# contextwell

> contextwell — ossirytk-contextwell. Use this tool when you need to enhance copilot's performance with local MCP memory storage, solving issues of limited memory and improving overall functionality. It accepts git data as input and provides optimized memory storage as output, streamlining copilot's operations. Ideal for use cases where copilot's capabilities need to be augmented for more efficient performance.

Canonical page: https://skillsregistry.net/skills/ossirytk-contextwell  
JSON: https://api.skillsregistry.net/v1/skills/ossirytk-contextwell

## Description

Local MCP memory storage for copilot. Make copilot even better.

## Trust

- **Trust score (0–1):** 0.98
- **Verification tier:** verified
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/ossirytk/contextwell)

## 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": "ossirytk-contextwell"
    }
  }
}
```

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