problem-parser

problem-parser is a skill for Claude Code, Codex from zhnnky329/MathModeling-skills. It costs 40 tokens per session (562 once invoked), scanned A, original, MIT.

A problem-analysis tool that converts a mathematical-modeling question into a structured description of its goals, data, limits, unknowns, outputs, and success criteria.

In plain words
What is it for?
Use it to record each subquestion, identify missing materials and dependencies, separate facts from assumptions, and save a shared problem contract.
Why use it?
It prevents method choices from starting with assumptions or favorite algorithms before the actual problem is understood.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/zhnnky329/mathmodeling-skills/problem-parser
Any agent
npx skills add zhnnky329/MathModeling-skills --skill problem-parser
Clone the repo
git clone --depth 1 https://github.com/zhnnky329/MathModeling-skills

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 problem-parser

README.md
[![agentmods](https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/problem-parser.svg)](https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/problem-parser)
Your own site
<a href="https://agentmods.dev/skills/zhnnky329/mathmodeling-skills/problem-parser"><img src="https://agentmods.dev/badge/skills/zhnnky329/mathmodeling-skills/problem-parser.svg" alt="Measured on agentmods" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 562 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00040 $0.00562
Opus 5 $0.00020 $0.00281
Sonnet 5 $0.00008 $0.00112
Haiku 4.5 $0.00004 $0.00056

Measured 5d ago against content hash 08b2ebc5e199, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

problem-parser 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 5d 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-parser/SKILL.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Purpose

Produce a model-neutral problem contract. Do not start from favorite algorithms or infer missing attachments.

Inputs

  • complete problem statement and attachments list;
  • contest rules and required deliverables;
  • user clarifications;
  • existing parse when revising.

Workflow

  1. Record source files and missing referenced material.
  2. Extract the global objective and each Qx verbatim enough to preserve intent.
  3. For each Qx identify:
    • goal;
    • objects/entities;
    • inputs and data;
    • decisions or unknowns;
    • hard and soft constraints;
    • required output and format;
    • evaluation/success criteria;
    • dependencies on other Qx;
    • uncertainty and ambiguity.
  4. Separate:
    • statement facts;
    • observations from supplied data;
    • proposed relationships;
    • assumptions requiring human judgment.
  5. If output form or success criteria are materially ambiguous, invoke one choice card. Do not choose the framing silently.
  6. Save:
    • planning/parse/problem_parse.json
    • an optional concise planning/parse/problem_parse.md only when a human-readable view is useful.
  7. Update the manifest status when present.

JSON Contract

{
  "schema_version": 1,
  "problem_source": [],
  "global_goal": "",
  "objects": [],
  "data_inventory": [],
  "global_constraints": [],
  "subquestions": [
    {
      "id": "Q1",
      "statement": "",
      "goal": "",
      "inputs": [],
      "unknowns_or_decisions": [],
      "constraints": [],
      "required_outputs": [],
      "success_criteria": [],
      "dependencies": [],
      "proposed_relationships": [],
      "ambiguities": []
    }
  ],
  "missing_material": [],
  "human_decisions_needed": []
}

Rules

  • Parse before classifying.
  • Do not name or recommend methods.
  • Do not fabricate data, fields, equations, causal relationships, or evaluation criteria.
  • Preserve units, time ranges, populations, and output formats.
  • A proposed relationship must be labeled as proposed until human-confirmed or evidence-supported.
  • Ask only about ambiguities that change the downstream problem.

Read the full file on GitHub · 88 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. 5d ago First seen · 88 lines · 40 tokens per session scan A 08b2ebc5e199

Subscribe to this mod's changes

problem-parser is a skill published in the GitHub repository zhnnky329/MathModeling-skills (716 stars, last pushed 11d ago), licensed MIT. It adds 40 tokens to every session and 562 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

instrument-data-to-allotrope

Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…

anthropics/knowledge-work-plugins · 123 tokens

exploratory-data-analysis

Perform bounded, local exploratory analysis of explicitly supported scientific files. Use for redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…

K-Dense-AI/scientific-agent-skills · 83 tokens

matlab

Build, review, migrate, and safely plan MATLAB or GNU Octave numerical workflows, including arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.

K-Dense-AI/scientific-agent-skills · 42 tokens

phylogenetics

Build and analyze phylogenetic trees using MAFFT (multiple alignment), IQ-TREE 2 (maximum likelihood), and FastTree (fast NJ/ML). Visualize with ETE3 or FigTree. For evolutionary analysis, microbial genomics, viral phylodynamics, protein family analysis, and molecular clock studies.

K-Dense-AI/scientific-agent-skills · 68 tokens

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

mapping-to-snomed

Maps clinical concept spans extracted by OpenMed to SNOMED CT concepts through a USER-SUPPLIED terminology server (the user's own Ontoserver, Snowstorm, or UMLS/UTS), never a bundled vocabulary. Use when the user wants to code findings, disorders, procedures, body structures, or substances to SNOMED CT, run an ECL…

maziyarpanahi/openmed · 205 tokens