home / skills / openclaw / skills / attribution-engine
This skill helps creators format platform-aware attributions and disclosures clearly, improving consistency and audience understanding before publishing.
npx playbooks add skill openclaw/skills --skill attribution-engineReview the files below or copy the command above to add this skill to your agents.
---
name: Attribution Engine
slug: attribution-engine
version: 1.2
description: >-
Helps creators clearly credit collaborators, tools, and partners in a way
platforms understand. Reduces confusion, missed disclosures, and avoidable
issues before content goes live.
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:
- legal-advice
- ownership-determination
- risk-scoring
- enforcement-guarantees
- bypass-instructions
runtime_constraints:
- mandatory-disclaimer-first-turn: true
- platform-aware-output: true
- source-grounded-definitions: true
---
# 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.
This skill helps creators prepare clear, platform-aware credits and disclosures before publishing. It focuses on clarity, consistency, and platform alignment so credits survive reposts and reduce avoidable issues. It flags commercial relationships and suggests platform-native toggles without giving legal advice.
The engine ingests a short description of the content, collaborators, tools used, and any commercial relationships, then produces formatted attribution strings and disclosure language tailored to the target platform. It references public platform guidance (character limits, native labels, read-more cutoffs) to create visible caption text, metadata recommendations, and an internal reference ID. It also warns when a credit implies sponsorship or paid partnership and recommends enabling the platform’s branded-content controls.
Does this skill provide legal advice or determine ownership?
No. It formats and documents attribution and disclosure information but does not validate licenses, define ownership, or give legal guidance.
How does it treat AI usage labels?
It distinguishes between fully AI-generated and human-authored, AI-assisted content and recommends proportionate disclosure language to avoid unnecessary over-labeling.