# coherra

> coherra — steve2009729-coherra. Use this tool when you need to ensure data consistency and accuracy in long-term memory storage. Coherra audits and repairs Sibyl Memory by detecting contradictions, duplicates, and stale facts, and provides a permanent audit trail as output. It solves data integrity problems, taking in potentially corrupted memory data as input and producing corrected and reliable information.

Canonical page: https://skillsregistry.net/skills/steve2009729-coherra  
JSON: https://api.skillsregistry.net/v1/skills/steve2009729-coherra

## Description

Enables agents to audit and repair their long-term memory in Sibyl Memory, detecting contradictions, duplicates, and stale facts, and fixing them with a permanent audit trail.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-08-28

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** other
- **Updated:** 2026-08-28

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/k0lmtwgrck)
- **Repository:** <https://github.com/Steve2009729/coherra>

## 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": "steve2009729-coherra"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/steve2009729-coherra` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/steve2009729-coherra/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