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Skill · Science

Doc2math

Convert narrative technical documents into grounded Mathematical Problem Specifications with variables, constraints, objectives, and uncertainty.

Agentic Awesome SkillsAdded Sep 5, 2026
Data & AnalyticsScience & Research
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How to use it

  1. Start your plan and connect your AI once
  2. Ask for the task in your own words, or say it directly:
Use the Doc2math skill to help me with this.

Without a connection: copy the SKILL.md below into your AI's project instructions.

SKILL.md

DOC2MATH — Document-to-Mathematics Problem Specification

When to Use This Skill

  • "Formalize this problem statement into math"
  • "Extract the mathematical structure from this research paper section"
  • "What variables, constraints, and objectives are in this spec?"
  • "Convert this word problem to a structured MPS"
  • "Find what's missing in this problem formulation"

Zero-Inference Protocol (Mandatory)

  1. Closed World — if it is not stated in the document, it does not exist in output
  2. Grounding Rule — every element must cite the exact source phrase ("evidence" field)
  3. No Silent Filling — unknown values use null; ambiguous types use "ambiguous"
  4. Inference Tagging — structural inferences tagged "inferred": true with "inference_basis"
  5. MISSING Markers — elements mentioned but insufficiently defined get "status": "MISSING" with "missing_reason"
  6. No Hallucinated Math — never introduce equations or values not in the source text

Limitations

  • Does not invent missing equations, domains, values, or assumptions that are absent from the source document.
  • Requires enough source text to cite every extracted element; sparse prompts should be returned with explicit missing-information markers.
  • Produces a formal specification, not a solved optimization model or proof.

How It Works

Step 1 — Receive Document

Accept the document text, research excerpt, problem description, or specification as input.

Step 2 — Classify

Identify problem_class: optimization | classification | simulation | proof | estimation | other

Step 3 — Extract MPS Components

Variables — id, name, symbol, type, domain, units, role, evidence, inferred, status

Operators — id, name, symbol, arity, acts_on, produces, evidence, inferred

Constraints — id, type, expression, variables_involved, evidence, hardness, inferred, status

Objectives — id, direction (minimize/maximize/satisfy/find/prove), expression, variables_involved, evidence, inferred

Uncertainty — id, type (stochastic/epistemic/measurement/model/none_stated), affects, characterization, evidence, status

Step 4 — Surface Missing Information

Identify what the document implies but doesn't state: missing_information[] with element, needed_for, missing_reason.

Step 5 — Validate and Score

validation_flags:

  • has_complete_objectives: true/false/partial
  • has_bounded_variables: true/false/partial
  • has_evidence_for_all_elements: true/false/partial
  • inference_count: integer
  • missing_count: integer
  • overall_formalizability: HIGH/MEDIUM/LOW

Output Format

Produce the complete MPS as a JSON object:

{
  "mps_version": "1.0",
  "source_title": "...",
  "problem_class": "optimization",
  "variables": [...],
  "operators": [...],
  "constraints": [...],
  "objectives": [...],
  "uncertainty": [...],
  "missing_information": [...],
  "validation_flags": {
    "overall_formalizability": "HIGH"
  }
}

Best Practices

  • ✅ Apply all 6 Zero-Inference Protocol rules before outputting any element
  • ✅ Surface MISSING markers rather than silently inferring — incomplete formalization is valid output
  • ✅ Cite the exact source phrase in every evidence field
  • ❌ Never introduce mathematical relationships not grounded in the source text

Additional Resources

More science skills

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