Destructive Reader LLM

A CLI literary critique tool inspired by r/DestructiveReaders — the Reddit community known for "brutal but loving" manuscript feedback.

Instead of spending days earning karma for inconsistent human critiques, get structured, actionable feedback on your fiction chapters in seconds.

What It Does

Feed it a chapter. Get a critique that doesn't pull punches but always offers solutions.

The critique follows a consistent structure:

• Opening Hook — what works, the biggest problem, overall take
• The Big Issues (2-3 max) — quoted from your text, explained, with concrete fixes
• Reader Journey — where the critic was hooked, lost, confused, or kept reading
• Quick Fixes — ranked actionable changes with before/after examples
• What's Working — genuine positives with quoted evidence

Requirements

• Python 3.12+
• Ollama account (free tier works)
• Ollama Python library: pip install ollama

Setup

1. Clone the repo:

   git clone git@github.com:aweussom/DestructiveReader-LLM.git
   cd DestructiveReader-LLM

2. Install the Ollama Python library:

   pip install ollama

3. Install Ollama and sign in:

   ollama login

4. Pull the model:

   ollama pull nemotron-3-nano:30b-cloud

Usage

python destructive-reader-llm.py path/to/your-chapter.md

The tool will:

1. Load the critique instructions from INSTRUCTIONS.md
2. Read your chapter
3. Send both to the LLM
4. Print the full critique to console
5. Save as <chapter-name>-critique-<timestamp>.md alongside your chapter file

Example

python destructive-reader-llm.py examples/01-AWAKENING.md

Sample output:

Loading chapter: examples/01-AWAKENING.md
Loading instructions from: INSTRUCTIONS.md
Sending chapter to nemotron-3-nano:30b-cloud for critique...
------------------------------------------------------------

[full critique output]

------------------------------------------------------------
✓ Critique completed in 15.4 seconds
✓ Saved to: examples/01-AWAKENING-critique-20260214_164857.md

Customisation

The critique style is defined entirely in INSTRUCTIONS.md. Edit it to change the voice, structure, or focus areas. The Python script is model-agnostic — change the model name in get_critique() to use any Ollama-supported model.

License

MIT