Individual skill
s4h-information-compression
Applies lossy vs lossless compression thinking to representation and communication. Use when asked to 'cut this down', 'what can I remove', 'what's essential here', 'summarise without losing the key point', 'what must be preserved', or when a representation needs to be more efficient without becoming misleading.
skills/s4h-information-compression/SKILL.md
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- MIT
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# Information: Compression
Every representation is a compression of reality. A report compresses events. A model compresses a domain. An explanation compresses understanding. A decision brief compresses weeks of analysis. The question is never whether to compress — it's whether the compression preserves what matters.
Claude Shannon proved that there is a theoretical lower bound on how far any lossless compression can go: you cannot compress below the entropy of the source without discarding information. This is the source coding theorem. Applied practically: there is always a floor. A message with genuine information content cannot be shortened indefinitely without loss — and often the thing being shortened to below-floor is not actually long; it's precise. The problem is usually the reverse: sources that are padded, redundant, or poorly structured can be dramatically shortened without any loss, because they weren't near the floor to begin with.
The critical distinction is lossy vs. lossless. Lossless compression preserves every bit of the original — the compression is reversible. Lossy compression discards some information permanently, in exchange for a representation that is smaller or more usable. Both are legitimate; the choice depends entirely on what the information is for, who will use it, and what the cost of loss is. An executive summary is lossy by design. A legal contract is lossless by necessity. The skill is making the trade-off explicit — and knowing what you are throwing away.
James Gleick's *The Information* notes that compression is cognition: the brain is fundamentally a compression engine. Understanding is compression. This is why the best explanations are short: not because they're simpler, but because they've found the structure that allows reconstruction from minimal representation. Teaching someone a principle rather than a list of cases is compression.
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## Your Process
