Token导航 LogoToken导航

Agent Skill · 效率

attribution-engine(归因引擎)

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。

attribution-engine 是效率类 Agent Skill,可用宿主为 OpenClaw,通过ClawHub获取,权限为只读;许可证为 MIT-0,第三方扫描已通过,按安装量在效率类的 21,003 个 Skill 里排第 738。

总安装 68,141周安装 2,927GitHub Stars 1下载 23,884核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
MIT-0
版本
1.2.1

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

这个 Skill 是做什么的

效率类 Skill 把重复的整理、汇总、转换工作交给助手完成,减少手工复制粘贴。

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。

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 OpenClaw;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 openclaw skills install attribution-engine;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它标注为只读,先用只读任务试一次,确认行为符合预期再扩大使用范围。

常见问题

6 条

attribution-engine 怎么安装?

在终端执行 openclaw skills install attribution-engine。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

attribution-engine 支持哪些 AI 工具?

公开资料标注支持 OpenClaw。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

attribution-engine 需要什么权限?

权限级别是只读。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。

attribution-engine 是免费的吗?

来源标注的许可证是 MIT-0。本站不代收任何费用,也不托管安装包;能否商用、要不要署名,以许可证原文为准。

attribution-engine 用的人多吗?

公开数据是累计安装 68,141 次,近一周安装 2,927 次,下载 23,884 次,GitHub 1 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

attribution-engine 安全吗?

第三方扫描结果为通过,但这只覆盖已知的静态检查项,不能替代自己读一遍源码和安装命令。

值得选的地方和要先想清楚的地方

值得选的地方

  • 专注 OpenClaw 一种宿主,触发方式和使用习惯不用来回切换。
  • 许可证明确为 MIT-0,商用和分发边界好判断。
  • 第三方扫描已通过,常见的高风险行为没有命中。
  • 标注为只读权限,试用阶段不容易误改本地文件。

先想清楚的地方

  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 OpenClaw,想直接扩展它能力的人。
  • 需要处理效率类任务、又不想每次都手写提示词的人。
  • 需要许可证清楚、并且有第三方扫描结果的团队项目。

不太适合

  • 需要厂商级技术支持和 SLA 的生产系统。

效率类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
github

GitHub 代码协作 · OpenClaw

7,632,918545需要联网MIT-0通过
nano-pdf

Nano PDF 文档 · OpenClaw

4,249,476231需要联网MIT-0通过
auto-updater

自动更新程序 · OpenClaw

2,964,063386操作浏览器MIT-0通过
video-frames

视频帧 · OpenClaw

2,621,304112需要联网MIT-0通过
gemini

Gemini 助手 · OpenClaw

2,547,39648需要联网MIT-0有提醒
attribution-engine

归因引擎 · 本页

68,1411只读MIT-0通过

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

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.

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。目前只有一个公开来源,建议同时核对源仓库。

继续浏览同类 Skills