# kenning-encounter

> kenning-encounter — kenningai-kenning-encounter. Use this tool when you need to enhance the working memory of LLM agents with structured and persistent storage, solving problems of knowledge retention and recall in complex conversations and tasks. It provides a robust interface for storing and retrieving information, leveraging Neo4j's graph database capabilities. Ideal for use cases requiring agents to maintain context and learn from interactions over time.

Canonical page: https://skillsregistry.net/skills/kenningai-kenning-encounter  
JSON: https://api.skillsregistry.net/v1/skills/kenningai-kenning-encounter

## Description

Structured, persistent working memory for LLM agents, backed by Neo4j.

## Trust

- **Trust score (0–1):** 0.68
- **Verification tier:** scanned
- **Last scanned:** 2026-09-28

## Facts

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

## Source

- **Source listing:** [GitHub](https://github.com/kenningai/kenning-encounter)

## 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": "kenningai-kenning-encounter"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/kenningai-kenning-encounter` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/kenningai-kenning-encounter/pull`

---
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