Reader panel
Skill tchr-dev/autonovel/.claude/skills/revision/reader-panel
Autonomous fantasy-novel pipeline as Claude Code skills, agents, and slash commands
npx -y skills add tchr-dev/autonovel --skill reader-panelAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
One thing to look at
- 1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
What its author says it does
Copied from the file, not written here
Run a 4-persona reader panel (editor, genre reader, writer, first reader) on the full novel. Spawns four subagents in parallel, each evaluating independently, then merges results into consensus + disagreement findings. Use during revision cycles to surface novel-level issues that per-chapter eval misses.
SKILL.md
3.1 KB, as published. Nobody here has run it
Reader panel
A reader panel evaluates the novel AS A WHOLE, not chapter by chapter. The disagreements between readers are where editorial decisions live.
Procedure
Step 1 — build the arc summary
If <novel-dir>/arc_summary.md does not already exist (or is stale), generate it. The arc summary is what you hand to each persona — they don't read 80k words.
For each chapter, write:
## Ch N — [title]
OPENING (~150 words, lightly trimmed from the actual chapter open)
...
KEY BEATS:
- beat 1
- beat 2
- beat 3
KEY DIALOGUE (1-2 lines that capture the chapter's tone)
CLOSING (~100 words from the chapter's actual ending)
Concatenate all chapters into <novel-dir>/arc_summary.md. Total target ~12,000-18,000 words for a 75k-word novel.
Step 2 — spawn four subagents in parallel
Use the Agent tool with one tool-use block containing four parallel calls:
| subagent_type | Persona |
|---|---|
panel-editor | Senior fiction editor |
panel-genre-reader | Avid fantasy reader |
panel-writer | Published novelist |
panel-first-reader | Thoughtful general reader |
To each, pass:
- The full content of
<novel-dir>/arc_summary.mdinline in the prompt - A note that the novel is N chapters / W words
- The JSON schema (in each agent's instructions, but reiterate it: the 10 questions)
Each agent returns JSON with the 10 questions answered.
Step 3 — merge
Save each persona's JSON to <novel-dir>/edit_logs/reader_panel.json under readers.<persona_key>.
For consensus detection across these questions: momentum_loss, cut_candidate, thinnest_character, worst_scene:
- Extract chapter numbers each persona mentions (
re.findall(r'Ch(?:apter)?\s*(\d+)', answer, re.IGNORECASE)). - For each chapter mentioned by any persona, list which personas flagged it and which didn't.
- Consensus item = 3-of-4 or 4-of-4 agreement. These are revision priorities.
- Disagreement item = 1-of-4 or 2-of-4. These are editorial calls — flag them but don't auto-act.
Write <novel-dir>/edit_logs/reader_panel.json:
{
"readers": {
"editor": { /* the editor's JSON */ },
"genre_reader": { /* ... */ },
"writer": { /* ... */ },
"first_reader": { /* ... */ }
},
"consensus_items": [
{"question": "cut_candidate", "chapter": 14, "agreement": "4/4", "details": {...}}
],
"disagreements": [
{"question": "thinnest_character", "chapter": 8, "flagged_by": ["editor"], "not_flagged": ["genre_reader", "writer", "first_reader"]}
],
"timestamp": "..."
}
Output to user
Print:
- Each persona's
would_recommendandbest_sceneanswer (one line each) - All consensus items with the question and chapter
- All disagreements with the split
Suggest: pass each consensus item to gen-brief to generate a revision brief, then gen-revision.