# semfind

> semfind — paperboardofficial-semfind. Use this tool when you need to search and retrieve relevant information from local text files based on semantic meaning, rather than exact keyword matches. Semfind solves problems of information discovery and retrieval by using embeddings to understand the context and intent behind search queries. It takes in natural language search queries as input and returns relevant text files as output, making it ideal for use cases where traditional keyword-based search falls short.

Canonical page: https://skillsregistry.net/skills/paperboardofficial-semfind  
JSON: https://api.skillsregistry.net/v1/skills/paperboardofficial-semfind

## Description

Semantic search over local text files using embeddings.

## Trust

- **Trust score (0–1):** 1.00
- **Verification tier:** scanned
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** instructions
- **Runtime environment:** llm
- **Category:** ai-ml
- **Updated:** 2026-09-25

## Source

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

## 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": "paperboardofficial-semfind"
    }
  }
}
```

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