/install llmbooster
LLMBooster Skill
A Thinking Framework, Not an Automation Tool
LLMBooster is a 4-step thinking framework that improves LLM output quality through structured reasoning. No LLM endpoint needed - the LLM follows the framework itself.
Core Philosophy
Problem with low-end LLMs: Jump to conclusions, miss details, lack self-review.
Booster solution: Enforce structured thinking process.
Plan → Draft → Self-Critique → Refine
Trigger Conditions
- User says "use booster", "booster", or "/booster"
- User requests: "detailed analysis", "in-depth analysis", "help me analyze"
- User requests: "improve quality", "detailed analysis"
- User asks for evaluation, comparison, or decision support
- User requests code review or technical documentation
- User asks complex questions (lengthy tasks, multi-step problems)
How It Works
LLM executes the framework itself, no Python calls needed:
- LLM reads
prompts/plan.md→ Create structured plan - LLM reads
prompts/draft.md→ Write complete draft - LLM reads
prompts/self_critique.md→ Review issues - LLM reads
prompts/refine.md→ Polish final output
Command Handling
When user enters /booster command, execute:
cd ~/.openclaw/workspace/skills/llmbooster && python3 -c "
from config_loader import ConfigLoader
from state_manager import SkillStateManager
from cli_handler import CLICommandHandler
loader = ConfigLoader()
config = loader.load('config.schema.json')
state_mgr = SkillStateManager(config)
cli = CLICommandHandler(state_mgr)
result = cli.handle('/booster status')
print(result.message)
"
CLI Commands
| Command | Description |
|---|---|
/booster enable |
Enable LLMBooster |
/booster disable |
Disable LLMBooster |
/booster status |
Show current status |
/booster stats |
Show usage statistics |
/booster depth \x3C1-4> |
Set thinking depth |
/booster help |
Show help |
Thinking Depth
| Depth | Steps | Quality | Speed | Use Case |
|---|---|---|---|---|
| 1 | Plan | ★★☆☆ | Fastest | Quick analysis, brainstorm |
| 2 | Plan → Draft | ★★★☆ | Fast | General tasks, simple Q&A |
| 3 | + Self-Critique | ★★★★ | Medium | Code review, technical docs |
| 4 | Full pipeline | ★★★★★ | Slowest | Important docs, complex analysis |
Visual Feedback
When executing, Booster displays:
🚀 **Booster Pipeline Started**: Analyzing task...
────────────────────────────────────────
🚀 Booster [█░░░░] Step 1/4: **Plan**
✅ Plan completed (2.3s)
🚀 Booster [██░░░] Step 2/4: **Draft**
✅ Draft completed (5.1s)
🚀 Booster [███░░] Step 3/4: **Self-Critique**
✅ Self-Critique completed (1.8s)
🚀 Booster [████] Step 4/4: **Refine**
✅ Refine completed (3.2s)
────────────────────────────────────────
✅ **Booster Complete** - 4 steps, 12.4s total
Prompt Templates
All templates are in prompts/ directory:
plan.md- Step 1: Create structured plandraft.md- Step 2: Write complete draftself_critique.md- Step 3: Review and list improvementsrefine.md- Step 4: Apply improvements
Why It Works
| Low-End LLM Problem | Booster Solution |
|---|---|
| Jumps to conclusions | Plan step forces structured thinking |
| Misses details | Draft step requires complete coverage |
| No self-review | Self-Critique step finds issues |
| Rough output | Refine step polishes final result |
Usage Statistics
/booster stats
# 📊 **Booster Statistics**
# ───────────────────────
# Status: enabled
# Thinking Depth: 4
# Tasks Processed: 5
# Last Used: 2026-03-22T09:30:00
Files
| File | Purpose |
|---|---|
SKILL.md |
Skill definition + trigger conditions |
README.md |
Documentation |
booster.py |
Core module + helpers |
cli_handler.py |
CLI command processing |
state_manager.py |
State + statistics |
stream_handler.py |
Visual feedback |
config_loader.py |
Config loading |
prompts/*.md |
Step prompt templates |
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install llmbooster - 安装完成后,直接呼叫该 Skill 的名称或使用
/llmbooster触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
LLMBooster 是什么?
A 4-step thinking framework to boost LLM output quality. Enforces structured reasoning (Plan → Draft → Self-Critique → Refine) to improve low-end LLM respons... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 161 次。
如何安装 LLMBooster?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install llmbooster」即可一键安装,无需额外配置。
LLMBooster 是免费的吗?
是的,LLMBooster 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
LLMBooster 支持哪些平台?
LLMBooster 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 LLMBooster?
由 danlct27(@danlct27)开发并维护,当前版本 v1.7.0。