Token导航 LogoToken导航TokenDH.com
效率只读clawhub未标认证来源可访问clear审计通过

attribution-engine归因引擎

Agent Skill

attribution-engine 用于处理 GitHub 仓库、Issue、Pull Request 和代码协作信息,适合在 OpenClaw 中需要围绕仓库状态、代码变更或协作事项进行整理时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

68,141

周安装

2,927

GitHub Stars

1

下载量

23,884
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:attribution-engine(归因引擎)
来源仓库:https://github.com/otherpowers/attribution-engine
安装命令:
openclaw skills install attribution-engine
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install attribution-engine

简介

attribution-engine 帮助创作者清晰声明合作者、工具和合作伙伴的贡献归属。

  • 适用于开源项目、联合创作和技术生态透明度建设场景。
  • 减少披露遗漏,提升协作信任与合规水平。
  • 安装命令为 openclaw skills install attribution-engine,无外部依赖。
  • 输出模板需根据具体项目调整,不可直接套用通用格式。

SKILL.md

name
Attribution Engine
slug
attribution-engine
version
1.2
description
>-
metadata
creator
org
OtherPowers.co + MediaBlox
author
Katie Bush
clawdbot
skillKey
attribution-engine
tags
[attribution, transparency, provenance, creators, platforms]
safety
posture
organizational-utility-only
red_lines
runtime_constraints

Attribution Engine

1. What this skill does

Attribution Engine helps creators prepare clear, platform-aware credits and disclosures before publishing.

It focuses on clarity, consistency, and platform alignment, so your work travels cleanly across feeds, remixes, and reposts without unnecessary confusion later.

This skill does not tell you what you are legally required to do. It helps you organize and format information using each platform’s own rules.


2. Important note before we begin

Before using this skill, you will see a short notice:

This tool helps format attribution and disclosure information using publicly available platform guidance. It does not provide legal advice, determine compliance, or guarantee outcomes. You remain responsible for how and where content is published.

3. Why attribution matters in 2026

Attribution is no longer just courtesy.

Platforms now use attribution and disclosure signals to decide:

  • how content is labeled
  • how far it travels
  • whether it is limited, flagged, or reviewed

Small mismatches, like forgetting a native toggle or using the wrong AI label, can quietly reduce reach or trigger reviews.

This skill helps you catch those issues early.


4. Core concepts (plain language)

Attribution

Who should be credited publicly for the work.

Example:

  • Performer
  • Producer
  • Visual artist
  • Brand partner
  • Tool or system used

Disclosure

Whether viewers need to be told something important about how the content was made or funded.

Examples:

  • AI-assisted editing
  • Synthetic or altered media
  • Paid or gifted brand relationships

Provenance

How the content came into being.

Examples:

  • Fully human-authored
  • Human-authored with AI assistance
  • Fully AI-generated

5. Human vs AI labels (avoiding over-labeling)

Not all AI use is the same.

Over-labeling simple edits as “AI-generated” can cause platforms to treat your work as low-effort or mass-produced.

This skill helps distinguish between:

  • AI-Generated

Content created autonomously by a system with no meaningful human editorial control.

  • Human-Authored, AI-Assisted

Content where a person made the creative decisions and used tools for help such as cleanup, mastering, or compositing.

Example:

“Human-authored with AI-assisted mastering.”

This helps preserve trust without self-demotion.


6. Commercial relationships and brand credits

Hashtags alone are no longer enough.

If a post involves a material connection, such as:

  • sponsorship
  • gifted products
  • affiliate links
  • paid usage

most platforms expect you to use their native branded content tools.

This skill will:

  • flag when attribution suggests a commercial relationship
  • remind you to enable the platform’s built-in partnership or branded toggle

Example warning you may see:

This credit appears promotional. Make sure the platform’s native paid partnership setting is enabled before publishing.

7. Platform-aware formatting

Each platform treats attribution differently.

The Attribution Engine adapts output based on:

  • character limits
  • “read more” cutoffs
  • native labels and toggles
  • visible vs hidden metadata

Supported platforms include:

  • YouTube
  • TikTok
  • Instagram
  • Spotify
  • YouTube Music
  • SoundCloud
  • Tidal
  • Netflix
  • Amazon Music

You can also name any other platform. The skill will reference that platform’s current public documentation when available.


8. Metadata does not always survive uploads

Many platforms strip file metadata during upload.

To reduce loss:

  • the skill can generate a visible attribution string for captions or

descriptions

  • and a reference ID you can keep internally

Example visible string:

Ref OP-2026-ALPHA | Auth R. Mutt | Human-AI Collaborative

This helps attribution survive reposts and re-uploads.


9. Collaborators and consent clarity

Attribution records are not contracts.

Listing collaborators here:

  • does not define ownership
  • does not imply revenue splits
  • does not replace agreements

This skill treats attribution as documentation, not legal representation.


10. How this fits with other skills

Attribution Engine works best alongside:

  • Creator Rights Assistant

Organizes rights, licenses, and internal records at creation time.

  • Content ID Guide

Helps you understand and organize information when automated claims appear.

Together, they support a calmer, more predictable content lifecycle.


11. What this skill does not do

This skill does not:

  • validate licenses
  • determine ownership
  • predict platform actions
  • guarantee reach or safety
  • advise on how to bypass systems

It exists to reduce avoidable mistakes and save time.


12. Simple example

Input: Video with original music, light AI color correction, and a gifted product.

Output:

  • Suggested credit string for YouTube description
  • Reminder to enable branded content toggle
  • Human-authored, AI-assisted disclosure language
  • Platform-specific formatting notes

No guessing. No legal claims. Just clarity.


13. Summary

Attribution Engine helps creators explain their work clearly in the language platforms expect.

It reduces confusion, protects context, and supports transparency without over-labeling or over-promising.

Clean inputs lead to calmer outcomes.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

92.42%
按下载量换算22,074

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

通过

权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills