mcpbeat

Math Review

athola/math-review

Verifies math-heavy code for algorithmic correctness and numerical stability. Use when reviewing scientific algorithms, ML models, or numerical code.

5k tokens
context cost
the whole folder, loaded on every use
5
files
instructions only
0
copies elsewhere
how many repositories repackaged it
324
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/athola/claude-night-market --skill math-review

The instruction itself

17 sections, as written by the author

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

/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

Required TodoWrite Items

  • math-review:context-synced
  • math-review:requirements-mapped
  • math-review:derivations-verified
  • math-review:stability-assessed
  • math-review:evidence-logged
  • math-review:findings-verified

Core Workflow

1. Context Sync

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

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

6. Verify Findings Are Grounded (math-review:findings-verified)

Every issue must cite a real location and a verbatim anchor. Write

findings to .review/findings.json and confirm each citation resolves:

python plugins/imbue/scripts/citation_verifier.py \
  --findings .review/findings.json --repo-root .

Drop or label UNVERIFIED any finding the verifier fails (exit 1);

only verified findings enter the report. See Skill(imbue:review-core)

Step 5 for the protocol and Skill(imbue:structured-output) for the

finding schema.

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

## 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: file.py:123
- Anchor: `verbatim source text at line 123`
- Issue: [what is wrong] | Fix: [remediation] | Evidence: [E1]

## Recommendation
Approve / Approve with actions / Block

Every issue's Anchor is the exact source text at Location; it is what

citation_verifier.py re-reads to prove the finding is real.

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

Exit Criteria

  • Context synced, requirements mapped, derivations verified, stability assessed, evidence logged with citations
  • Every reported issue carries a Location + verbatim Anchor, and citation_verifier.py confirmed all citations (exit 0) or unverified issues were dropped or labeled UNVERIFIED

How to use it

Copy the folder

Take athola/math-review from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.