#601

Globales Ranking · von 601 Skills

text-optimizer AI Agent Skill

Quellcode ansehen: kochetkov-ma/claude-brewcode

Safe

Installation

npx skills add kochetkov-ma/claude-brewcode --skill text-optimizer

2.0K

Installationen

Plugin: kochetkov-ma/claude-brewcode

Text 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.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.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

  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
  4. Apply rules by mode (see Mode-to-Rules Mapping)
  5. Edit file with optimized content
  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.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)

Installationen

Installationen 2.0K
Globales Ranking #601 von 601

Sicherheitsprüfung

ath Safe
socket Safe
Warnungen: 0 Bewertung: 90
snyk Low
zeroleaks Safe
Bewertung: 93
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So verwenden Sie diesen Skill

1

Install text-optimizer by running npx skills add kochetkov-ma/claude-brewcode --skill text-optimizer in your project directory. Führen Sie den obigen Installationsbefehl in Ihrem Projektverzeichnis aus. Die Skill-Datei wird von GitHub heruntergeladen und in Ihrem Projekt platziert.

2

Keine Konfiguration erforderlich. Ihr KI-Agent (Claude Code, Cursor, Windsurf usw.) erkennt installierte Skills automatisch und nutzt sie als Kontext bei der Code-Generierung.

3

Der Skill verbessert das Verständnis Ihres Agenten für text-optimizer, und hilft ihm, etablierte Muster zu befolgen, häufige Fehler zu vermeiden und produktionsreifen Code zu erzeugen.

Was Sie erhalten

Skills sind Klartext-Anweisungsdateien — kein ausführbarer Code. Sie kodieren Expertenwissen über Frameworks, Sprachen oder Tools, das Ihr KI-Agent liest, um seine Ausgabe zu verbessern. Das bedeutet null Laufzeit-Overhead, keine Abhängigkeitskonflikte und volle Transparenz: Sie können jede Anweisung vor der Installation lesen und prüfen.

Kompatibilität

Dieser Skill funktioniert mit jedem KI-Coding-Agenten, der das skills.sh-Format unterstützt, einschließlich Claude Code (Anthropic), Cursor, Windsurf, Cline, Aider und anderen Tools, die projektbezogene Kontextdateien lesen. Skills sind auf Transportebene framework-agnostisch — der Inhalt bestimmt, für welche Sprache oder welches Framework er gilt.

Data sourced from the skills.sh registry and GitHub. Install counts and security audits are updated regularly.

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