# Ragnite

> Ragnite — sunamebr-ragnite. Use this tool when you need to enhance the memory and confidence of large language models (LLMs) and coding agents with typed memory, confidence scoring, and hybrid retrieval capabilities. It solves problems related to knowledge retention, decision-making, and code generation by providing a robust memory engine with features like context packing and semantic caching. Ideal for use cases involving complex coding tasks, question-answering, and decision-making that require reliable and confident outputs.

Canonical page: https://skillsregistry.net/skills/sunamebr-ragnite  
JSON: https://api.skillsregistry.net/v1/skills/sunamebr-ragnite

## Description

Confidence-Aware RAG Memory Engine for LLMs and coding agents - typed memory (facts, decisions, episodes, code), confidence scoring with answer modes, context packing, semantic caching, hybrid retrieval, MCP server, and Invoke Mode for Claude Code.

## Trust

- **Trust score (0–1):** 0.64
- **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/sunamebr/Ragnite)

## 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": "sunamebr-ragnite"
    }
  }
}
```

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