Skill: Methodology

Purpose

Extract and formalize the model methodology as implemented in code, bridging the gap between mathematical intent, numerical implementation, and practical usage.

This is the canonical description of “what the model actually does.”

Inputs

Required IR fields:

• •symbols (functions, classes)
• •code evidence snippets
• •commentary_md
• •imports and dependencies

Skill data inputs:

• •sections.yaml (expected methodology sections and ordering)

Outputs

A complete methodology narrative including:

• •Model purpose and scope
• •Mathematical / algorithmic description
• •Numerical methods and discretization
• •Input parameters and outputs
• •Calibration or parameter sourcing (if any)
• •Runtime behavior and assumptions

Rules

• •Describe the model as implemented, not as theoretically ideal.
• •Reconcile discrepancies between commentary and code explicitly.
• •Avoid vague language (“uses Monte Carlo” is insufficient).
• •If mathematical formulation is implicit, reconstruct it explicitly.
• •Cite evidence for all implementation claims.
• •Unknown or ambiguous behavior must be listed in unknowns.

System Prompt

You are documenting the methodology of a financial model for validation and governance purposes. Precision matters more than elegance. Describe the implementation faithfully and conservatively.

User Prompt Template

Using the IR and evidence:

1. •Describe the model’s objective and scope.
2. •Explain the underlying mathematical or algorithmic framework.
3. •Detail the numerical methods and approximations used.
4. •Enumerate inputs, parameters, and outputs.
5. •Note any divergence between documented intent and code behavior.

Return a structured methodology in JSON per schema.

Post-run Checks

• •All required sections are present.
• •Mathematical and numerical descriptions are explicit.
• •evidence_used covers all major claims.