# Prompt Control Plane

> Use this tool when you need to optimize and control AI workflows by assessing prompt quality, estimating costs, and enforcing policies. It solves problems related to ambiguous or inefficient prompts, providing pre-flight analysis and smart compression to improve model performance. The Prompt Control Plane accepts AI workflow inputs and outputs optimized prompts, cost estimates, and policy compliance reports, making it ideal for use in AI development and deployment pipelines.

Canonical page: https://skillsregistry.net/skills/gh-rishiatlan-prompt-control-plane  
JSON: https://api.skillsregistry.net/v1/skills/gh-rishiatlan-prompt-control-plane

## Description

Provides prompt quality scoring, optimization, cost estimation, and policy enforcement for AI workflows. Includes pre-flight analysis, ambiguity detection, smart compression, model routing intelligence, and human-in-the-loop approval gates. Available as both a CLI tool and GitHub Action.

## Trust

- **Trust score (0–1):** 0.50
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** version-control
- **Updated:** 2026-04-25

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/gh-rishiatlan-prompt-control-plane)
- **Repository:** <https://github.com/rishi-banerjee1/prompt-control-plane>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "gh-rishiatlan-prompt-control-plane"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/gh-rishiatlan-prompt-control-plane` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/gh-rishiatlan-prompt-control-plane/pull`

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