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danlct27

LLMBooster

by danlct27 · GitHub ↗ · v1.7.0 · MIT-0
cross-platform ✓ Security Clean
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Install in OpenClaw
/install llmbooster
Description
A 4-step thinking framework to boost LLM output quality. Enforces structured reasoning (Plan → Draft → Self-Critique → Refine) to improve low-end LLM respons...
README (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
Usage Guidance
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.
Capability Analysis
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.
Capability Assessment
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.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install llmbooster
  3. After installation, invoke the skill by name or use /llmbooster
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
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
Metadata
Slug llmbooster
Version 1.7.0
License MIT-0
All-time Installs 1
Active Installs 1
Total Versions 8
Frequently Asked Questions

What is LLMBooster?

A 4-step thinking framework to boost LLM output quality. Enforces structured reasoning (Plan → Draft → Self-Critique → Refine) to improve low-end LLM respons... It is an AI Agent Skill for Claude Code / OpenClaw, with 161 downloads so far.

How do I install LLMBooster?

Run "/install llmbooster" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is LLMBooster free?

Yes, LLMBooster is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does LLMBooster support?

LLMBooster is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created LLMBooster?

It is built and maintained by danlct27 (@danlct27); the current version is v1.7.0.

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