AgentSkillsCN

semantic-compression

积极移除语法层面的冗余结构,让大语言模型在保留语义核心内容的前提下进行重建。输出结果可能仅保留片段信息。适用于压缩提示文本、降低 Token 数量、为大语言模型输入准备更精简的上下文,或使文档更加节省 Token 开销。该方法遵循大语言模型特有的压缩规则:在保留语义的同时,删除那些具有高度可预测性的语法成分。

SKILL.md
--- frontmatter
name: semantic-compression
description: 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.

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/that/these/those/he/she/they (drop when referent obvious; replace with noun if ambiguous)
  • Relative pronouns: which, that, who, whom
  • Prepositions: of, for, to, in, on, at, by (keep for material, direction, agency, or disambiguation)

Tier 3 — Delete only if relation still clear:

  • Remaining prepositions: with/without, between/among, within, after/before, over/under, through (drop only if relation obvious)
  • Redundant adverbs: "shout loudly" → "shout"

Always Preserve

  • Nouns, main verbs, meaning-bearing adjectives/adverbs
  • Numbers, quantifiers: "at least 5", "approximately", "more than"
  • Uncertainty markers: "appears", "seems", "reportedly", "what sounded like"
  • Negation: not, no, never, without, none
  • Temporal markers: dates, frequencies, durations
  • Causality and conditionals: because, therefore, despite, although, if, unless
  • Requirements/permissions: must, required, prohibited, allowed
  • Proper nouns, titles, technical terms
  • Prepositions encoding relationships: from/to (direction), with/without (inclusion), between/among/within (relation), after/before (temporal), by (agent if passive)

Structural Compression

  • Passive → active when agent known: "was eaten by dog" → "dog ate"
  • Nominalization → verb: "made a decision" → "decided"
  • Drop implied subject when context allows: "System should log errors" → "Log errors"
  • Redundant pairs → single: "each and every" → "every"
  • Clause → modifier: "anomaly that was reported" → "reported anomaly"

Examples

OriginalCompressed
The system was designed to efficiently process incoming data from multiple sourcesSystem design: efficient process incoming data, multiple sources
There were at least 20 people who appeared to be waitingAt least 20 people apparent waiting
It is important to note that the medication should not be taken without foodMedication: should not take without food
The researcher made a decision to investigate the anomaly that was reportedResearcher decided: investigate reported anomaly