evomath-tao

evomath-tao is a skill for Claude Code, Codex from CamusGIT/EvoQuant. It costs 203 tokens per session (3,527 once invoked), scanned A, a copy of evomath-tao, Apache-2.0.

A rigorous workflow for checking difficult mathematical claims, including contest problems, machine-learning theory, and research conjectures.

In plain words
What is it for?
It is for testing small cases, trying standard proof methods, auditing proof drafts, finding counterexamples, and clearly documenting what has or has not been established.
Why use it?
It helps separate proven results from guesses, numerical checks, counterexamples, and unresolved gaps.

Skill for Claude CodeCodex

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 skills/camusgit/evoquant/evomath-tao
Any agent
npx skills add CamusGIT/EvoQuant --skill evomath-tao
Clone the repo
git clone --depth 1 https://github.com/CamusGIT/EvoQuant

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for evomath-tao

README.md
[![agentmods](https://agentmods.dev/badge/skills/camusgit/evoquant/evomath-tao.svg)](https://agentmods.dev/skills/camusgit/evoquant/evomath-tao)
Your own site
<a href="https://agentmods.dev/skills/camusgit/evoquant/evomath-tao"><img src="https://agentmods.dev/badge/skills/camusgit/evoquant/evomath-tao.svg" alt="Measured on agentmods" height="20"></a>
Per session 203 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,527 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 97% copy Near-identical to another mod 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 $0.00203 $0.03527
Opus 5 $0.00102 $0.01764
Sonnet 5 $0.00041 $0.00705
Haiku 4.5 $0.00020 $0.00353

Measured 5d ago against content hash 4dc325661e25, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

evomath-tao 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 5d ago.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/evomath_workspace.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

Origin

This is a copy

97% identical to evomath-tao — 10 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

EvoQuant/skills/evomath-tao/SKILL.md · 337 lines

How it starts

The opening of the file, as written. The whole thing — 337 lines — stays where its author put it; the contents beside it link to each section on GitHub.

EvoMath (Tao-style)

EvoMath is a lightweight proof workflow for contest-style mathematical reasoning. Its job is to produce a rigorous proof, a verified counterexample, a useful partial result, or a clear handoff. Keep the process small; do not run a heavy audit pipeline by default.

Methodology Anchor — Terence Tao's Research-Math Practice

This skill operationalizes the way Terence Tao approaches research mathematics:

  1. Compute small cases first (Kepler before Newton) — build intuition from data before reaching for theory.
  2. Try the standard toolbox broadly before going deep — most hard problems crack to a standard technique; the few that don't only reveal which after several have failed.
  3. Hold rigor and intuition together (post-rigorous mathematics) — trust intuition, but verify every step. "It feels right" is a hypothesis, not a proof.
  4. Atomize when stuck — decompose into independently checkable sub-claims. A clean map of proved / conjectured / open beats a polished but shaky narrative.
  5. Stay honest about what isn't proved — distinguish PROVED / VERIFIED_NUMERICALLY / CONJECTURED / HANDED_OFF. When blocked, name the precise gap.
  6. Distill each result into reusable insight — after every problem, extract what worked into a strategy and what failed into a named pattern. Mathematical maturity is accumulated meta-insight.

Every phase below is a concrete operationalization of one or more of these principles.

Operating Rules

  • Use Markdown notes for handoff between steps. Do not require JSON/YAML unless a script explicitly asks for it.
  • Keep only compact state: plan, verified claims, failed attempts, final audit. Do not pass long failed derivations into later prompts.
  • Prefer a few independent proof attempts over one long derivation.
  • Numerical verification is NOT a proof step (math-olympiad rule). Checking a claim on n=1..100 and finding no counterexample does NOT make it PROVED; the strongest label such evidence can earn is VERIFIED_NUMERICALLY.
  • Exact arithmetic can refute; approximate numerics only suggest.
  • A proof is final only after an adversarial check of the clean proof.
  • Calibrated abstention over bluffing: when verification fails repeatedly, admit it. Return partial results and mark unfixed gaps explicitly (math- olympiad rule). Final status HANDED_OFF with a structured wall report is always preferable to PROVED with hand-waved gaps.
  • Every final answer must include a visible final-status: ... line.
  • Use TodoWrite to drive the workflow. Each step is one todo; you cannot mark a todo completed unless the corresponding .md file passes its validator.

Read the full file on GitHub · 337 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. 5d ago First seen · 337 lines · 203 tokens per session scan A 4dc325661e25

Subscribe to this mod's changes

evomath-tao is a skill published in the GitHub repository CamusGIT/EvoQuant (212 stars, last pushed 2d ago), licensed Apache-2.0. It adds 203 tokens to every session and 3,527 once invoked, about $0.0010 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to evomath-tao, differing in 10 lines, and is treated as a copy.

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