Doc2math

Skill sickn33/agentic-awesome-skills/plugins/agentic-awesome-skills-claude/skills/doc2math

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Install

npx -y skills add sickn33/agentic-awesome-skills --skill doc2math

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What its author says it does

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

SKILL.md

3.9 KB, 930 tokens by cl100k_base, as published. Nobody here has run it

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)

Closed World — if it is not stated in the document, it does not exist in output
Grounding Rule — every element must cite the exact source phrase ("evidence" field)
No Silent Filling — unknown values use null; ambiguous types use "ambiguous"
Inference Tagging — structural inferences tagged "inferred": true with "inference_basis"
MISSING Markers — elements mentioned but insufficiently defined get "status": "MISSING" with "missing_reason"
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

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

Repository: thebrierfox/doc2math-skill
Full BYOK tool: ace-license-server-production.up.railway.app/byok/doc2math
Built by IntuiTek¹ (~K¹) — MIT License