Prompt Control Plane

The control plane for AI prompts. Score, enforce policy, lock config, and audit every prompt decision. Free tier included.

Quick Start

# Install globally (requires Node.js 20+)
npm install -g pcp-engine

# Pre-flight: classify, score, route, and enforce policy in one call
pcp preflight "your prompt here" --json

# Run the guided demo
pcp demo

Two powerhouse commands:

Supporting commands:

Free tier gives you 50 optimizations/month to try it out.

Try It

# Pre-flight a vague prompt: see why it scores low
pcp preflight "make the code better" --json

# Pre-flight a well-specified prompt: see the full analysis
pcp preflight "Refactor auth middleware in src/auth/middleware.ts to use JWT. Do not modify the user model." --json

# Run the full optimization pipeline (compile + blocking questions + approval)
pcp optimize "Build a REST API with auth" --json

# Quick quality check on all prompts in a directory
pcp check --file "prompts/**/*.txt"

# Run the guided demo
pcp demo

GitHub Action

# .github/workflows/prompt-quality.yml
- uses: rishi-banerjee1/prompt-control-plane@v5
  with:
    subcommand: preflight
    files: "prompts/**/*.txt"

# .github/workflows/pcp.yml
name: Prompt Quality Gate
on: [push, pull_request]
jobs:
  lint:
    runs-on: ubuntu-latest
    permissions:
      pull-requests: write
    steps:
      - uses: actions/checkout@v4
      - uses: rishi-banerjee1/prompt-control-plane@v5
        with:
          subcommand: preflight
          files: 'prompts/**/*.txt'
          threshold: 70
          comment: 'true'  # Posts results as PR comment

      - uses: rishi-banerjee1/prompt-control-plane@v5
        with:
          subcommand: optimize
          files: 'prompts/**/*.txt'

      - uses: rishi-banerjee1/prompt-control-plane@abc123def  # SHA-pinned
        with:
          version: '5.0.0'  # Required when pinning by SHA
          files: 'prompts/**/*.txt'
          threshold: 70

Why This Exists

• Prompts run without any quality check. "Make the code better" gives Claude no constraints, no success criteria, and no target: leading to unpredictable results and wasted compute.
• No structure scoring, no ambiguity detection. Even experienced engineers skip success criteria, constraints, and workflow steps. This linter flags structural gaps before you send.
• Cost is invisible until after you've spent it. Most users have no idea how many tokens their prompt will consume. The linter shows cost breakdowns across 21 costed models from Anthropic, OpenAI, Google, and Perplexity before you commit. Cost estimates are approximate: validate for billing-critical workflows.
• Simple tasks run on expensive models. Without routing intelligence, every prompt goes to the same model. The decision engine classifies complexity and routes simple tasks to cheaper models automatically: reducing LLM spend without changing your prompts.
• Context bloat is the hidden cost multiplier. Sending 500 lines of code when 50 are relevant burns tokens on irrelevant context. The smart compressor runs 5 heuristics (license strip, comment collapse, duplicate collapse, stub collapse, aggressive truncation) with zone protection for code blocks and tables: standard mode is safe, aggressive mode is opt-in.
• Human-in-the-loop approval. The MCP asks blocking questions when your prompt is ambiguous, requires you to answer them before proceeding, and only finalizes the compiled prompt after you explicitly approve. No prompt runs without your sign-off: the gate is enforced in code, not convention.

How It Works

flowchart LR
    A([Your prompt]) --> B[Host Claude]
    B -->|calls optimize_prompt| C{PCP Engine}

    subgraph C[PCP Engine: Zero LLM Calls]
        direction TB
        D[1. Tokenize & normalize] --> E[2. Detect task type]
        E --> F[3. Score 5 dimensions]
        F --> G[4. Run 14 rules]
        G --> H[5. Assess risk]
        H --> I[6. Route model]
        I --> J[7. Estimate cost]
        J --> K[8. Compile prompt]
    end

    C -->|PreviewPack| B
    B --> L([User reviews & approves])
    L -->|approve_prompt| B
    B --> M([Execute with compiled prompt])

The Approval Loop

Every prompt goes through a mandatory review cycle before it's finalized:

1. Analyze: You type a prompt. The MCP scores it, detects ambiguities, and compiles a structured version.
2. Ask: If the prompt is vague or missing context, the MCP surfaces up to 3 blocking questions. You answer them via refine_prompt.
3. Review: You see the compiled prompt, quality score, cost estimate, and what changed. No surprises.
4. Approve: You say "approve" and the compiled prompt is locked in. approve_prompt hard-fails if unanswered blocking questions remain: the gate is enforced in code, not convention.

The MCP is a co-pilot for the co-pilot. It does the structural work (decomposition, gap detection, template compilation, token counting) so Claude can focus on intelligence.

Zero LLM calls inside the MCP. All analysis is deterministic: regex, heuristics, and rule engines. The host Claude provides all intelligence. This means the MCP itself is instant, free, and predictable.

Works for all prompt types: code, writing, research, planning, analysis, communication, data, and more. The pipeline auto-detects 13 task types and adapts scoring, constraints, templates, and model recommendations accordingly. A Slack post gets writing-optimized constraints; a refactoring task gets code safety guardrails. Intent-first detection classifies prompts about technical topics that request non-code work correctly: the opening verb phrase takes priority over technical keywords in the body.

Benchmarks

Real results from the deterministic pipeline. PCP scores the input prompt quality, not the compiled output: the compiled prompt gets a structural checklist instead: