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LLMBooster

作者 danlct27 · GitHub ↗ · v1.7.0 · MIT-0
cross-platform ✓ 安全检测通过
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在 OpenClaw 中安装
/install llmbooster
功能描述
A 4-step thinking framework to boost LLM output quality. Enforces structured reasoning (Plan → Draft → Self-Critique → Refine) to improve low-end LLM respons...
使用说明 (SKILL.md)

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:

  1. LLM reads prompts/plan.md → Create structured plan
  2. LLM reads prompts/draft.md → Write complete draft
  3. LLM reads prompts/self_critique.md → Review issues
  4. 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 plan
  • draft.md - Step 2: Write complete draft
  • self_critique.md - Step 3: Review and list improvements
  • refine.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
安全使用建议
What to consider before installing: - This skill is coherent with its description: it uses local prompt templates and provides CLI/status commands, and it does not ask for external credentials or network access. - It writes and reads files inside its own skill directory (prompts, config schema, booster_stats.json). That is expected for local state/stats, but be aware of persistent files being created/updated. - The SKILL.md example runs a shelled python3 -c that imports modules from the skill folder. That will execute code from the installed skill. Before running CLI commands, inspect the code in the skill directory (especially booster.py, cli_handler.py, state_manager.py) to ensure nothing unexpected was added or modified. - I noticed some code-quality inconsistencies (e.g., a malformed import line in the shown booster.py and small version metadata mismatches). These look like packaging / formatting mistakes rather than malicious intent, but they could cause runtime errors. Consider running the included unit tests in a safe environment (or reviewing the full source) before enabling. - If you install, prefer obtaining the skill from a trusted source, verify file integrity, and (if possible) run it in a sandbox or with limited permissions until you confirm behavior. - If you want, I can point out the exact files/lines with the syntax/metadata issues and suggest fixes or give guidance on how to run the tests safely.
功能分析
Type: OpenClaw Skill Name: llmbooster Version: 1.7.0 The LLMBooster skill is a well-engineered thinking framework designed to improve LLM output quality through a multi-step reasoning process (Plan, Draft, Self-Critique, Refine). The bundle includes a comprehensive test suite (unit and property-based tests), clear documentation, and structured state management. No evidence of data exfiltration, malicious prompt injection, or unauthorized system access was found; the shell execution defined in SKILL.md is strictly used to interface the agent with the local Python CLI handler for status and configuration management.
能力评估
Purpose & Capability
Name/description (a 4-step thinking framework) matches the provided files and prompts. Required env vars/binaries are none and the code only accesses prompt files, a local JSON config schema, and a local stats file — all consistent with a local LLM prompting helper.
Instruction Scope
SKILL.md instructs the LLM to read local prompt templates and lists a CLI flow. The example /booster command runs a python3 -c snippet that imports and executes code from the skill directory; this is within the scope of a CLI-enabled skill but it means the command will execute local Python modules (not network calls). No instructions request unrelated system files, environment variables, or external endpoints.
Install Mechanism
No install spec is provided (instruction-only in registry), and all code lives in the skill package. No external downloads or package installs are present in the manifest.
Credentials
The skill requests no environment variables or external credentials. It only reads/writes files inside its own skill directory (prompts, config schema, booster_stats.json), which is appropriate for the functionality described.
Persistence & Privilege
The skill persists usage stats to booster_stats.json in the skill directory (write access). Also, the documented CLI runs Python code from the skill directory: if the skill's files are modified by an attacker, running that CLI snippet would execute arbitrary local code. The skill is NOT force-enabled (always:false) and allows normal autonomous invocation.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install llmbooster
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /llmbooster 触发
  4. 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
版本历史
v1.7.0
Full English SKILL.md. Clarified trigger conditions and thinking framework purpose.
v1.6.0
Full English README. Clarified as thinking framework for low-end LLMs.
v1.5.0
Repositioned as thinking framework (no LLM endpoint needed). Focus on guiding low-end LLMs through structured thinking. Removed async execution, simplified core.
v1.4.0
Added /booster stats command for usage statistics
v1.3.0
Added persistent statistics: tasks_processed saved across sessions
v1.2.0
Added visual feedback: Booster branding, progress bar, emoji indicators
v1.1.0
Added more natural language triggers
v1.0.0
Initial release - Multi-step thinking pipeline
元数据
Slug llmbooster
版本 1.7.0
许可证 MIT-0
累计安装 1
当前安装数 1
历史版本数 8
常见问题

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。

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