semantic-compression
Aggressively remove grammatical scaffolding LLMs reconstruct while preserving meaning-carrying content. Output may be fragments. Use when compressing text for prompts, reducing token count, preparing context for LLM input, or making documentation more token-efficient. Applies LLM-aware compression rules that delete predictable grammar while preserving semantics.
npx skills add majiayu000/claude-skill-registry --skill semantic-compression-az9713-oh-my-pi --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Semantic Compression LLMs reconstruct grammar from content words. Remove predictable glue; keep semantic payload. Prefer fragments over sentences. ## Aggressive Stance - Output can be noun/verb stacks, list fragments, or label:value phrases. - Default to deletion; keep function words only when loss changes meaning. - Prefer base verb forms; drop tense/aspect unless timeline is critical. ## Deletion Tiers **Tier 1 — Always delete (even if fragments):** - Articles: a, an, the - Copulas: is, are, was, were, am, be, been, being - Expletive subjects: "There is/are...", "It is..." - Complementizer: that (as clause marker) - Pure intensifiers: very, quite, rather, really, extremely, somewhat - Filler phrases: "in order to" → to, "due to the fact that" → because, "in terms of" → delete - Infinitive "to" before verbs (unless it prevents noun/verb confusion) - Conjunctions when list/contrast obvious: and, or, but **Tier 2 — Delete unless meaning changes:** - Auxiliary verbs: have/has/had, do/does/did, will/would (keep if tense/aspect matters) - Modal verbs: can/could/may/might/should (keep when obligation/permission/possibility is critical; always keep must/must not) - Pronouns: it/this/th
- Aggressive Stance
- Deletion Tiers
- Always Preserve
- Structural Compression
- Examples
What does the semantic-compression skill do?
Aggressively remove grammatical scaffolding LLMs reconstruct while preserving meaning-carrying content. Output may be fragments. Use when compressing text for prompts, reducing token count, preparing context for LLM input, or making documentation more token-efficient. Applies LLM-aware compression rules that delete predictable grammar while preserving semantics.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill semantic-compression-az9713-oh-my-pi --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.