow dependencies to prepare the workspace and generate aligned illustrations.

Can I automatically generate illustrations that align with major sections of my research notes?▼

Yes, automatic illustration generation produces visuals aligned with major sections of your research notes. The workflow processes the text and embeds the generated images directly into the final Markdown output.

Does this automated workflow support draft management without manual prompts?▼

Yes, the workflow runs fully automated from workspace setup to final output without user prompts. It handles draft generation internally, preserving the original content while applying the two-pass interpretation.

What's the best way to transfer writing style to long-form essays while preserving core content?▼

The best way to transfer style while preserving content is using a two-pass interpretation process. Pass one applies the style transfer, and pass two compares the draft with the original to recover any missing core content.