MathModeling-skills: Skill for Claude Code

.claude/skills/problem-classifier/SKILL.md

problem-classifier is a skill for Claude Code from zhnnky329/MathModeling-skills. It costs 39 tokens per session (521 once invoked), scanned A, original, MIT.

A workflow for labeling each part of a mathematical-modeling problem by the kind of answer it needs, such as prediction, optimization, simulation, or classification.

In plain words
What is it for?
Use it to classify parsed subquestions, record evidence and confidence, and describe how the classification affects validation and deliverables. It does not choose the algorithm.
Why use it?
It prevents the team from choosing a method based only on keywords or silently resolving an ambiguous problem statement. Important framing choices are shown and recorded for human review.

Skill for Claude Code

Written for Claude Code: installed under .claude/.

This is zhnnky329/MathModeling-skills's own configuration. It tells Claude Code how to work on MathModeling-skills itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything MathModeling-skills configures →

About the project

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.

zhnnky329/MathModeling-skills · 1,080 stars · on GitHub

Reuse

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.

Copy the file
curl -O https://raw.githubusercontent.com/zhnnky329/MathModeling-skills/main/.claude/skills/problem-classifier/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills

Made for: Claude Code.

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README.md
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<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/problem-classifier"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/problem-classifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 39 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 521 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00039 $0.00521
Opus 5.5 $0.00016 $0.00208
Sonnet 5.5 $0.00008 $0.00104
Haiku 4.5 $0.00004 $0.00052

Measured 2mo ago against content hash 6de755f37026, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

problem-classifier 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.

.claude/skills/problem-classifier/SKILL.md · 80 lines

How it starts

The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Preconditions

  • planning/parse/problem_parse.json exists and maps every Qx to an output.
  • Material framing ambiguities are visible.

Read legacy parse paths only during migration.

Task Types

  • evaluation/ranking;
  • prediction/estimation;
  • optimization/decision;
  • mechanism/dynamics;
  • classification/clustering;
  • graph/routing/network;
  • simulation/scenario;
  • descriptive/inference;
  • mixed.

Detailed cues are in references/task-type-guide.md.

Workflow

  1. Classify from the required output, decision structure, constraints, and relationships—not keywords alone.
  2. Assign:
    • primary type;
    • optional secondary type;
    • confidence;
    • evidence from the parse;
    • consequences for validation and deliverables.
  3. Identify mixed or ambiguous framings that would change what the team can claim.
  4. For a load-bearing ambiguity, invoke one choice card explaining consequences. Do not silently settle it.
  5. Save planning/classification/problem_classification.json.
  6. Record the human framing decision in methods/Qx/qx_decisions.jsonl, or in planning/framing_decisions.jsonl when the Qx method directory does not yet exist.

Output Contract

{
  "schema_version": 1,
  "subquestions": [
    {
      "id": "Q1",
      "primary_type": "evaluation",
      "secondary_type": null,
      "confidence": "high",
      "evidence": [],
      "required_validation": [],
      "framing_decision_id": null,
      "risks": []
    }
  ]
}

Rules

  • Do not propose or choose methods.
  • Do not classify only from nouns such as “forecast” or “optimal”; verify the required output.
  • A subquestion may be mixed, but avoid listing many types without prioritization.
  • Human framing is required when alternative classifications lead to materially different outputs or claims.
  • Do not create a long taxonomy report when the JSON record is sufficient.

Verification

  • Every Qx has one primary type.
  • Mixed/secondary types are justified.
  • Classification evidence resolves to the parse.
  • Ambiguous framing is human-confirmed or remains a blocker.
  • No algorithm selection leaked into classification.

Read the full file on GitHub · 80 lines

Files

What ships with it

1 file 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.

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. 2mo ago First seen · 80 lines · 39 tokens per session scan A 6de755f37026

Subscribe to this mod's changes

problem-classifier is a skill published in the GitHub repository zhnnky329/MathModeling-skills (1,080 stars, last pushed 1mo ago), licensed MIT. It adds 39 tokens to every session and 521 once invoked, about $0.0002 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

problem-classifier compared with skills from other repositories

problem-classifier next to 6 skills from other repositories
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