semantic-compress

# Semantic Compress Make a document written **for an LLM reader** smaller while preserving what it *does*. The essence of an LLM-directed document is **behavioural**, not textual - the behaviour it induces in the reading model across the tasks it handles. Compression splits content into two kinds and treats each correctly: - **Core knowledge** - anything in the model's training (named concepts, standard definitions, common-domain background). Replace the explanation with a **pointer**: the bare concept name or a short cue that activates the right knowledge. A pointer costs a few tokens and reliably switches on the model's existing understanding; a full explanation is wasted tokens, and *deleting the concept entirely* gambles that it is already active in the model's reasoning - often it is not, and disambiguation suffers. - **Project / bespoke knowledge** - anything the model cannot know from training: specific facts, local decisions, constraints, and non-standard twists on a known concept. Keep these **explicit and verbatim**. The model has no other source for them. Compression is therefore **point at core, spell out bespoke**. Pointing is not deletion and it is not full explanation; it is the minimum that both activates the right core knowledge and preserves every bespoke detail. ## Mode Selection This skill operates in one of two modes, selected **deterministically**: | Input | Mode | What happens | |-------|------|-------------| | Short snippet with an obvious local ...