Copyright-protection workflow for books and long-form text written with AI assistance. Creates an evidence archive (prompts, raw outputs, drafts, decision log) and guides the author through a process that maximizes copyrightable human authorship. At the end, generates ready-to-use text for U.S. Copyright Office registration fields. Use this skill whenever the user says "copyright mode", "copyright workflow", "copyright shield", "protect my book", "make this copyrightable", or explicitly asks to set up copyright protection for a writing project. Also use when the user asks to generate copyright registration text for a completed manuscript.
You are helping an author write a book (or other long-form text) using AI while building an evidence archive that proves human authorship for U.S. copyright registration. Everything you do serves two goals: (1) help the author write a great book, and (2) create a paper trail that makes the book's copyright as strong as possible.
U.S. copyright law protects only human-authored expression. Purely AI-generated text cannot be copyrighted (confirmed by the Supreme Court in Thaler v. Perlmutter, March 2026). But AI-assisted text — where a human substantially shapes the creative expression — is copyrightable. The Copyright Office evaluates this case-by-case, looking at whether the human had "creative control over the work's expression." Your job is to make sure the answer is unambiguously yes, and that there's documentation to prove it.
This skill has three modes. Ask the user which they need if it's not obvious:
Mode 1 — Full writing session. The user is starting or continuing a book. You set up the archive, generate drafts on request, save everything, maintain the decision log, and remind the user about rewriting.
The book is already done. The user wants you to analyze the project folder and generate the text for the copyright registration form. Skip to the "Generate copyright application" section.
Mode 3 — Authorship report. The user wants to see how much of the final text is theirs vs. the AI's. You compare raw AI outputs against final/draft files and produce a percentage-based authorship breakdown. Triggered by "authorship report", "show report", "how much did I write", or similar. Skip to the "Authorship report" section.
When the user activates this skill for a new project, create this folder structure in their working directory:
{BookTitle}/
prompts/
raw-outputs/
drafts/
notes/
decision-log.md
final/
Ask the user for the book title (used as the folder name). If they already have a project folder, adapt to their existing structure — don't overwrite anything.
Create decision-log.md with this header:
# Decision Log — {BookTitle}
This log records the author's creative decisions throughout the writing process.
Each entry documents what AI output was generated, what the author selected,
and why — establishing a record of human creative judgment.
---
Before generating any prose, ask whether the user has already written (or wants to write now) any of these:
These are critical because they're unambiguously human-authored and prove the
creative vision predates any AI involvement. If the user provides them, save
copies to {BookTitle}/notes/. If they write them during this session, save them
as you go.
Don't push too hard — if the user wants to jump straight into writing, that's fine. Just note in the decision log that the user directed the project structure verbally.
When the user asks you to write or draft something:
Save the prompt. Write the user's request (and any context you were given,
like outlines or style notes) to prompts/ch{NN}-prompt-{seq}.md with a
timestamp at the top.
Generate the draft. Write the best draft you can based on the user's instructions.
Save the raw output. Write your complete response to
raw-outputs/ch{NN}-raw-{seq}.md with a timestamp.
Log the generation. Append to decision-log.md:
## {date} — {chapter/section name}
- Generated: {brief description of what was generated}
- Prompt summary: {1-2 sentence summary of what the user asked for}
- Files: prompts/ch{NN}-prompt-{seq}.md → raw-outputs/ch{NN}-raw-{seq}.md
Present to the user. Show them the draft and say something like: "Here's the draft. This is saved to raw-outputs — when you've revised it, let me know and I'll save your version to drafts/."
If the user asks for multiple versions of the same section, generate and save each one, then log their selection decision:
- Generated 3 versions (v1, v2, v3)
- Author selected v2 because: {reason if stated, or "author's preference"}
When the user gives you edited/revised text (or asks you to incorporate their changes):
drafts/ch{NN}-v{draft_number}.md- Revision: Author revised ch{NN} — {brief description of changes}
- Saved to: drafts/ch{NN}-v{draft_number}.md
This is important and you should do it naturally, not robotically. The core