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.
git clone --depth 1 https://github.com/CBirkbeck/mathlib-qualityWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/commands/cbirkbeck/mathlib-quality/setup-chatgpt)<a href="https://agentmods.dev/commands/cbirkbeck/mathlib-quality/setup-chatgpt"><img src="https://agentmods.dev/badge/commands/cbirkbeck/mathlib-quality/setup-chatgpt/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/commands/cbirkbeck/mathlib-quality/setup-chatgpt"><img src="https://agentmods.dev/badge/commands/cbirkbeck/mathlib-quality/setup-chatgpt.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.02649 |
| Opus 5.5 | $0.00006 | $0.01060 |
| Sonnet 5.5 | $0.00003 | $0.00530 |
| Haiku 4.5 | $0.00002 | $0.00265 |
Grade A, and why
setup-chatgpt scanned grade A with 1 finding 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 1mo 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
import { execFile } from "node:child_process"; How it starts
The opening of the file, as written. The whole thing — 309 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/setup-chatgpt - Set Up the ChatGPT MCP Server
Set up an MCP server that lets Claude Code query ChatGPT for mathematical second opinions during Lean 4 formalization work.
Usage
/setup-chatgpt
No arguments needed. This command will guide you through setup.
What This Does
The chatgpt-math MCP server provides a single tool:
ask_chatgpt_math- Ask ChatGPT a self-contained mathematics question. Useful for getting a second opinion on proof strategies, verifying mathematical claims, finding Mathlib API hints, or getting unstuck on formalization problems.
Parameters:
question(required) - The mathematical question. Must be self-contained with all definitions, context, and notation included.model(optional) - Model to use. Default:gpt-5.6-sol.reasoning_effort(optional) -minimal,low,medium,high,xhigh, ormax(default). Always use the highest the model supports; the ceiling is model-specific —gpt-5.4tops out atxhigh,gpt-5.6-solsupportsmax.
Prerequisites
-
ChatGPT desktop app (Codex CLI) - The server calls ChatGPT via the Codex CLI bundled with the ChatGPT desktop application.
- macOS: Install from https://chatgpt.com/download (or the Mac App Store).
- After install, the binary should be at:
/Applications/ChatGPT.app/Contents/Resources/codexor/Applications/Codex.app/Contents/Resources/codex. - You must be signed in with a ChatGPT Plus/Pro subscription.
-
Node.js (>= 18) - The MCP server runs on Node.js.
node --version # Should be >= 18
Setup Workflow
Step 1: Find the Codex Binary
Locate the Codex CLI binary. Check these paths in order:
ls /Applications/ChatGPT.app/Contents/Resources/codex 2>/dev/null
ls /Applications/Codex.app/Contents/Resources/codex 2>/dev/null
If neither exists, tell the user to install the ChatGPT desktop app first.
Store the path as CODEX_BIN.
Step 2: Create the MCP Server
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.
- 1mo ago First seen · 309 lines · 16 tokens per session scan A e2cd039af5c2
setup-chatgpt is a command published in the GitHub repository CBirkbeck/mathlib-quality (35 stars, last pushed 5d ago), licensed MIT. It adds 16 tokens to every session and 2,649 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
speckit.bug.fix
Apply the remediation from a bug assessment and record what was changed.