Astronomy

Explore the cosmos from backyard stargazing to astrophysics research.

Security Scan

The skill's instructions and requirements align with its stated purpose (astronomy tutoring and research guidance); it is an instruction-only skill that requests no credentials, does not install software, and appears internally consistent.

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.

Assessment

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.

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