# tnl

> tnl — janaraj-tnl. Use this tool when you need to create, manage, and store structured English contracts for AI coding agents, streamlining collaboration and version control through integration with git. It solves problems of contract consistency and transparency by allowing agents to propose contracts, which are then approved by users and saved for future reference. The tool takes in proposed contracts as input and outputs approved, stored agreements, facilitating seamless communication between humans and AI agents.

Canonical page: https://skillsregistry.net/skills/janaraj-tnl  
JSON: https://api.skillsregistry.net/v1/skills/janaraj-tnl

## Description

Structured English contracts for AI coding agents — proposed by the agent, approved by you, saved on disk, read by every future session.

## Trust

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

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** other
- **Updated:** 2026-09-21

## Source

- **Source listing:** [GitHub](https://github.com/janaraj/tnl)

## 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": "janaraj-tnl"
    }
  }
}
```

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