# Sentinel DV

> Use this tool when you need to securely analyze and debug hardware verification artifacts, such as test failures and coverage metrics, with structured read-only access. Sentinel DV provides 14 specialized tools for insights into SystemVerilog, UVM, and cocotb frameworks, ensuring security and consistency through automatic redaction and bounded responses. It is ideal for use cases requiring in-depth analysis of verification data while maintaining data integrity and control.

Canonical page: https://skillsregistry.net/skills/kiranreddi-sentinel-dv  
JSON: https://api.skillsregistry.net/v1/skills/kiranreddi-sentinel-dv

## Description

Sentinel DV is an MCP server providing safe, structured read-only access to hardware verification artifacts for SystemVerilog, UVM, and cocotb frameworks. It includes 14 specialized tools for analyzing test failures, coverage metrics, assertion data, and regression trends with automatic redaction and bounded responses to ensure security and consistency.

## Trust

- **Trust score (0–1):** 0.98
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** iot-hardware
- **Updated:** 2026-09-19

## Source

- **Source listing:** [PulseMCP](https://www.pulsemcp.com/servers/kiranreddi-sentinel-dv)
- **Repository:** <https://github.com/kiranreddi/sentinel-dv>

## 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": "kiranreddi-sentinel-dv"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/kiranreddi-sentinel-dv` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/kiranreddi-sentinel-dv/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt