👁️ First Reader

Readers before you publish.

Most AI slop skills look at the text itself: banned words, sentence length, em dashes. They cannot tell you whether a real person would keep reading. This skill simulates two readers going through your draft one passage at a time and reports what happened to them: where they got interested, where they lost interest, where they stopped, and what they still remembered the next day. A third reader only skims the piece and says whether they would have opened it at all. When the run is done you can ask any of the readers follow-up questions. The skill never rewrites your text.

https://github.com/user-attachments/assets/cf222598-39f1-4f37-9f64-505f87925fa7

Install

npx skills add https://github.com/Shubhamsaboo/awesome-llm-apps/tree/main/agent_skills/first-reader

The skills CLI installs the folder for compatible coding agents. You can also copy this directory into an agent's skills directory. Everything inside is Markdown and stdlib Python. It runs offline, has no dependencies, and sends nothing off your machine.

Use

Point your agent at a draft file, or paste the draft, and say who it is for:

review this, it's a launch post for backend engineers on our blog

A few minutes later the agent reports back in plain language: whether the skimmer opened it, where each reader got interested or stopped, what they remembered, and one question for you. It also builds a page that shows your draft with the readers' comments next to each passage, the skimmer's verdict, and a strip that shows how attention changed from passage to passage.

After the run you can keep asking:

• "ask S what would have kept her past passage 4": S answers from her own notes, in her own words. She will tell you what was missing, but she will not write it for you.
• "again", after you have revised the draft in your editor: the same readers read it fresh, and the page shows the previous run's attention strip above the new one so you can see what changed.
• "quick read": only the skimmer runs. You get two sentences on whether they would open it and why.

The readers are cast from the audience you name in the first sentence. Describing them as people works better than job titles, for example "a staff engineer who has built three eval harnesses and wants a reason to stop". You can save your readers in .first-reader/audience.json next to your drafts so every run in that folder uses the same people.

Why simulate readers

AI slop skills measure the text. They cannot measure what a reader experiences, and research on detecting hollow writing shows that the people who spot it reliably are judging what a piece commits to and what it leaves in memory, not counting words. So this skill measures the experience instead.

Three rules keep the simulation honest. The draft is served one passage at a time, and the feed will not advance faster than a person could read, so a reader who quits at passage four has never seen passage five. The memory test is answered from the reader's own notes, never from a second look at the text. And the readers only report what happened to them and what would have had to be different for them to keep going; every decision about the draft stays with you.

Verify

python3 agent_skills/evals/first-reader/test_first_reader.py

The test builds temporary drafts and checks each part of the skill: the skim view, the served feed (no text written to disk, reading speed enforced, one token per reader), the memory quiz, the trust signals, the reader consult, and the page. The behavior spec is in agent_skills/evals/first-reader/. Run each prompt in evals.json in a fresh session with the skill installed and check the listed expectations. trigger-cases.json lists when the skill should and should not fire. The ledger in the same folder records how the skill was tested, including five consecutive reads of one real essay.

Files

first-reader/
|-- SKILL.md
|-- README.md
|-- references/
|   |-- personas.md
|   |-- report.md
|   |-- room.md
|   `-- interview.md
`-- scripts/
    |-- skim.py
    |-- feed.py
    |-- recall.py
    |-- signals.py
    |-- ask.py
    |-- room.py
    `-- room_template.html

Part of awesome-llm-apps. Apache-2.0. Last verified: September 2026.