promptslim 0.3.0

AI Prompt Slimming Toolkit — reduce token consumption at the source before every API call. 40+ Chinese & English redundancy patterns, code protection, Anthropic Prompt Caching analysis.

PromptSlim 🪒

AI Prompt Slimming Toolkit — reduce token consumption at the source before every API call.

🌐 English | 中文

🎯 Token Optimization · Cost Saving · Prompt Engineering · LLM Tools

📖 掘金详解 v0.3.0： 给你的 AI 提示词剃得再干净一点

What Problem Does This Solve?

Every word you send to an LLM costs money. Filler words, redundant phrases, and polite fluff silently drain your budget. Most developers don't realize 5-40% of their token spend is waste.

PromptSlim strips redundancy at the prompt level — before it reaches the API — giving you free savings with zero code changes to your app logic.

Quick Start

pip install git+https://github.com/JING04-PRODUCER/promptslim.git

Python SDK

from promptslim import quick_slim

text = "嗯，那个我想说的是，这个功能非常非常好用，对吧？"
report = quick_slim(text)
print(f"Token saved: {report.savings_pct}% | Cost saved: ${report.cost_per_call_saved:.6f}/call")

CLI

# Count tokens
promptslim count "Hello world" -m gpt-4o

# Quick slim (rule-based, no API required)
promptslim slim prompt.txt -o slimmed.txt

# Smart compression (LLM-powered, preserves semantics)
promptslim smart long_chat.json -m gpt-4o-mini --max-tokens 512 -o slimmed.txt

# Compare two texts
promptslim compare old.txt new.txt

Demo

Original: In order to basically say that this is really very important
          and actually I think we should definitely consider it.
Slimmed:  say this is important and I think we should consider it.
Saved:    31.3% tokens

Original: 嗯，那个我想说的是，这个功能非常非常非常好用，对吧？你知道吗？
Slimmed:  我想说的是，这个功能好用。
Saved:    40.0% tokens

Features

Feature	Description
🔍 Redundancy Detection	40+ patterns in Chinese & English — filler words, redundant modifiers, verbose phrases
📝 Smart Compression	LLM-powered semantic compression for chat history before context overflow
📊 Comparison Reports	Before/after token count, cost, and savings percentage at a glance
🎯 Multi-Model Tokenizer	Accurate tiktoken counting for GPT / Claude / DeepSeek / Qwen
🔧 Python SDK	One-line integration: from promptslim import quick_slim
🌐 Bilingual	Works with both Chinese and English text

Redundancy Patterns

Type	Examples
English fillers	um, uh, hmm, basically, literally, actually
English modifiers	very, really, extremely, absolutely
English verbose phrases	in order to → to, due to the fact that → because
Chinese fillers	嗯, 啊, 哦, 那个, 就是说
Chinese modifiers	非常, 特别, 极其, 十分, 超级
Polite fluff	希望对你有所帮助, 如有问题请随时联系
Repeated punctuation	！！→！, ？？→？

Paired with AI Cost Sentinel

Slim before call → Track after call. Form a complete cost optimization loop.

import openai
from promptslim import quick_slim

# 1. Slim before sending
text = load_prompt()
report = quick_slim(text)

# 2. Send through Sentinel proxy (tracks actual cost)
client = openai.OpenAI(base_url="http://localhost:8000/v1")
response = client.chat.completions.create(
    model="gpt-4o",
    messages=[{"role": "user", "content": report.slimmed}]
)

# 3. See estimated savings
print(f"Estimated savings: ${report.cost_per_call_saved:.6f}/call")

Project Structure

promptslim/
├── promptslim/
│   ├── __init__.py        # Public API exports
│   ├── cli.py             # CLI entry point
│   ├── compressor.py      # Compressors (rule-based + LLM)
│   ├── redundancy.py      # Redundancy detection patterns
│   ├── reporter.py        # Report generation + pricing table
│   └── tokenizer.py       # Multi-model token counting
├── tests/
│   └── test_prompslim.py
├── pyproject.toml
└── README.md

Roadmap

• Web playground (paste text, see savings live)
• VS Code extension (slim on save)
• Custom regex rules
• Batch processing directory
• LangChain / LlamaIndex integration

AI Assistance

This project was developed with Claude (Anthropic) as a coding assistant. AI contributions include code structure suggestions, test generation, and documentation drafts. All AI-generated code has been reviewed and verified by the developer. Design decisions and core logic are independently authored.

License

MIT — see LICENSE