text-optimizer
npx machina-cli add skill kochetkov-ma/claude-brewcode/text-optimizer --openclawText Optimizer
Reduces token count in prompts, docs, and agent instructions by 20–40% without losing meaning. Applies 41 research-backed rules across 6 categories: Claude behavior, token efficiency, structure, reference integrity, perception, LLM comprehension.
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.mdbefore 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.6 | Literal following, avoid "think", positive framing, match style, descriptive instructions, overengineering |
| Token efficiency | T.1-T.8 | Tables, bullets, one-liners, inline code, abbreviations, filler, comma lists, arrows |
| Structure | S.1-S.8 | XML tags, imperative, single source, context/motivation, blockquotes, progressive disclosure, consistent terminology, ref depth |
| 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 |
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 |
Mode-to-Rules Mapping
| Mode | Applies | Notes |
|---|---|---|
| Light | C.1-C.6, T.6, R.1-R.3, P.1-P.4 | Text cleanup only — no restructuring |
| Medium | All rules (C + T + S + R + P) | Balanced transformations |
| Deep | All rules + aggressive rephrasing | Merge sections, max compression |
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
- Read
references/rules-review.md— load all optimization rules - Read target file(s)
- Analyze: identify type (prompt, docs, agent, skill), note critical info and cross-references
- Apply rules by mode (see Mode-to-Rules Mapping)
- Edit file with optimized content
- 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.6 (Claude behavior) | Yes | Yes | Yes |
| T.6 (filler removal) | Yes | Yes | Yes |
| T.1-T.5, T.7-T.8 (token compression) | - | Yes | Yes |
| S.1-S.8 (structure/clarity) | - | Yes | Yes |
| R.1-R.3 (reference integrity) | Yes | Yes | Yes |
| P.1-P.4 (LLM perception) | Yes | Yes | Yes |
| Aggressive rephrasing | - | - | Yes |
| No information loss | Yes | Yes | Yes |
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)
---
**Full development platform:** [brewcode](https://github.com/kochetkov-ma/claude-brewcode) — infinite focus tasks, 14 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
| 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) |
Source
git clone https://github.com/kochetkov-ma/claude-brewcode/blob/main/skills/text-optimizer/SKILL.mdView on GitHub Overview
Text-optimizer reduces token counts in prompts, docs, and agent instructions by 20–40% without sacrificing meaning. It relies on 41 research-backed rules across six categories—Claude behavior, token efficiency, structure, reference integrity, perception, and LLM comprehension—to improve instruction quality and lower API costs.
How This Skill Works
The tool loads rules from references/rules-review.md and applies mode-based transformations (Light, Medium, Deep) that implement the 41 rules across six categories. It rewrites text for density and clarity, prioritizing structured outputs (tables, bullets, single-source truth, consistent terminology) to maximize token efficiency while preserving meaning.
When to Use It
- To shrink prompt or document length without changing meaning (token reduction 20–40%).
- Before deploying or sharing agent instructions, to improve clarity and alignment.
- Compress verbose docs or knowledge bases for faster model consumption.
- Reduce API costs by decreasing token counts in prompts and responses.
- Standardize prompts with consistent terminology and structure across projects.
Quick Start
- Step 1: Read references/rules-review.md and understand the 41 rules.
- Step 2: Choose a mode (-l for Light, -d for Deep, or default Medium) and run the optimizer on targets.
- Step 3: Review the transformed files and verify meaning and instruction quality.
Best Practices
- Always load and reference references/rules-review.md before optimizing.
- Start with Light mode for cleanup; progress to Medium or Deep only if density is needed.
- Prefer 1–2 sentence outputs and bullet lists; use tables for multi-column data.
- Maintain consistent terminology and single source of truth (S.3/S.7).
- Review results to ensure meaning is preserved and avoid reference loops.
Example Use Cases
- Reduce a long Claude prompt from 600 tokens to around 360 tokens without changing intent.
- Compress an verbose wiki-style documentation page into concise steps and bullets.
- Rewrite an instruction set to remove 'think' phrasing and favor descriptive directives.
- Convert a policy file with nested sections into a single-source, clear guide.
- Refactor a library of prompts to use inline code blocks and standard abbreviations.