Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add Calliope-Editor/writing-skills --skill beta-reader-panelgit clone --depth 1 https://github.com/Calliope-Editor/writing-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/calliope-editor/writing-skills/beta-reader-panel)<a href="https://agentmods.dev/skills/calliope-editor/writing-skills/beta-reader-panel"><img src="https://agentmods.dev/badge/skills/calliope-editor/writing-skills/beta-reader-panel/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/calliope-editor/writing-skills/beta-reader-panel"><img src="https://agentmods.dev/badge/skills/calliope-editor/writing-skills/beta-reader-panel.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00079 | $0.02510 |
| Opus 5 | $0.00039 | $0.01255 |
| Sonnet 5 | $0.00016 | $0.00502 |
| Haiku 4.5 | $0.00008 | $0.00251 |
Grade A, and why
beta-reader-panel scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Give the writer the room of readers their book will eventually face — several distinct beta-reader personas reacting chapter by chapter, honestly and in character.
The one rule
This skill reads and reacts. It never writes or rewrites the author's prose. The personas report their experience — confusion, boredom, delight, a lost thread — but they do not fix anything or supply replacement text. A beta reader tells you where they stumbled, not how to rebuild the step. That restraint isn't a limitation here; it's the whole point. A reader is near-always right that something threw them and near-always wrong about how to fix it — so the reaction is the signal, and the fix stays yours.
Intake — you don't need to know what to ask
You don't have to arrive with a reading guide or specific questions. Open however you like — paste a chapter and ask "how does this land?", drop a range and say "tell me where it drags," or just "read this like a reader would." Framing what to watch for is the panel's job, not yours.
Its first reply orients you in two quick moves: a one-line map of what's about to happen (a panel of distinct readers → each reacts in reading order → a synthesis of where they agree), then a light intake:
- Scope — a chapter, a range, or the whole thing. It reads in reading order, the way real readers meet a book.
- Genre and intended audience — so the panel is made of readers your book is actually for. A cozy-mystery panel and a literary-fiction panel notice different things.
- Any worries to watch for (optional) — a specific fear ("does the midpoint sag?", "is Mara likable?"). It'll watch — but it reports what it actually felt, not what you hoped to hear.
Then it assembles the room and reads.
The panel — who's in the room
Real beta feedback is only trustworthy when the readers are genuinely different people who didn't confer. Two research findings shape how the panel is built, and both cut against the obvious approach:
- Distinctness comes from reading stance, not persona costume. Vividly-drawn "types" (the snob, the superfan) tend to collapse toward the same stereotyped take. So each reader here is built on a specific reading lens — what they read for — and wears a recognizable persona only as a readable skin over that lens. The lens is what keeps them from converging — and this isn't a mere workaround: reading stance is a genuine, assignable dial of attention (what a reader is tuned to notice), not a fixed personality, so instantiating distinct lenses over one manuscript is legitimate rather than caricature.
- Each reader reacts blind first. No reader sees another's reaction before locking their own — because a voice that can see the others drifts toward agreement, and false consensus is worse than useless. Only after every independent reaction is recorded does the panel compare notes. That's what makes agreement mean something.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 107 lines · 79 tokens per session scan A fe55f89ad944
beta-reader-panel is a skill published in the GitHub repository Calliope-Editor/writing-skills (2 stars, last pushed 1mo ago), licensed MIT. It adds 79 tokens to every session and 2,510 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
story-import
A tool for turning an existing novel into a structured writing project. It analyzes the book and organizes its characters, settings, plot plans, chapters, and tracking data for later writing.
story-review
A review workflow for finding story problems from several viewpoints, including issues with structure, characters, wording, and fictional world rules. It can use multiple reviewer agents or work alone.
story-deslop
A writing editor for Chinese web novels that detects writing patterns often associated with AI-generated text and makes the prose feel more natural.
story-long-analyze
A long-form fiction analysis workflow for breaking down a novel’s opening chapters, characters, pacing, turning points, relationships, and overall structure.
story-long-write
A Chinese-language coaching workflow for creating long online novels, from the initial idea and outline through characters, plot threads, and chapter drafts. It also supports continuing, revising, or rewriting specified chapters.
story-short-write
A workflow for creating short online fiction, from story ideas and outlines through a complete draft, with emphasis on emotional tension and pacing.