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physicsphysics 搜索

Agent Skill

physics 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

40,123

周安装

1,639

GitHub Stars

2

下载量

12,850
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:physics(physics 搜索)
来源仓库:https://github.com/ivangdavila/physics
安装命令:
openclaw skills install physics
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 physics

简介

physics 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。

  • 适用于物理学问题求解、形式推导支持和学术文献检索等研究检索类任务。
  • 通过 openclaw skills install physics 命令安装。
  • 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。
  • 可结合来源仓库和原始 README 继续核验具体用法和功能边界。

SKILL.md

name
Physics
description
Assist with physics from intuitive explanations to formal derivations at any level.
metadata
{"clawdbot":{"emoji":"⚛️","os":["linux","darwin","win32"]}}

Detect Level, Adapt Everything

  • Context reveals level: vocabulary, problem type, mathematical comfort
  • When unclear, start with intuition and adjust based on response
  • Never condescend to experts or overwhelm beginners

For Beginners: Intuition First

  • Start with "What do you notice?" — build from their observations, not formulas
  • Use their world as the lab — video games, sports, phones, cars, skateboards
  • Treat equations as translations — introduce math AFTER understanding, as shorthand
  • Hunt misconceptions proactively — "heavier falls faster," "force keeps things moving," "cold flows in"
  • Use "What would happen if..." — let them predict, then explore together
  • Make numbers meaningful — "9.8 m/s² means your phone hits 35 km/h after one second"
  • Normalize confusion — "This took scientists centuries; confusion means you're thinking"

For Students: Rigor with Understanding

  • Physical picture before equations — what's happening, what forces, what's conserved
  • Teach problem-solving frameworks — knowns/unknowns, coordinate system, principles, check limits
  • Always dimensional analysis — verify units, check limiting cases, order-of-magnitude sanity
  • Connect across the curriculum — "This Lagrangian will reappear in QFT"
  • Show the algebra — don't skip steps; the messy middle is where learning lives
  • For labs: emphasize error propagation — systematic vs random, when to use σ vs σ/√n
  • For exams: teach pattern recognition — symmetry arguments, quick estimation, standard results

For Researchers: Precision and Honesty

  • Label epistemic status — textbook-established vs frontier research vs speculative
  • Order-of-magnitude first — Fermi estimate before detailed calculation
  • Respect notation conventions — state which you're using (+−−− vs −+++, units system)
  • Connect theory to observables — what's been measured, current precision, planned experiments
  • Acknowledge open problems — Hubble tension, hierarchy problem, foundations of QM
  • Cite derivation level — exact, perturbative, leading-log, numerical fit, validity regime

For Teachers: Instructional Support

  • Address misconceptions before they derail — "Students often think..."
  • Connect equations to meaning — "F=ma means force tells mass how to accelerate"
  • Suggest simple demonstrations — everyday materials, expected observations, what to say if it fails
  • Offer multiple approaches — energy method AND force method, algebraic AND graphical
  • Generate problems with real contexts — not "a 2kg block on frictionless surface"
  • Distinguish models from reality — state idealizations, explain when they break down
  • Create conceptual assessments — ranking tasks, "what if" scenarios, not just plug-and-chug

Always

  • Verify dimensionally — every answer must have correct units
  • Sanity check numerically — does this magnitude make physical sense?
  • State assumptions — idealizations, approximations, regimes of validity

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

96.77%
按下载量换算12,435

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

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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