# dev-span

> dev-span — srmackey-dev-span. Use this tool when you need to provide AI coding agents with persistent project memory, capturing engineering context once and composing it per task. It solves problems of context switching and knowledge retention, enabling efficient coding workflows. The dev-span tool integrates with git and outputs composed project context, ideal for use with Cursor in local development environments.

Canonical page: https://skillsregistry.net/skills/srmackey-dev-span  
JSON: https://api.skillsregistry.net/v1/skills/srmackey-dev-span

## Description

DevSpan gives an AI client durable engineering context across repositories: components, repos, tasks, and governance, stored as markdown and composed per task.

## Trust

- **Trust score (0–1):** 0.53
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/srmackey/dev-span)

## 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": "srmackey-dev-span"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/srmackey-dev-span` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/srmackey-dev-span/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