math-review

Verifies math-heavy code for algorithmic correctness and numerical stability

Night Market Skill — ported from claude-night-market/pensive. For the full experience with agents, hooks, and commands, install the Claude Code plugin.

Table of Contents

• Quick Start
• When to Use
• Required TodoWrite Items
• Core Workflow
• 1. Context Sync
• 2. Requirements Mapping
• 3. Derivation Verification
• 4. Stability Assessment
• 5. Proof of Work
• Progressive Loading
• Essential Checklist
• Output Format
• Summary
• Context
• Requirements Analysis
• Derivation Review
• Stability Analysis
• Issues
• Recommendation
• Exit Criteria

Mathematical Algorithm Review

Intensive analysis ensuring numerical stability and alignment with standards.

Quick Start

bash

/math-review

Verification: Run the command with --help flag to verify availability.

When To Use

• Changes to mathematical models or algorithms
• Statistical routines or probabilistic logic
• Numerical integration or optimization
• Scientific computing code
• ML/AI model implementations
• Safety-critical calculations

When NOT To Use

• General algorithm review - use architecture-review
• Performance optimization - use parseltongue:python-performance
• General algorithm review - use architecture-review
• Performance optimization - use parseltongue:python-performance

Required TodoWrite Items

1. math-review:context-synced
2. math-review:requirements-mapped
3. math-review:derivations-verified
4. math-review:stability-assessed
5. math-review:evidence-logged

Core Workflow

1. Context Sync

bash

pwd && git status -sb && git diff --stat origin/main..HEAD

Verification: Run git status to confirm working tree state. Enumerate math-heavy files (source, tests, docs, notebooks). Classify risk: safety-critical, financial, ML fairness.

2. Requirements Mapping

Translate requirements → mathematical invariants. Document pre/post conditions, conservation laws, bounds. Load: modules/requirements-mapping.md

3. Derivation Verification

Re-derive formulas using CAS. Challenge approximations. Cite authoritative standards (NASA-STD-7009, ASME VVUQ). Load: modules/derivation-verification.md

4. Stability Assessment

Evaluate conditioning, precision, scaling, randomness. Compare complexity. Quantify uncertainty. Load: modules/numerical-stability.md

5. Proof of Work

bash

pytest tests/math/ --benchmark
jupyter nbconvert --execute derivation.ipynb

Verification: Run pytest -v tests/math/ to verify. Log deviations, recommend: Approve / Approve with actions / Block. Load: modules/testing-strategies.md

Progressive Loading

Default (200 tokens): Core workflow, checklists +Requirements (+300 tokens): Invariants, pre/post conditions, coverage analysis +Derivation (+350 tokens): CAS verification, standards, citations +Stability (+400 tokens): Numerical properties, precision, complexity +Testing (+350 tokens): Edge cases, benchmarks, reproducibility

Total with all modules: ~1600 tokens

Essential Checklist

Correctness: Formulas match spec | Edge cases handled | Units consistent | Domain enforced Stability: Condition number OK | Precision sufficient | No cancellation | Overflow prevented Verification: Derivations documented | References cited | Tests cover invariants | Benchmarks reproducible Documentation: Assumptions stated | Limitations documented | Error bounds specified | References linked

Output Format

markdown

## Summary
[Brief findings]

## Context
Files | Risk classification | Standards

## Requirements Analysis
| Invariant | Verified | Evidence |

## Derivation Review
[Status and conflicts]

## Stability Analysis
Condition number | Precision | Risks

## Issues
[M1] [Title]: Location | Issue | Fix

## Recommendation
Approve / Approve with actions / Block

Verification: Run the command with --help flag to verify availability.

Exit Criteria

• Context synced, requirements mapped, derivations verified, stability assessed, evidence logged with citations