# moss

> moss — coderomaster-moss. Use this tool when you need to harness the power of semantic search for precise information retrieval, solving problems such as finding relevant data, answering complex queries, and navigating large knowledge bases. Moss semantic search takes in natural language queries as input and outputs relevant results, making it an ideal solution for applications requiring intelligent information discovery. It is particularly useful in contexts where traditional search methods fall short, such as researching niche topics or identifying subtle relationships between concepts.

Canonical page: https://skillsregistry.net/skills/coderomaster-moss  
JSON: https://api.skillsregistry.net/v1/skills/coderomaster-moss

## Description

Documentation and capabilities reference for Moss semantic search.

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-05-22

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** search
- **Updated:** 2026-09-13

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/coderomaster-moss)

## 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": "coderomaster-moss"
    }
  }
}
```

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