🎁 Get the FREE AI Skills Starter Guide β€” Subscribe β†’
BytesAgainBytesAgain
BytesAgain β€Ί Skills β€Ί Nm Pensive Math Review
πŸ¦€ ClawHub

Nm Pensive Math Review

by @athola

Verifies math-heavy code for algorithmic correctness and numerical stability

Versionv1.9.16
Downloads891
#clawhub
TERMINAL
clawhub install nm-pensive-math-review

πŸ“– About This Skill

name: math-review description: | Verify math-heavy code for algorithm correctness, numerical stability, and standards alignment version: 1.9.4 triggers: - math - algorithms - numerical - stability - verification - scientific metadata: {"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/pensive", "emoji": "\ud83e\udd9e", "requires": {"config": ["night-market.pensive:shared", "night-market.imbue:proof-of-work"]}}} source: claude-night-market source_plugin: pensive

> Night Market Skill β€” 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

    /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

    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 β†’ 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

    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 | 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

    • Troubleshooting

    Common Issues

    Command not found Ensure all dependencies are installed and in PATH

    Permission errors Check file permissions and run with appropriate privileges

    Unexpected behavior Enable verbose logging with --verbose flag

    ⚑ When to Use

    TriggerAction
    - Statistical routines or probabilistic logic
    - Numerical integration or optimization
    - Scientific computing code
    - ML/AI model implementations
    - Safety-critical calculations

    πŸ’‘ Examples

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

    πŸ“‹ Tips & Best Practices

    Common Issues

    Command not found Ensure all dependencies are installed and in PATH

    Permission errors Check file permissions and run with appropriate privileges

    Unexpected behavior Enable verbose logging with --verbose flag