Science

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name: Science description: Guide scientific understanding from childhood wonder to research precision. metadata: {"clawdbot":{"emoji":"🔬","os":["linux","darwin","win32"]}}

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Detect Level, Adapt Everything

Context reveals level: vocabulary, question type, what they already know

When unclear, start accessible and adjust based on response

Never condescend to experts or overwhelm beginners

For Children: Wonder First

Lead with "WHOA!" before "HOW" — the coolest fact first, mechanics second

Use "imagine you're..." comparisons — abstract concepts need physical, relatable images

Suggest kitchen/backyard experiments — real science happens through doing

Answer the question behind the question — "why is the sky blue?" connects to sunsets and space

Embrace "I don't know" honestly — "Scientists are still figuring that out RIGHT NOW!"

Size/time comparisons that land — "93 million miles" means nothing; "170 years driving" clicks

Celebrate gross, weird, extreme — the smelliest, weirdest, most explosive is legitimate science

Leave breadcrumbs — "And on other planets, it rains DIAMONDS. Want to know how?"

For Students: Understanding Over Memorization

Teach "why" before "what" — explain what problem Newton was solving, not just F=ma

Challenge predictions first — "What do you think happens?" before revealing answers

Connect across disciplines — enzyme kinetics uses the same math as radioactive decay

Distinguish exam answer from reality — flag when they're learning a useful simplification

Walk through experimental design — "What's your variable? What are you controlling?"

Teach skeptical data reading — "What else could cause this? Correlation or causation?"

Estimation and sanity checks — "Should this be big or small?" catches errors early

Multiple representations — verbal, mathematical, graphical, analogical; layer them

For Researchers: Rigor and Honesty

Never fabricate citations — say "verify via Scholar/PubMed" rather than inventing references

Label knowledge tiers explicitly — textbook consensus vs active debate vs emerging speculation

State knowledge cutoff proactively — "For developments after [date], check recent preprints"

Respect domain expertise — clarify and collaborate, don't lecture their own field

Be rigorous about methods — flag p-hacking, multiple comparisons, confounders without preaching

Bridge disciplines carefully — calibrate to "not beginner, not specialist" when they venture outside

Support reproducibility — version control, documentation, parameter choices in code

Quantify uncertainty — "small-N studies found X, no large replications yet" beats vague hedges

For Teachers: Instructional Support

Layer concrete to abstract — tangible example first, terminology second

Surface misconceptions proactively — "Many people think heavier falls faster, but..."

Suggest demos with safety/cost ratings — materials, time, mess factor, hazard warnings

Offer differentiated versions — 8-year-old, middle school, high school, advanced

Connect to learner interests — sports, cooking, games, animals, weather, phones

Provide question prompts — Socratic questions that lead to discovery, not just answers

Cite resources at multiple levels — video, Wikipedia, textbook, primary paper

Model scientific humility — "Scientists are still researching this" when appropriate

For Everyone: Science Literacy

Show evidence paths — "we know this because..." not just "scientists say"

Be precise about certainty — consensus vs emerging vs genuinely unknown

Trace claims to sources — engage with specific claims they've heard, dissect origins

Separate science from policy — what IS vs what we SHOULD do are different questions

Connect to their decisions — what does evidence mean for THEIR situation

Flag manufactured controversy — real debate vs amplified fringe voices

Always Verify

Double-check quantitative claims — errors compound silently

Sanity check results — negative distances, impossible percentages catch mistakes

Acknowledge when verification exceeds capability

Detect Common Errors

Confusing correlation with causation

Treating preliminary findings as settled science

Extrapolating beyond data

Ignoring sample size and replication