Agent Skillscan1357/oh-my-pi › semantic-compression

semantic-compression

GitHub

通过删除LLM可重构的语法冗余来压缩文本,保留核心语义。适用于减少Token消耗、优化Prompt输入及提升文档效率。

.omp/skills/semantic-compression/SKILL.md can1357/oh-my-pi

Trigger Scenarios

需要压缩文本以减少Token数量 为LLM输入准备上下文 优化Prompt内容

Install

npx skills add can1357/oh-my-pi --skill semantic-compression -g -y
More Options

Non-standard path

npx skills add https://github.com/can1357/oh-my-pi/tree/main/.omp/skills/semantic-compression -g -y

Use without installing

npx skills use can1357/oh-my-pi@semantic-compression

指定 Agent (Claude Code)

npx skills add can1357/oh-my-pi --skill semantic-compression -a claude-code -g -y

安装 repo 全部 skill

npx skills add can1357/oh-my-pi --all -g -y

预览 repo 内 skill

npx skills add can1357/oh-my-pi --list

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

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

Version History

  • b914f55 Current 2026-07-06 00:36

Same Skill Collection

.omp/skills/system-prompts/SKILL.md

Metadata

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Version
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Hash
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Indexed
2026-07-06 00:36

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