evomath-tao

evomath-tao is a skill for Claude Code from EvoScientist/EvoSkills. It costs 203 tokens per session (3,514 once invoked), scanned A, original, Apache-2.0.

A structured method for checking difficult mathematical claims, such as competition problems, research statements, and proofs. It uses small examples, standard methods, and step-by-step verification to produce a proof, counterexample, partial result, or clearly marked gap.

In plain words
What is it for?
Use it to test a claim, audit a proof, explore examples, find counterexamples, or separate proven results from conjectures and unresolved parts.
Why use it?
It helps prevent plausible-looking but incomplete or incorrect mathematical reasoning.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: names the TodoWrite tool.

Good fit Use it to test a claim, audit a proof, explore examples, find counterexamples, or separate proven results from conjectures and unresolved parts.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/evoscientist/evoskills/evomath-tao
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.

Any agent
npx skills add EvoScientist/EvoSkills --skill evomath-tao
Clone the repo
git clone --depth 1 https://github.com/EvoScientist/EvoSkills

Made for: Claude Code.

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/evoscientist/evoskills/evomath-tao/github.svg)](https://agentmods.dev/skills/evoscientist/evoskills/evomath-tao)
Your own site
<a href="https://agentmods.dev/skills/evoscientist/evoskills/evomath-tao"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/evomath-tao/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.

agentmods 80×15 button for evomath-tao

Your own site · 80×15
<a href="https://agentmods.dev/skills/evoscientist/evoskills/evomath-tao"><img src="https://agentmods.dev/badge/skills/evoscientist/evoskills/evomath-tao.svg" alt="Reviewed on agentmods" width="80" 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 3,514 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00203 $0.03514
Opus 5 $0.00102 $0.01757
Sonnet 5 $0.00041 $0.00703
Haiku 4.5 $0.00020 $0.00351

Measured 10d ago against content hash 684f08118a81, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 10d 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

Copies of this mod

1 near-identical copy found in the catalogue:

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. 10d ago First seen · 337 lines · 203 tokens per session scan A 684f08118a81

Subscribe to this mod's changes

evomath-tao is a skill published in the GitHub repository EvoScientist/EvoSkills (436 stars, last pushed 9d ago), licensed Apache-2.0. It adds 203 tokens to every session and 3,514 once invoked, about $0.0010 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 2026-08-30.

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