# ccontext

> Use this tool when you need to maintain persistent execution context across sessions for AI agents, solving problems of shared memory and task tracking in single or multi-agent workflows. It takes local YAML files as input and provides outputs for milestone management and context hygiene. Use ccontext in scenarios where agents require continuous learning, workflow management, or shared knowledge retention.

Canonical page: https://skillsregistry.net/skills/chesterra-ccontext  
JSON: https://api.skillsregistry.net/v1/skills/chesterra-ccontext

## Description

Provides AI agents with persistent execution context across sessions through local YAML files, enabling shared memory, task tracking, milestone management, and context hygiene for single or multi-agent workflows.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-05-03

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ybkvugfixp)
- **Repository:** <https://github.com/ChesterRa/ccontext>

## 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": "chesterra-ccontext"
    }
  }
}
```

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

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