# attribution-engine

> Use this tool when you need to accurately assign credits and acknowledge contributions to a project, helping to solve issues of intellectual property and transparency. The attribution engine takes in project details and collaborator information as inputs and generates properly formatted credits as outputs. It is particularly useful in contexts where multiple creators, tools, or sources are involved, such as research papers, art projects, or software development.

Canonical page: https://skillsregistry.net/skills/otherpowers-attribution-engine  
JSON: https://api.skillsregistry.net/v1/skills/otherpowers-attribution-engine

## Description

Helps creators clearly credit collaborators, tools.

## 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:** other
- **Updated:** 2026-09-19

## Source

- **Source listing:** [ClawHub](https://clawskills.sh/skills/otherpowers-attribution-engine)

## 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": "otherpowers-attribution-engine"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/otherpowers-attribution-engine` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/otherpowers-attribution-engine/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