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 sweetcornna/mathodology --skill mathodology-award-gatesgit clone --depth 1 https://github.com/sweetcornna/mathodologyWrote 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/sweetcornna/mathodology/mathodology-award-gates)<a href="https://agentmods.dev/skills/sweetcornna/mathodology/mathodology-award-gates"><img src="https://agentmods.dev/badge/skills/sweetcornna/mathodology/mathodology-award-gates/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/sweetcornna/mathodology/mathodology-award-gates"><img src="https://agentmods.dev/badge/skills/sweetcornna/mathodology/mathodology-award-gates.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 | $0.00025 | $0.00534 |
| Opus 5 | $0.00013 | $0.00267 |
| Sonnet 5 | $0.00005 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00053 |
Grade A, and why
mathodology-award-gates 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 55 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Mathodology Review Questions
The legacy skill name is retained for discovery. Review the substance of the work; do not estimate awards from invented numeric thresholds or require a particular handoff format. Scale review to the claims and the deadline.
Mathematical and empirical questions
- Does the solution answer every required question and represent its essential mechanisms?
- Are equations, units, constraints and limiting cases consistent? Is the solution feasible?
- Can the parameters be identified from the available observations?
- Does the described method match the code, including preprocessing and exclusions?
- Is a reported advantage measured against an appropriate baseline on comparable data?
- Are fitting, tuning and evaluation separated where the claim requires it?
- Are outcomes forced by normalization or constraints labeled as such?
- Could plausible changes to important assumptions reverse the recommendation?
- Are confidence intervals, predictive intervals and simulation variability distinguished?
- Can reported numbers be traced to data, code, a derivation or a stated assumption?
- Are evidence gaps, failed runs and material limitations disclosed?
Use relevant questions, not every possible test. When scenarios share comparable random inputs, paired simulations may improve precision; explain the coupling. Use Monte Carlo uncertainty for estimated probabilities, and disclose the number of simulations. Do not claim proof from a handful of successful runs.
Figures and paper
Load figure presets. Read figures at the size used in the final document. Check labels, uncertainty definitions, legends, color scales and captions against the underlying results. Inspect dense pages of the compiled PDF as well as individual exports. Image2 imagery must remain consistent with the mechanism; generated pixels are not quantitative evidence.
The optional PDF overview utility requires Poppler's pdftoppm and Matplotlib. It creates a page overview for visual review:
What ships with it
2 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.
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.
- 4d ago Changed · -213 lines · -15 tokens per session 3aebadb6a82c
- 12d ago First seen · 268 lines · 40 tokens per session scan A b18a097d6605
mathodology-award-gates is a skill published in the GitHub repository sweetcornna/mathodology (219 stars, last pushed 4d ago), licensed MIT. It adds 25 tokens to every session and 534 once invoked, about $0.0001 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.
Other skills, from other repositories
nutrient-document-processing
A guide for processing documents through the Nutrient DWS API, an online service for converting, reading, editing, and signing files. It supports PDFs, office documents, web pages, and images.
document-processing
Use when the deliverable is a document's bytes or its literal content — text/tables out of PDFs, AcroForm fill and flatten, page merge/split, PDF/DOCX from templates, OCR of image-only scans. NOT schema-typed fields pulled from text (that is structured-extraction), NOT signature routing (e-signature) or spreadsheet…
document-skills
Professional document creation, editing, and analysis for Office formats (docx, pdf, pptx, xlsx). Use when working with Word documents, PDFs, PowerPoint presentations, or Excel spreadsheets.
akf-trust-metadata-v2
AKF — The AI Native File Format workflow skill. Use this skill when the user needs The AI native file format. EXIF for AI — stamps every file with trust scores, source provenance, and compliance metadata. Embeds into 20+ formats (DOCX, PDF, images, code). EU AI Act, SOX, HIPAA auditing and the operator should preserve…
kb-import
Import knowledge from existing documents into structured KB entries. Reads source documents (Markdown, PDF, DOCX, plain text), extracts key information, and creates properly formatted KB entries with YAML frontmatter.
Read, create, edit, split, merge, rotate, watermark, fill, encrypt/decrypt, OCR, or extract text/tables/images from PDF files; use whenever .pdf is mentioned or requested.