sc-tir-mathematical-reasoning

sc-tir-mathematical-reasoning is a skill for Claude Code, Codex from topprismdata/cultivating-ml-agent. It costs 125 tokens per session (3,094 once invoked), scanned A, original, MIT.

A method for solving maths problems by trying several reasoning paths, checking calculations with tools, and choosing a consistent answer. TDD here refers to mathematical competition datasets such as AIME and MATH.

In plain words
What is it for?
Use it to build mathematical reasoning systems, improve model performance on competition problems, and verify algebra, calculus, and combinatorics.
Why use it?
It reduces arithmetic mistakes, dead-end reasoning, and incorrect final answers from a single attempt.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to build mathematical reasoning systems, improve model performance on competition problems, and verify algebra, calculus, and combinatorics.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning
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 topprismdata/cultivating-ml-agent --skill sc-tir-mathematical-reasoning
Clone the repo
git clone --depth 1 https://github.com/topprismdata/cultivating-ml-agent

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 sc-tir-mathematical-reasoning

README.md
[![agentmods](https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning/github.svg)](https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning)
Your own site
<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning/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 sc-tir-mathematical-reasoning

Your own site · 80×15
<a href="https://agentmods.dev/skills/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning"><img src="https://agentmods.dev/badge/skills/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 125 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,094 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. A grade says what 26 rules found in the file — not that it is safe.
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 $0.00125 $0.03094
Opus 5 $0.00063 $0.01547
Sonnet 5 $0.00025 $0.00619
Haiku 4.5 $0.00013 $0.00309

Measured 12d ago against content hash b8e4250e8c05, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

sc-tir-mathematical-reasoning 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 12d 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.

result = subprocess.run(
skills/examples/sc-tir-mathematical-reasoning/SKILL.md · 394 lines

How it starts

The opening of the file, as written. The whole thing — 394 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SC-TIR: Self-Consistency with Tool-Integrated Reasoning

Problem

Large language models struggle with mathematical reasoning due to:

  1. Calculation errors: Text-only reasoning often makes arithmetic mistakes
  2. Lack of precision: Cannot reliably compute complex expressions
  3. Single-path failure: One reasoning trace may hit a dead end
  4. No verification: Cannot check intermediate results

Symptoms:

  • Model gives correct reasoning but wrong final answer
  • Long calculations contain arithmetic errors
  • Inconsistent answers across multiple runs
  • Competition scores plateau around 30-40%

Context / Trigger Conditions

Use SC-TIR when:

  • Working on mathematical reasoning tasks (AIMO, AIME, MATH benchmarks)
  • LLM outputs need precise calculation (algebra, calculus, combinatorics)
  • Single-pass reasoning is insufficient or unreliable
  • Need state-of-the-art performance on math competitions
  • Problem: "My model gets the reasoning right but the answer wrong"

Competition context:

  • AIMO (AI Mathematical Olympiad) - 0-99999 integer answers
  • MATH benchmark - High school competition problems
  • AIME - American Invitational Mathematics Examination

Solution

SC-TIR Algorithm Overview

SC-TIR combines two powerful techniques:

  1. Self-Consistency: Sample multiple reasoning paths and vote
  2. Tool-Integrated Reasoning: Model generates Python code for precise calculation

Core parameters:

  • N (width): Number of reasoning traces to generate (typical: 4-48)
  • M (depth): Number of iterations per trace (typical: 1-4)

Algorithm flow:

Input: Mathematical problem P
Output: Final answer A

1. Initialize N candidates with problem text
2. For each candidate i in 1..N:
   For iteration j in 1..M:
     a) Model generates Python code to solve current step
     b) Execute code, capture output
     c) Append output to context
     d) If no code generated → restart or prune
   End For
3. Extract all \boxed{answer} from candidates
4. Filter invalid answers (non-numeric, negative)
5. Apply modulo 1000 (if required by competition)
6. Majority vote: select most common answer
7. Return A

Read the full file on GitHub · 394 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. 12d ago First seen · 394 lines · 125 tokens per session scan A b8e4250e8c05

Subscribe to this mod's changes

sc-tir-mathematical-reasoning is a skill published in the GitHub repository topprismdata/cultivating-ml-agent (5 stars, last pushed 15d ago), licensed MIT. It adds 125 tokens to every session and 3,094 once invoked, about $0.0006 per session on Opus 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 2026-08-31.

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