MathModeling-skills is a collection of Claude Code and Codex skills for mathematical modeling competitions, guiding users through data analysis, method-specific checks, reproducible computations, figures, and paper audits without taking over modeling decisions. It is for contestants who want AI to handle mechanical checks while they retain responsibility for choosing methods and interpreting results. The catalogue entries form the project's skills, instructions, hook, setting, and plugin workflow.
Borrowing it
Nothing to install: this file belongs to zhnnky329/MathModeling-skills. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/related-paper-analyzer/SKILL.mdgit clone --depth 1 https://github.com/zhnnky329/MathModeling-skillsWrote 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/zhnnky329/mathmodeling-skills/related-paper-analyzer)<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/related-paper-analyzer"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/related-paper-analyzer/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/zhnnky329/mathmodeling-skills/related-paper-analyzer"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/related-paper-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 115 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00029 | $0.01489 |
| Opus 5.5 | $0.00012 | $0.00596 |
| Sonnet 5.5 | $0.00006 | $0.00298 |
| Haiku 4.5 | $0.00003 | $0.00149 |
Grade A, and why
related-paper-analyzer 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 2mo 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 — 207 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Analyze user-provided papers and reports before final method selection.
This skill checks whether the user has placed original paper files under workspace/papers/, reads those originals, and extracts transferable method cues, assumptions, variables, validation ideas, and limitations for the current subquestions.
This skill does not fabricate references, browse for new papers, write the final method card, or copy a published model blindly.
When to use
Use this skill:
- After
problem-parserandproblem-classifierhave produced validated artifacts. - Before
method-selector. - When the team wants to ground method selection in user-supplied literature rather than guessing from model names.
- When original papers, reports, or extracted paper text are available under
workspace/papers/.
Preconditions
The following should already exist or be provided:
- A validated problem parse.
- A validated problem classification artifact.
- At least one original paper file under
workspace/papers/.
If no paper originals are present under workspace/papers/, stop and remind the user to place them there before analysis begins.
Inputs
Use or request:
workspace/problem/problem-parser/problem_parse.json, if available.workspace/problem/problem-classifier/problem_classification.json, if available.- Original paper files under
workspace/papers/. - User notes about which papers matter most, if available.
Workflow
-
Check
workspace/papers/first.- Look for user-supplied paper originals or extracted paper text.
- Accept formats such as
.pdf,.docx,.md,.txt, or clearly named extracted text files. - Ignore
workspace/papers/related_paper_analysis.mdif it already exists.
-
Stop early if the paper folder is empty.
- If no paper originals are found, tell the user to place the paper originals under
workspace/papers/. - Do not fabricate literature summaries from memory.
- If no paper originals are found, tell the user to place the paper originals under
-
Inventory the available papers.
- Record file name, apparent title if recoverable, likely language, and likely relevance.
- Mark unreadable or weakly relevant files explicitly.
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.
- 2mo ago First seen · 207 lines · 29 tokens per session scan A 7cf4a52212eb
related-paper-analyzer is a skill published in the GitHub repository zhnnky329/MathModeling-skills (1,080 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 1,489 once invoked, about $0.0001 per session on Opus 5.5. 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.
related-paper-analyzer compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| related-paper-analyzerzhnnky329/MathModeling-skills | Skill | 1.1k | 1mo ago | MIT | A | 29 | Claude Code |
| instrument-data-to-allotropeanthropics/knowledge-work-plugins | Skill | 35k | 2d ago | Apache-2.0 | A | 123 | Claude Code |
| exploratory-data-analysisK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 83 | Claude Code |
| matlabK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 44 | Claude Code |
| phylogeneticsK-Dense-AI/scientific-agent-skills | Skill | 56k | 4d ago | MIT | A | 71 | Claude Code, Codex |
| research-engineerdavila7/claude-code-templates | Skill | 33k | yesterday | MIT | A | 43 | Claude Code, Codex |
| mapping-to-snomedmaziyarpanahi/openmed | Skill | 5.8k | yesterday | Apache-2.0 | A | 205 | Claude Code |