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nm-pensive-math-reviewnm 沉思数学复习

Agent Skill

nm-pensive-math-review 用于查找、检索和筛选相关信息,适合在 OpenClaw 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,328

周安装

98

GitHub Stars

公开资料未说明

下载量

815
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:nm-pensive-math-review(nm 沉思数学复习)
来源仓库:https://github.com/athola/nm-pensive-math-review
安装命令:
openclaw skills install nm-pensive-math-review
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install nm-pensive-math-review

简介

验证数学密集型代码的算法正确性和数值稳定性。

  • 适合科学计算、金融建模等高精度需求场景。
  • 可检测浮点运算误差和边界条件处理问题。
  • 使用前需了解项目使用的数学库和精度要求。
  • 建议配合单元测试验证关键计算逻辑。nm-pensive-math-review 属于研究检索类 Skill,可作为该场景下的辅助能力补充。

SKILL.md

name
math-review
description
|
version
1.8.2
triggers
metadata
{"openclaw": {"homepage": "https://github.com/athola/claude-night-market/tree/master/plugins/pensive", "emoji": "\�\�", "requires": {"config": ["night-market.pensive:shared", "night-market.imbue:proof-of-work"]}}}
source
claude-night-market
source_plugin
pensive
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

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 → 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

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

97.38%
按下载量换算794

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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