Prompt Optimizer: The Universal AI Architect & Scaffolding Platform

🚀 Enterprise-grade, MCP-native platform designed to transform AI development workflows through professional prompt engineering, agentic scaffolding, and cloud-powered optimization.

License split: the skill/ directory (Claude Code Skills) is free and open-source under MIT — no account, no signup. Everything else in this repo (backend, MCP packages, web dashboard) is Commercial — see the root LICENSE.

🌟 The Four-Tier Ecosystem

Prompt Optimizer is more than just a server; it's a complete ecosystem for high-performance AI interaction.

1. ☁️ Cloud Pro (v3.7.5)

The flagship MCP server. Routes complex prompts through a sophisticated LLM rewriting pipeline with Bayesian tuning and AG-UI real-time streaming. Includes team collaboration and shared quotas.

2. 🔒 Local Core (v4.1.2)

A privacy-first, 100% offline version. Uses a library of 120+ domain-specific rules and platform-specific binaries for zero-latency, secure optimization on your own machine.

3. 🖥️ Web Dashboard

The command center at promptoptimizer.xyz. Manage API keys, configure Personal Model Choice (via OpenRouter), track analytics, and run A/B evaluations.

4. ⚡ Claude Code Skills (MIT, zero-friction)

Free Claude Code Skills distilling this platform's methodology into pure in-context instructions. No npm install, no API key, no license key, no external process — copy the SKILL.md you want into .claude/skills/<name>/ and Claude Code loads it directly. Each is MIT-licensed, separate from this repo's Commercial license covering the backend and MCP packages — see skill/LICENSE.

• skill/prompt-optimizer/SKILL.md — this platform's optimization methodology (context classification, sophistication assessment, optimization moves, parameter preservation). A weaker sibling to Cloud Pro and Local Core (no LLM-based optimization tier, no persistent history/quota/templates, no Bayesian tuning), positioned as the zero-account entry point.

• skill/context-cartographer/SKILL.md — assembles high-signal repository context before non-trivial implementation, debugging, or review work.

• skill/empirical-diagnostician/SKILL.md — forces evidence-based debugging: mandatory log extraction, a Fast-Track bypass for unambiguous single-token defects, a hypothesis matrix for anything more complex, and a Root-Cause Contract before any edit. Validated against a fixed behavioral benchmark (6/6 disposable-repo runs, independent pytest oracle, 1.0 on a live LLM-rubric fidelity check).

• skill/prompt-evaluation-engineer/SKILL.md — turns prompts into reproducible evaluation protocols: contracts, balanced test matrices, deterministic checks before semantic rubrics, evidence preservation, and regression-safe comparisons.

• skill/prompt-injection-guard/SKILL.md — detects, classifies, and responds to prompt-injection against an LLM application: input and output inspection, graded severity with a confidence factor, tiered response strategy, fail-mode and per-check latency budget, and outbound tool-argument hardening.

• skill/agent-prompt-architect/SKILL.md — architects the system prompt, context budget, and tool contract of an AI agent as one system: an agent contract, a cache-stable context budget, tool-contract engineering, explicit stop conditions split into prompt-side and harness-enforced, and evaluation-driven iteration.

• skill/prompt-complexity-triage/SKILL.md — decides how much to change a prompt before changing it: five consumer-anchored scoring dimensions plus a non-summed technical-density risk cap, fixed thresholds mapping to four tiers (leave as-is, light touch, structured rewrite, full rebuild), meaning-preservation guardrails with a mandatory pre-output preservation check, and a visible triage line every run so the tier decision is auditable.

• skill/subagent-dispatch-economics/SKILL.md — decides whether delegating work to a subagent is worth its cost: a four-question delegation test, a fork-vs-fresh-vs-inline shape selection with an explicit tiebreaker, a prompt-scoping contract for briefing zero-context fresh agents, wave-sizing rules for parallel dispatch, and a visible dispatch line every decision so the delegation call is auditable.

Related MCP server: MCP Prompt Optimizer

🚀 Quick Start

Step 1: Install the MCP Package

# Install the cloud-connected version (recommended)
npm install -g mcp-prompt-optimizer

Step 2: Get Your API Key

1. Visit promptoptimizer.xyz/pricing

2. Choose your tier (Free tier includes 20 optimizations/month, no credit card required).

3. API keys follow the format: sk-opt-*, sk-team-*, or sk-local-*.

Step 3: Configure Your MCP Client

Add to ~/.claude/claude_desktop_config.json (Claude Desktop):

{
  "mcpServers": {
    "prompt-optimizer": {
      "command": "npx",
      "args": ["mcp-prompt-optimizer"],
      "env": {
        "OPTIMIZER_API_KEY": "sk-opt-your-key-here"
      }
    }
  }
}

🧠 Intelligent Optimization Pipeline

Prompts are routed through a tiered system to ensure the highest quality based on your subscription and connectivity.

• Tier 1 — LLM Optimization (70–95% Confidence): Genuine rewriting and enrichment using advanced models (Gemini, Claude, and GPT families, configurable per your OpenRouter setup).

• Tier 2 — Backend Rules ( < 25% Confidence): Rapid rules-based pass for simple prompts or when personal models aren't configured.

• Tier 3 — Local Fallback (35–55% Confidence): Structured optimization applied locally if the backend is unreachable.

🤖 Context Engineer (CE) Suite

Available on Pro and Enterprise tiers.

Transform vague goals into production-ready agentic scaffolding directly in your IDE.

• generate_agent_sop: Generate structured Standard Operating Procedures for AI agents.

• generate_skill_package: Create a complete skill package (SOP + SKILL.md + reference + examples).

• transform_for_framework: Convert SOPs into native code for LangChain, AutoGen, or Claude Code.

🛠️ Available MCP Tools

🎨 AI Context Detection

Automatically applies specialized goals for:

• 💻 Code Generation: Technical accuracy, parameter preservation, precision.

• 🎨 Image Generation: Midjourney/DALL-E syntax, style boosters, camera settings.

• 📊 Structured Output: JSON/Schema integrity, YAML, CSV transformations.

• 💬 Human Communication: Tone adjustment, clarity, formal/informal shifts.

• 🔍 Research & Analysis: Context specificity, token efficiency, actionability.

🎛️ Personal Model Choice

Don't be locked into one model. Configure your own OpenRouter keys in the WebUI to pick from the current Claude, GPT, and Gemini model families — swap models per task without changing your integration.

💰 Subscription Plans

🔒 Security & Privacy

• Encrypted Transmission: All data is sent over TLS.

• Scoped Retention: Optimizations are saved to your own template library, encrypted at rest — never shared across users or used to train models.

• Local Option: Use mcp-prompt-optimizer-local for 100% on-device processing.

📞 Support & Resources

• Documentation: promptoptimizer.xyz/documentation

• Dashboard: promptoptimizer.xyz/dashboard

• Email: support@promptoptimizer.help

Transforming AI interactions through professional prompt engineering.