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 topprismdata/cultivating-ml-agent --skill sc-tir-mathematical-reasoninggit clone --depth 1 https://github.com/topprismdata/cultivating-ml-agentWrote 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/topprismdata/cultivating-ml-agent/sc-tir-mathematical-reasoning)<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.
<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>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 | $0.00125 | $0.03094 |
| Opus 5 | $0.00063 | $0.01547 |
| Sonnet 5 | $0.00025 | $0.00619 |
| Haiku 4.5 | $0.00013 | $0.00309 |
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( 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:
- Calculation errors: Text-only reasoning often makes arithmetic mistakes
- Lack of precision: Cannot reliably compute complex expressions
- Single-path failure: One reasoning trace may hit a dead end
- 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:
- Self-Consistency: Sample multiple reasoning paths and vote
- 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
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.
- 12d ago First seen · 394 lines · 125 tokens per session scan A b8e4250e8c05
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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