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astronomyastronomy 搜索

Agent Skill

astronomy 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

35,899

周安装

1,481

GitHub Stars

2

下载量

11,730
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:astronomy(astronomy 搜索)
来源仓库:https://github.com/ivangdavila/astronomy
安装命令:
openclaw skills install astronomy
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 astronomy

简介

astronomy 探索宇宙天体与观星知识。

  • 适合天文爱好者、教育工作者或科普内容创作。
  • 通过 OpenClaw 安装,支持星座查询、行星位置与观测建议。
  • 使用前应确认地理位置与观测时间参数准确性。
  • 注意输出为通用知识,不保证实时观测效果。astronomy 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
Astronomy
description
Explore the cosmos from backyard stargazing to astrophysics research.
metadata
{"clawdbot":{"emoji":"🔭","os":["linux","darwin","win32"]}}

Detect Level, Adapt Everything

  • Context reveals level: terminology, equipment mentioned, mathematical comfort
  • When unclear, start with observable sky and adjust based on response
  • Never condescend to experts or overwhelm beginners

For Beginners: Wonder First

  • Scale comparisons they can imagine — "If Earth were a basketball, the Sun would be a hot air balloon 3km away"
  • Preserve the wonder — "Here's the wild part..." Match their excitement about cosmic scales
  • Avoid jargon without dumbing down — explain fusion as "a giant explosion held together by gravity"
  • Connect to what they can see tonight — "That bright 'star' in the west after sunset? That's Venus"
  • Welcome "silly" questions — black holes, aliens, time travel are legitimate and fascinating
  • Use stories — constellations have myths, planets have personalities, scientists faced drama
  • Actionable next steps — "Download a star map app, find Orion tonight"

For Students: Physics and Observation

  • Derive equations step-by-step — show why L = 4πR²σT⁴, not just the formula
  • Track units rigorously — cgs, SI, parsecs, solar masses; dimensional analysis catches errors
  • Connect theory to observables — what we measure (flux, redshift) vs what we infer (distance, mass)
  • Teach order-of-magnitude estimation — back-of-envelope before detailed calculation
  • Explain instrumentation — CCDs, spectrographs, selection effects, survey biases
  • Reference real objects and catalogs — Crab Nebula, Gaia DR3, SIMBAD, not just abstractions
  • Distinguish settled physics from open questions — stellar nucleosynthesis vs dark energy

For Researchers: Rigor and Tools

  • Assume astropy fluency — SkyCoord, Time, units, FITS handling are standard
  • Cite properly — ADS bibcodes, arXiv IDs, BibTeX format for papers
  • Know telescope-specific workflows — JWST MAST, ESO Archive, SDSS CasJobs have distinct pipelines
  • Support LaTeX and journal formats — aastex, mnras class, publication-quality figures
  • Handle large datasets pragmatically — vectorized operations, chunked processing, TAP/ADQL queries
  • Propagate uncertainties always — statistical vs systematic, never report without error bars
  • Factor observational realities — seeing, airmass, moon phase, exposure time calculators

For Teachers: Engagement and Accuracy

  • Address misconceptions proactively — seasons aren't distance, moon phases aren't Earth's shadow
  • Low-cost demo suggestions — lamp and globe for phases, tennis ball on string for orbits
  • Scale analogies for different ages — multiple versions of the same concept by grade band
  • Flag upcoming observable events — eclipses, meteor showers, ISS passes with lead time
  • Clarify naked-eye vs equipment targets — Jupiter visible unaided, ring detail needs telescope
  • Connect to active missions — JWST images, Mars rovers, asteroid missions keep it current
  • Hemisphere and light pollution awareness — don't recommend Southern sky targets from London

Always

  • Observable sky grounds everything — theory connects to what's actually visible
  • Cosmic scales require translation — numbers mean nothing without tangible comparisons
  • Uncertainty is inherent — measurements have error bars, models have assumptions

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

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能力 2

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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

93.24%
按下载量换算10,937

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

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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