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:** 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: ```bash 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 <1-4>` | 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 ```bash /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 |