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Reader panel

Skill tchr-dev/autonovel/.claude/skills/revision/reader-panel

Autonomous fantasy-novel pipeline as Claude Code skills, agents, and slash commands

Install
npx -y skills add tchr-dev/autonovel --skill reader-panel

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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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_typePersona
panel-editorSenior fiction editor
panel-genre-readerAvid fantasy reader
panel-writerPublished novelist
panel-first-readerThoughtful general reader

To each, pass:

  • The full content of <novel-dir>/arc_summary.md inline 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:

  1. Extract chapter numbers each persona mentions (re.findall(r'Ch(?:apter)?\s*(\d+)', answer, re.IGNORECASE)).
  2. For each chapter mentioned by any persona, list which personas flagged it and which didn't.
  3. Consensus item = 3-of-4 or 4-of-4 agreement. These are revision priorities.
  4. 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_recommend and best_scene answer (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.

Keep looking

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