Individual skill
semantic-compress
Make an LLM-directed document smaller while preserving what it does. Two modes: a local span-level core->pointer pass, and an A/B-validated distill loop that produces the smallest document that behaves the same as the original. Point at core knowledge the model already holds (a concept name activates it); keep project-specific detail explicit and verbatim. TRIGGER when asked to compress, tighten, shorten, or strip a prompt / instruction / system message meant for an LLM; to distill a skill; to compress a whole document; to make this smaller while preserving behaviour; to A/B test a compression or produce a behaviourally-equivalent compression; when an instruction set explains concepts the model already knows from training; or when reducing token cost of an LLM-directed prompt without losing meaning. Not for human-facing prose - that is /deslop.
skills/semantic-compress/SKILL.md
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- Oct 16, 2026
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- Apache-2.0
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SKILL.mdcore · 23,631 bytesSKILL.mdcore · 23,631 bytesreferences/battle-scar-classifier.mddoc · 6,953 bytesreferences/cognitive-ergonomics.mddoc · 5,374 bytesreferences/directive-clarity-patterns.mddoc · 7,524 bytesreferences/directive-clarity-rewrites.mddoc · 7,022 bytesreferences/distill-loop.mddoc · 18,140 bytesreferences/distillation-report-template.mddoc · 8,648 bytesreferences/transfer-set-design.mddoc · 11,690 bytes
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# 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 |