author

An interactive tool for creating a mathematical paper section by section, including prose, equations, and checked Python code.

In plain words
What is it for?
Use it to develop a new math paper or continue one using findings from an earlier review file.
Why use it?
It helps turn informal ideas into a structured paper while validating each section before continuing.

Command

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add commands/reggiechan74/cc-plugins/author
Clone the repo
git clone --depth 1 https://github.com/reggiechan74/cc-plugins
Per session 18 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 5,045 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00018 $0.05045
Opus 5 $0.00009 $0.02523
Sonnet 5 $0.00004 $0.01009
Haiku 4.5 $0.00002 $0.00505

Measured 2d ago against content hash da0fb0c21b3f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

author scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

math-paper-creator/commands/author.md · 420 lines

How it starts

The opening of the file, as written. The whole thing — 420 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Author a Math Paper

Create a new .model.md document through interactive ideation. You describe concepts — Claude formalizes them into complete paper sections with prose, display math, and validated Python blocks. Each section is written and validated before moving on.

Step 1: Initialize

New paper (no path given, or file doesn't exist):

  1. Check for review findings. If the user provides a .review.md file as the path argument, or if a <basename>.review.md file exists adjacent to the target .model.md path, read the review findings. These contain a recommended template, outline, and key decisions from a prior /math-paper-creator:review session. Tell the user: "I found review findings from a prior session. I'll use these to guide template selection and authoring." Skip items 1-3 below and proceed directly to Step 1.5 with the review context loaded. If no review file is found, continue with item 1.
  2. Ask the user to describe the problem domain. Accept anything from a vague concept ("I want to model workforce optimization under constraints") to a specific sketch ("I have sets I, J, P and an allocation variable x_ijp").
  3. Create the .model.md file with YAML frontmatter:
    ---
    title: <derived from user description>
    date: <today>
    author: <ask or infer>
    version: 0.1
    ---
    
  4. Initialize an empty running symbol name registry (a list you maintain across sections).
  5. Proceed to Step 2.

Resume (path to an existing .model.md file):

  1. Read the file.
  2. Run the meta-compiler check to rebuild the symbol state:
    cd ${CLAUDE_PLUGIN_ROOT} && PYTHONPATH=src python3 -m meta_compiler.cli check "<file_path>"
    
  3. Rebuild the running symbol name registry from the existing validate blocks (read all symbol names already registered).
  4. Show the user a summary: number of sections, symbol count by type, any warnings.
  5. Ask where they want to continue. Proceed to the authoring loop (Step 3).

Step 1.5: Template selection

Read the full file on GitHub · 420 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 420 lines · 18 tokens per session scan A da0fb0c21b3f

Subscribe to this mod's changes

author is a command published in the GitHub repository reggiechan74/cc-plugins (6 stars, last pushed 3mo ago), licensed MIT. It adds 18 tokens to every session and 5,045 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.