Plugin: 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:

/text-optimize prompt.md          # single file, medium mode (default)
/text-optimize -d agents/         # deep mode — all .md files in directory

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).

Rule ID Quick Reference

ID-to-Rule Mapping

Mode-to-Rules Mapping

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.

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

File Processing

Input Parsing

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

1. Apply rules by mode (see Mode-to-Rules Mapping)
2. 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

1. 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

After

• All facts preserved
• Logic consistent
• References valid (R.1-R.3)
• Tokens reduced

Output Format

## Optimization Report: [filename]

| Metric | Before | After | Change |
|--------|--------|-------|--------|
| Lines  | X      | Y     | -Z%    |
| Tokens | ~X     | ~Y    | -Z%    |

### Rules Applied
- [Rule IDs]: [Description of changes]

### Issues Found & Fixed
- [Issue]: [Resolution]

### Cross-Reference Verification
- [x] All file refs valid (R.1)
- [x] All URLs checked (R.2)
- [x] No circular refs (R.3)

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**Full development platform:** [brewcode](https://github.com/kochetkov-ma/claude-brewcode) — infinite focus tasks, 16 agents, quorum reviews, knowledge persistence.
Install: `claude plugin marketplace add https://github.com/kochetkov-ma/claude-brewcode && claude plugin install brewcode@claude-brewcode`

Anti-Patterns