# jevcore

> jevcore — perrylink-jevcore. Use this tool when you need to create typed judgments instead of prose for DeepSeek Harness, Model Context Protocol, or plain Node applications. It solves problems related to offline data processing and type safety, providing a reliable interface for git-based projects. By utilizing jevcore, developers can ensure accurate and efficient data handling with typed outputs.

Canonical page: https://skillsregistry.net/skills/perrylink-jevcore  
JSON: https://api.skillsregistry.net/v1/skills/perrylink-jevcore

## Description

TypeSafe Jev for DeepSeek Harness, the Model Context Protocol, and plain Node: typed judgments instead of prose, offline by default.

## Trust

- **Trust score (0–1):** 0.95
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **License:** Apache-2.0
- **Updated:** 2026-09-28

## Source

- **Source listing:** [GitHub](https://github.com/PerryLink/jevcore)

## 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": "perrylink-jevcore"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/perrylink-jevcore` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/perrylink-jevcore/pull`

---
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