semantic-compression
Semantic-compression removes grammatical scaffolding from text while preserving meaning-carrying content, outputting fragments rather than complete sentences. Use this skill when condensing text for LLM prompts, reducing token consumption, preparing context windows, or making documentation more efficient. It applies LLM-aware deletion rules that strip predictable articles, copulas, auxiliaries, and filler phrases while retaining nouns, main verbs, negation, temporal markers, and semantic relationships.
git clone --depth 1 https://github.com/can1357/oh-my-pi /tmp/semantic-compression && cp -r /tmp/semantic-compression/.omp/skills/semantic-compression ~/.claude/skills/semantic-compressionSKILL.md
# 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 |
Write system prompts, tool docs, and agent definitions. Project tag conventions + RFC 2119 keywords + dense compression. Use when authoring or editing any prompt the model reads.
Optimize the description prompts an AI agent reads to learn its built-in tools (the `.md` files under prompts/tools/). Two halves: (1) measure how much of a prompt is already inferable from the tool's JSON parameter schema + name, to prune redundancy with evidence; (2) house authoring rules for what belongs in a tool prompt vs what stays in code. Use when auditing, trimming, writing, or reviewing tool prompts, deciding what schema field descriptions already cover, or testing schema-vs-prompt overlap before deleting prompt lines.