text-optimizer

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.

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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

## 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

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)