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.
npx skills add EvoScientist/EvoSkills --skill evomath-taogit clone --depth 1 https://github.com/EvoScientist/EvoSkillsWrote 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/skills/evoscientist/evoskills/evomath-tao)<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.
<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>- NVIDIA SkillSpector pass
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 | $0.00203 | $0.03514 |
| Opus 5 | $0.00102 | $0.01757 |
| Sonnet 5 | $0.00041 | $0.00703 |
| Haiku 4.5 | $0.00020 | $0.00351 |
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.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- evomath-tao — 97% identical, 10 lines differ
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:
- Compute small cases first (Kepler before Newton) — build intuition from data before reaching for theory.
- 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.
- Hold rigor and intuition together (post-rigorous mathematics) — trust intuition, but verify every step. "It feels right" is a hypothesis, not a proof.
- Atomize when stuck — decompose into independently checkable sub-claims. A clean map of proved / conjectured / open beats a polished but shaky narrative.
- Stay honest about what isn't proved — distinguish PROVED / VERIFIED_NUMERICALLY / CONJECTURED / HANDED_OFF. When blocked, name the precise gap.
- 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
.mdfile passes its validator.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/angles-by-type.md 9.2 KB
- references/claim-memory.md 27 KB
- references/confidence-rules.md 6.0 KB
- references/grading-taxonomy.md 15 KB
- references/handoff-template.md 9.6 KB
- references/intake-checklist.md 8.0 KB
- references/model-tier.md 3.6 KB
- references/output-formats.md 9.6 KB
- references/output-schema.md 14 KB
- references/phase-4-audit.md 12 KB
- references/test-prompts.md 7.5 KB
- scripts/evomath_workspace.py 15 KB runs code
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.
- 10d ago First seen · 337 lines · 203 tokens per session scan A 684f08118a81
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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