Readers before you publish.
Every anti-slop skill audits properties of the text: banned words, sentence shapes, rhythm. This skill occupies the layer none of them touch: the experience of a reader. The people who detect hollow text near-perfectly do not count words; they notice what the text commits to, what it risks, and what it leaves in memory. A human reader is a forager building a gist model under time pressure, running a trust evaluation of the writer in parallel, free to quit at any sentence. This skill reproduces that reader and reports what happened to them.
It produces a reading, not an audit. Run it as the final gate before publishing.
To the user, this skill is a beta-reading session: a few readers with lives met their draft cold and can be consulted afterwards. Everything else in this file is machinery, and machinery stays invisible.
scripts/ask.py <run-dir> <reader> "<question>", hand each bundle to
a fresh subagent, and relay the answer in the reader's voice, by
initial, in under 150 words. Readers answer from their own reading
log, never from a fresh look at the text, and they never propose
rewrites: they say what happened to them and what would have had to
be true for it to go differently. If the log cannot support an
answer, the reader says so.references/interview.md). Never write their experience for them.You see whole documents at once, forget nothing, and never get bored. A human reader has none of those powers, and pretending to read while holding the full text is performance, not measurement. So:
Do not open the draft. From the moment this skill is invoked until the timed reads are complete, never read the draft file or let the user paste it. Only the scripts touch it. If the draft is already in your context (the user pasted it earlier), you are contaminated as a reader: run every reading step through fresh subagents and say so in the report. Your own full-text read happens once, at step 6, after all reader experiments are done.
All scripts are stdlib Python, offline, no dependencies:
python3 scripts/<name>.py. Keep every run's files in a .first-reader/
directory next to the draft, never in a session scratch directory:
scratch gets wiped between sessions, and "again" in a tomorrow session
needs the prior run to compare against. If the draft lives in a git
repo, mention once that .first-reader/ is worth gitignoring.
First decide whether a timed read even models this text's real encounter:
Work silently per the user contract: one line at the start, at most one mid-run message for a dramatic event, then the result. In anything the user does see, plain words only.
Read references/personas.md. If the audience or venue is unknown, ask
the user one question: who is this for and where will it be published?
Then cast two personas, sympathetic and skeptical, each with priors,
situation, patience budget from the table, and a one-sentence stake. If
you cannot write the stake sentence, report that first.
Also ask, if not obvious: what should the reader think, feel, or do after reading? That is the intended gist the recall test will be judged against.
Fold the audience into the one starting line only when it needs
confirming ("Reading it as engineers on HN would"); otherwise just
start. Before casting, look for .first-reader/audience.json beside the
draft (format in references/room.md): it holds the readers this
author already chose, with their initials and one-line lives, so the
same two people read every draft in the project and "S" means the same
person next month. Reuse it unless the user names a different audience;
write it after the first cast. Write manifest.json in the run
directory per references/room.md for your own use. On a repeat review, reuse the
prior manifest's casts and point previous_run at the old run so the
results can be compared honestly.
Have a fresh subagent playing the skeptical persona run
scripts/skim.py <draft> itself and answer from that view alone; if you
must run the script, redirect its output straight to a file you never
open, because the scanner view quotes fragments of the draft and reading
it contaminates you before the timed reads. It answers: what is this
piece, do you commit to a full read, and what single element decided it.
A scanner who cannot say what the piece is, or declines to commit, is the first finding. For most real readers this gate is the whole encounter.
No lookahead is enforced by mechanism, not by asking nicely. Start the feed in the background BEFORE dispatching any reader:
python3 scripts/feed.py serve <draft> --run <run-dir> --readers keen,skeptic \
--persona "keen=<one line>" --persona "skeptic=<one line>" \
--ready-file <run-dir>/feed.json
The text now lives only in that process's memory. feed.json holds
addresses, nothing else; read it and give each reader ONLY its own
line. A reader's dispatch prompt contains the persona, export READER_FEED=<reader-address>, and three commands; it never
contains the draft path, the run directory, or the other reader's
address. The reader runs feed.py start, then reads one passage at a
time, logging honestly at each step with
feed.py next --log "needle=<-2..+2> expected... got... <felt notes>".
The needle is the felt reaction: +2 leaning in, 0 neutral, -1 drifting
or doubting, -2 done. Log skimming the moment it starts. When the
persona's patience runs out, quit with
feed.py quit --log "needle=-2 <why the persona stopped>", because
quitting is the single most informative thing a reader does. A reader
who finishes still records one final line the same way; the transcript
labels it FINAL rather than QUIT.
The feed releases the next passage only after a real log lands, never
re-serves anything but the current passage, refuses to advance faster
than a person could read the passage, and writes nothing of the piece
to disk until every reader is finished (feed.py progress --admin <admin-address> shows where they are; feed.py close --admin ...
flushes early if a reader dies). The reader subagent returns nothing
of substance; the artifact is the session. Collect it with
feed.py transcript <run-dir>/<reader> after close.
If subagents are unavailable, use file mode (feed.py start <draft> --session <dir>) yourself under the same rules, before ever opening
the draft, and disclose that one mind played both reader and reviewer
(see Adapting to your environment).
Run scripts/recall.py <session-dir> for the sympathetic persona's
session (and the skeptic's if it finished). Hand the output to a fresh
subagent with no other context. It answers the quiz from the transcript
alone: sayback, pointing, peak, ending, center of gravity, one action.
Judge the answers against the intended gist from step 1. A piece whose one-sentence retelling does not match its thesis, or whose ending nobody remembers, has a finding no line edit can fix. "Nothing survived" is a finding about the piece, not about the reader.
Run scripts/signals.py <draft> --json, but only after the timed reads
are complete: its output quotes draft sentences, so reading it earlier
contaminates you. Read the numbers through the genre: costly signals (checkable numbers, named entities, quotes,
admissions against interest, first-person experience) buy trust; free
signals (hedges everywhere, certainty everywhere, portable sentences that
fit any document) buy nothing. Near-zero variance in epistemic commitment
reads as machine confidence. The counters are floors, not truth: the
admissions detector in particular under-counts (it pattern-matches stock
phrasings and misses lines like "our test data was a fantasy"), so
confirm costly signals in your own step 6 read rather than trusting a
zero.
Only now read the draft in full, once, for two judgments the scripts cannot make: the implied author (describe the person these sentences imply, and quote any seam where that person changes mid-document) and whether the transcripts' complaints are the piece's fault or the cast's.
Write the report exactly as references/report.md specifies: sayback and
meaning first, then the reading transcripts, what survived, the person
behind it, then neutral questions and opinions by permission. Testimony
register throughout, no scores, altitude rule enforced, and the report is
allowed to be happy.
Save the skim gate's answer as <run-dir>/skim.txt and put its verdict
in the annotations (skim). Build the page per references/room.md
(scripts/room.py): the draft with the readers' comments beside each
passage, the skimmer's verdict, the lenses, the strip; no fixes. Publish
it where you can, or write it beside the run and open it. Deliver per
the user contract: the friend's report, then the link. Keep the
Lerman-order report on disk beside the run for anyone who asks.
The readers persist in the run directory. Every later "ask" in the
session goes through scripts/ask.py and a fresh subagent per reader;
never answer for a reader yourself, and never let a reader see the
draft again: their memory is their log.
This skill runs in any SKILL.md-compatible coding agent, but three capabilities vary. Check what you have BEFORE the run starts, adapt silently, and tell the user only what changes for them, in one line.
Subagents. If you can spawn fresh agents (Claude Code's agent tool, or any delegation mechanism), each reader is one. If you cannot, you play the readers yourself, one persona at a time, and the order of operations becomes everything: do NOT read or open the draft file for any reason before the reads; start immediately with the feed script and meet the piece one passage at a time in persona (file mode; served mode is pointless when reader and reviewer share a mind); do the skeptic's read in a later, separate pass from the sympathetic one; run recall by answering the quiz from the transcript alone before ever seeing the full text. This is a weaker experiment than fresh minds, so say so once in the verdict ("I played both readers myself here, so treat the findings as slightly softer") and never pretend otherwise. If the draft is already in your context (you read it earlier in the session, it was pasted, or your harness auto-loaded it), solo mode cannot blind you; do the reads anyway as honest role-passes, and weight the mechanical signals (quit logic, recall-from-log, signals.py) more heavily than the needle.
A place for the page. With artifact publishing (Claude Code on claude.ai), the page is a private link. Without it, it is a local HTML file next to the run, opened for the user. The page is read, not clicked; questions to the readers come to you in chat.
Turn budget. In agents where every script call is a visible tool step, the full run is many steps. Say so up front in the starting line ("this takes a few minutes and a lot of small steps") and never ask permission step by step; batch where your harness allows.
The first time the skill runs in a session, the starting line also teaches the interface in passing, because nothing else will: "Reading it as your audience would, about 5 minutes. When I'm back: where each reader leaned in or left, what stuck with them, and a page with their comments beside your draft. You can ask them follow-ups, and 'again' re-reads after you revise."