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在 OpenClaw 中安装
/install astronomy
功能描述
Explore the cosmos from backyard stargazing to astrophysics research.
使用说明 (SKILL.md)
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
安全使用建议
This is an instruction-only astronomy skill and appears to be what it claims: educational and research guidance. It does not ask for credentials or install software. Things to watch for after installing: if the agent asks you to upload local FITS files, run code, or provide API keys for data archives (MAST, ESO, etc.), only do so if you understand and trust the destination; avoid sharing cloud/OS credentials. For advanced research workflows expect the agent to require scientific libraries or dataset access — verify whether computation runs locally (your machine) or remotely, and prefer giving minimal, non-sensitive example data rather than broad access to your systems or private archives.
功能分析
Type: OpenClaw Skill
Name: astronomy
Version: 1.0.0
The skill bundle contains standard metadata and a `SKILL.md` file. The `SKILL.md` provides detailed instructions for an AI agent on how to interact with users of varying expertise levels regarding astronomy. All instructions are pedagogical and conversational, focusing on explaining concepts, adapting communication style, and providing relevant information. There are no commands, network calls, file system operations, or prompt injection attempts designed to subvert the agent or perform harmful actions. The content is entirely aligned with its stated purpose.
能力评估
Purpose & Capability
The name/description (astronomy from backyard to research) matches the SKILL.md content: beginner explanations, student derivations, teacher activities, and researcher workflows. No unrelated binaries, env vars, or config paths are requested.
Instruction Scope
The instructions are conversational and pedagogical and do not direct the agent to read local files or exfiltrate data. For researcher-level guidance the doc assumes access to scientific tools and archives (astropy, FITS handling, JWST/ESO/SDSS workflows). That assumption is reasonable for expert-level help but could lead the agent to request access to large datasets, files, or archive credentials when actually executed; the SKILL.md itself does not instruct any unauthorized file or credential access.
Install Mechanism
No install spec, no downloads, and no code files — lowest-risk instruction-only skill. Nothing will be written to disk by an installer.
Credentials
The skill requests no environment variables or credentials (proportionate). It does, however, presuppose scientific libraries and archive access for research workflows; those dependencies are not declared here, so users should be aware advanced tasks may require providing data or external service access later.
Persistence & Privilege
Defaults (always: false, model invocation allowed) are appropriate. The skill does not request permanent presence or modify other skills' settings.
如何使用
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install astronomy - 安装完成后,直接呼叫该 Skill 的名称或使用
/astronomy触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.0.0
Initial release
元数据
常见问题
Astronomy 是什么?
Explore the cosmos from backyard stargazing to astrophysics research. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 1134 次。
如何安装 Astronomy?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install astronomy」即可一键安装,无需额外配置。
Astronomy 是免费的吗?
是的,Astronomy 完全免费(开源免费),可自由下载、安装和使用。
Astronomy 支持哪些平台?
Astronomy 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(linux, darwin, win32)。
谁开发了 Astronomy?
由 Iván(@ivangdavila)开发并维护,当前版本 v1.0.0。
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