Optimizes text, prompts, and documentation for LLM token efficiency. Applies 52 research-backed rules across 8 categories: Claude behavior, token efficiency, structure, deduplication, reference integrity, perception, LLM comprehension, and aggressive lossy (deep only). Use when optimizing prompts, r

Plugin: [kochetkov ma/claude brewcode](https://github.com/kochetkov ma/claude brewcode) Text Optimizer Reduces token count in prompts, docs, and agent instructions by 20–40% without losing meaning. Applies 52 research backed rules across 8 categories: Claude behavior, token efficiency, structure, deduplication, reference integrity, perception, LLM comprehension, aggressive lossy (deep only). Benefits: cheaper API calls · faster model responses · clearer LLM instructions · fewer hallucinations Examples: Skill text is written for LLM consumption and optimized for token efficiency. Text & File Optimizer Step 0: Load Rules REQUIRED: Read references/rules review.md before ANY optimization. If file not found ERROR + STOP. Do not proceed without rules reference. Modes Parse $ARGUMENTS : l / light d / deep no flag medium (default). Mode Flag Scope Light l , light Text cleanup only — structure, lists, flow untouched Medium (default) Balanced restructuring — all standard transformations Deep d , deep Max density — rephrase, merge, compress aggressively Rule ID Quick Reference Category Rule IDs Scope Claude behavior C.1 C.8 Literal following, avoid "think", positive framing, match style, descriptive instructions, overengineering, avoid ALL CAPS, prompt format Token efficiency T.1 T.8, T.10 Tables, bullets, one liners, inline code, abbreviations, filler, comma lists, arrows, strip whitespace Structure S.1 S.8 XML tags, imperative, single source, context/motivation, blockquotes, progressive disclosure, consistent terminology, ref depth Deduplication D.1 D.6 Exact/near/cross format merge, emphasis cap <=2, cross file SSOT, wrong merge guard Reference integrity R.1 R.3 Verify file paths, check URLs, linearize circular refs Perception P.1 P.6 Examples near rules, hierarchy, bold keywords, standard symbols, instruction order, default over options LLM Comprehension L.1 L.8 Critical info position, documents first, conciseness, quote first, add WHY, reiterate constraint, prompt repetition, preserve scope qualifiers Aggressive lossy (deep only) A.1 A.4 Line fusion, low value word drop, aggressive paraphrase, common knowledge elision ID to Rule Mapping ID Rule ID Rule C.1 Literal instruction following C.2 Avoid "think" word C.3 Positive framing (do Y not don't X) C.4 Match prompt style to output C.5 Descriptive over emphatic instructions C.6 Overengineering prevention T.1 Tables over prose (multi column) T.2 Bullets over numbered (~5 10%) T.3 One liners for rules T.4 Inline code over blocks T.5 Standard abbreviations (tables only) T.6 Remove filler words T.7 Comma separated inline lists T.8 Arrows for flow notation S.1 XML tags for sections S.2 Imperative form S.3 Single source of truth S.4 Add context/motivation S.5 Blockquotes for critical S.6 Progressive disclosure R.1 Verify file paths R.2 Check URLs R.3 Linearize circular refs P.1 Examples near rules P.2 Hierarchy via headers (max 3 4) P.3 Bold for keywords (max 2 3/100 lines) P.4 Standard symbols (→ + / ✅❌⚠️) S.7 Consistent terminology S.8 One level reference depth P.5 Instruction order (anchoring) P.6 Default over options C.7 Avoid ALL CAPS emphasis (4.x) C.8 Prompt format → output format T.10 Strip whitespace from code L.1 Critical info at START or END L.2 Documents first, query last L.3 Explicitly request conciseness L.4 Quote first grounding L.5 Add WHY to instructions L.6 Reiterate constraint at END L.7 Prompt repetition (non reasoning) L.8 Preserve scope qualifiers D.1 Exact duplicate merge D.2 Near duplicate merge (keep specific) D.3 Cross format duplicate D.4 Emphasis cap (<=2/doc, echo @ END) D.5 Cross file dedup (SSOT + pointer) D.6 Wrong merge guard A.1 Line fusion (loss free merge) A.2 Low value word drop (gate neutral) A.3 Aggressive paraphrase (preserved) A.4 Common knowledge elision (ledger) Mode to Rules Mapping Mode Applies Notes Light C.1 C.8, T.6, D.1, R.1 R.3, P.1 P.4, L.1 L.8 Text cleanup only — no restructuring Medium All rules (C + T + S + D + R + P + L) Balanced transformations Deep All rules (C + T + S + D + R + P + L) + A.1 A.4 (aggressive lossy) Merge sections, max compression Loss Budget per Mode Content essence is untouchable at light/medium; small deliberate loss only at deep — explicitly reported. Dedup merged facts count as preserved, never as loss. Mode Semantic match target Allowed loss Light 100% None — wording cleanup only Medium 100% None — self check fact inventory, zero loss Deep = 95% Low value connective detail; self verify round required, warn with loss list if < 95% Deduplication Pass (All Modes) Runs during analysis, BEFORE compression: 1. Build fact inventory: one atomic fact per line, numbered 2. Flag facts appearing 2+ times (exact, reworded, or cross format) 3. Classify each repeat: intentional emphasis (marked critical/blockquote, or start+end sandwich) vs accidental 4. Accidental merge to single MOST SPECIFIC statement (D.1 D.3), best position wins 5. Intentional cap at 2: full form early + <=1 line echo at END (D.4) 6. Wrong merge guard (D.6): differing scope/numbers/conditions = NOT duplicates — keep both Usage Input Action No args Prompt user for file or folder path Single path Process file directly path1, path2 Process files sequentially l file.md Light mode — text cleanup only d file.md Deep mode — max compression folder/ All .md files in directory File Processing Input Parsing Input Action No args Prompt user for file or folder path Single path Process directly path1, path2 Process files sequentially Execution Flow 1. Read references/rules review.md — load all optimization rules 2. Read target file(s) 3. Analyze: identify type (prompt, docs, agent, skill), note critical info and cross references 3a. Dedup pass (D.1 D.6): fact inventory merge accidental dups cap intentional emphasis at 2/doc 3b. Deep only: aggressive lossy pass (A.1 fusion A.3 paraphrase A.2 word drop A.4 elision); A.2/A.4 drops loss ledger; A.4 counts as elided known against the =95% gate, A.2 is gate neutral 4. Apply rules by mode (see Mode to Rules Mapping) 5. Edit file with optimized content 5a. Medium: self check — re check fact inventory against output, zero loss required 5b. Deep mode: self verify — fact inventory original vs compressed, (kept + merged)/total = 95%; merged = preserved; warn with loss list if below 6. Generate optimization report Quality Checklist Before [ ] Read entire text [ ] Identify type (prompt, docs, agent, skill) [ ] Note critical info and cross references During — Apply by Mode Check Light Med Deep C.1 C.8 (Claude behavior) Yes Yes Yes T.6 (filler removal) Yes Yes Yes T.1 T.5, T.7 T.8 (token compression) Yes Yes T.10 (strip code whitespace) Yes Yes S.1 S.8 (structure/clarity) Yes Yes D.1 (exact dedup) Yes Yes Yes D.2 D.4, D.6 (smart dedup + emphasis cap) Yes Yes D.5 (cross file dedup, folder runs) Yes Yes Yes R.1 R.3 (reference integrity) Yes Yes Yes P.1 P.4 (LLM perception) Yes Yes Yes P.5 P.6 (anchoring, default over options) Yes Yes L.1 L.8 (LLM comprehension) Yes Yes Yes A.1 A.4 (aggressive lossy) Yes Loss within mode budget 100% 100% =95% After [ ] All facts preserved [ ] Logic consistent [ ] References valid (R.1 R.3) [ ] Tokens reduced Output Format Anti Patterns Avoid Why Remove all examples Hurts generalization (P.1) Over abbreviate Reduces readability (T.5 caveat) Generic compression Domain terms matter Over aggressive language Opus 4.5 overtriggers (C.5) Flatten hierarchy Loses structure (P.2) "Don't do X" framing Less effective than "Do Y" (C.3) Overengineer prompts Opus 4.5 follows literally (C.6) Overload single prompts Divided attention, hallucinations (S.3) Over focus on wording Structure word choice (T.1) Merge similar looking facts blindly Different scope/numbers/conditions = different facts (D.6)

text-optimizer