Skip to main content

Paper Fluff Cutter

A CLI tool that cuts through academic paper fluff to extract what actually matters.

Most research has close to zero value. This tool uses multimodal LLMs to analyze papers and answer three questions:

  1. Why should I care? - What problem does this address and why does it matter?
  2. What's the actual innovation? - What's the core idea in plain terms?
  3. Is the evidence convincing? - Do the experiments actually support the claims?

Acknowledgement: the design of the fluff-cutter wiki is inspired by Andrej Karpathy's gist on LLM Wiki: https://gist.github.com/karpathy/442a6bf555914893e9891c11519de94f

Quick Start

pip install fluff-cutter
fluff-cutter init          # set up API keys and defaults
fluff-cutter analyze paper.pdf

Requires Python 3.10+.

Usage

# Local file
fluff-cutter analyze paper.pdf

# URL (arxiv /abs/ and GitHub /blob/ links are normalized automatically)
fluff-cutter analyze https://arxiv.org/pdf/2411.19870
fluff-cutter analyze https://github.com/MoonshotAI/Kimi-K3/blob/main/k3_tech_report.pdf

# Options
fluff-cutter analyze paper.pdf --output analysis.md   # custom output path
fluff-cutter analyze paper.pdf --print                 # stdout only, no file
fluff-cutter analyze paper.pdf --provider openai       # openai, anthropic, openrouter
fluff-cutter analyze paper.pdf --model gpt-5.2         # override default model
fluff-cutter analyze paper.pdf --max-pages 30          # limit pages for long papers

Wiki workflow

For persistent research tracking, you can keep a markdown wiki alongside the raw PDFs:

# Create a wiki project
fluff-cutter wiki init ./research-wiki

# `wiki init` saves this as your default wiki root, so later commands work
# from anywhere unless you explicitly override with --root
fluff-cutter wiki add https://arxiv.org/pdf/2411.19870

# Inspect the wiki
fluff-cutter wiki ls
fluff-cutter wiki show agents-for-useful-things
fluff-cutter wiki pdf agents-for-useful-things
fluff-cutter wiki query "agents planning"

# Maintenance
fluff-cutter wiki rebuild --root ./research-wiki
fluff-cutter wiki doctor --root ./research-wiki
fluff-cutter wiki remove paper-slug --root ./research-wiki --delete-pdf

The wiki layout looks like this:

research-wiki/
├── fluff-cutter.yaml
├── raw/
│   └── pdfs/
└── wiki/
    ├── papers/
    ├── index.md
    ├── overview.md
    └── log.md

By default, results are printed to the terminal and saved next to the input PDF with a recognizable title, source identifier, and extraction date. Modern arXiv notes retain their numeric paper ID, for example scaling-does-it-work-2411.19870-2026-07-30.md. Model output streams live during analysis (provider-side streaming), so you see tokens immediately instead of waiting for full completion.

Obsidian-ready notes

Saved analyses and wiki paper pages include YAML properties that Obsidian can index:

---
title: Scaling Test-Time Compute
source: https://arxiv.org/abs/2411.19870
created: 2026-07-30
content_type: research-paper
authors:
  - Example Author
published_year: 2025
research_type: empirical
concepts:
  - verifier-guided search
prerequisites:
  - transformer architecture
tags:
  - paper
  - summary
  - language-models
  - reasoning
model: OpenAI (gpt-5.2)
---

The visible note uses one paper-title H1 followed by consistent H2 analysis sections. Model-generated metadata is validated and removed from the note body, so files can be dropped directly into an Obsidian vault and organized with Properties, tags, and search.

Supported Providers

Provider Default Model Env Variable
OpenAI gpt-5.2 OPENAI_API_KEY
Anthropic claude-sonnet-4-5 ANTHROPIC_API_KEY
OpenRouter anthropic/claude-sonnet-4-5 OPENROUTER_API_KEY

All providers support native PDF input -- no external dependencies like poppler needed.

Configuration

Run fluff-cutter init for interactive setup. It asks which provider to configure, then prompts only for that provider's API key and model. Run it again to configure another provider; existing provider settings are preserved.

You can also set environment variables directly:

export OPENAI_API_KEY=sk-your-key-here
export FLUFF_CUTTER_PROVIDER=anthropic          # default provider
export FLUFF_CUTTER_ANTHROPIC_MODEL=claude-sonnet-4-5  # override model

Config is read in this order (highest priority first): CLI flags, env variables, ~/.fluff-cutter/config.yaml, provider defaults.

License

MIT

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

fluff_cutter-0.7.0.tar.gz (40.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

fluff_cutter-0.7.0-py3-none-any.whl (28.5 kB view details)

Uploaded Python 3

File details

Details for the file fluff_cutter-0.7.0.tar.gz.

File metadata

  • Download URL: fluff_cutter-0.7.0.tar.gz
  • Upload date:
  • Size: 40.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for fluff_cutter-0.7.0.tar.gz
Algorithm Hash digest
SHA256 03ff443380bb9963b19d51f0d3a8ee4d5b0747c2c07b4c41afa7ca0f594e4763
MD5 b0b404af59659e00d80c851a4125350f
BLAKE2b-256 168cf1276d9afb0d9f4e78e9359d84d86a320b9072c5a3056a6dd12a6cd130a4

See more details on using hashes here.

Provenance

The following attestation bundles were made for fluff_cutter-0.7.0.tar.gz:

Publisher: publish.yml on weijianzhg/paper-fluff-cutter

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file fluff_cutter-0.7.0-py3-none-any.whl.

File metadata

  • Download URL: fluff_cutter-0.7.0-py3-none-any.whl
  • Upload date:
  • Size: 28.5 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for fluff_cutter-0.7.0-py3-none-any.whl
Algorithm Hash digest
SHA256 7a6b70e7511610a8967bca0013c31a39bcd3707e93aa7aeb193e5dbced8efe94
MD5 305b010bd7e29d4e9975e0510cf7fcc1
BLAKE2b-256 7c6686eec7caaa7c9e2d0a0b0c5738fff1db29a64bfbc29bc35a61101212cfb1

See more details on using hashes here.

Provenance

The following attestation bundles were made for fluff_cutter-0.7.0-py3-none-any.whl:

Publisher: publish.yml on weijianzhg/paper-fluff-cutter

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

This release

0.7.0 This release

2 files

0.6.0

2 files

0.5.2

2 files

0.5.1

2 files

0.5.0

2 files

0.4.2

2 files

0.4.1

2 files

0.4.0

2 files

0.3.0

2 files

0.2.0

2 files

0.1.0

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page