Autonovel

Autonovel — pipeline orchestrator

This skill drives the full novel pipeline. It is a state machine over /state.json. Each step delegates to a specialised skill (drafting, evaluation, revision) or a Python script (mechanical work).

Inputs

The user invokes via /autonovel or /autonovel-resume . The ` resolves to = novels//` relative to the autonovel project root.

If the directory does not exist:

1. Create novels//
2. Initialise state: python scripts/state.py novels// init
3. Copy templates: cp templates/{voice.md,world.md,characters.md,outline.md,canon.md,MYSTERY.md} novels//
4. Ask the user for seed.txt content (or run the seed skill if they want suggestions)

AUTONOVEL_NOVEL_DIR=novels/ is exported for child Python scripts.

Phase 0 — Setup

1. Verify seed.txt exists and is specific. A good seed has:

• A world differentiator (one specific surreal/sensory thing)
• A central tension (personal + cosmic, in conflict)
• A cost/constraint on the magic
• A sensory hook

If it's vague, push back: "the seed reads like a back-cover blurb. What does breakfast smell like in this world?"

1. Verify state.json has phase: foundation.
2. Confirm with the user before starting. The pipeline is multi-hour.

Phase 1 — Foundation

LOOP until foundation_score > 7.5 AND lore_score > 7.0:
  1. If iteration == 0:
       - Run `voice-discovery` (writes voice.md Part 2)
       - Run `gen-world`
       - Run `gen-characters`
       - Run `gen-outline`
       - Ask user to fill `MYSTERY.md` (or generate it from outline if user prefers)
       - Run `gen-canon`
     Else (iterations 2+):
       - Identify weakest layer/dimension from prior eval
       - Re-run only the relevant generator with a focus instruction (e.g. "expand magic_system: add 3 societal implications, focus on cost trade-offs")
       - Re-run `gen-canon` if any of world / characters changed
  2. Run `evaluate-foundation`
  3. If `overall_score` improved over `state.foundation_score`:
       - Snapshot novel dir to `novels//.snapshots/foundation_iter_/`
       - Update `state.foundation_score` and `state.lore_score`
       - Append to `results.tsv` (use `scripts/results_log.py`)
     Else:
       - Restore the previous snapshot
       - Try a different focus
  4. Note the weakest dimension for the next iteration.
  5. Halt the loop after 15 iterations regardless of score (operator escape).

Exit: state.phase = "drafting".

Cross-layer consistency checks every iteration:

• Outline references only lore that exists in world.md
• Character abilities match magic system rules
• Foreshadowing ledger balances (every plant has a payoff)
• Voice exemplars exist and aren't generic
• Canon contains all hard facts from world / characters

Phase 2 — First draft

N = number of chapters in outline.md (parse the "### Ch N:" headers)
state.chapters_total = N

FOR ch in 1..N:
  FOR attempt in 1..5:
    1. Run `draft-chapter `
    2. Run `evaluate-chapter ` (which itself runs slop_scan first)
    3. If overall_score > 6.0:
         - Snapshot the chapter file to chapters/.history/ch_NN_v.md
         - Update state.chapters_drafted += 1
         - Append to results.tsv
         - Append new_canon_entries to canon.md
         - Break inner loop, move to next chapter
       Else:
         - If attempt  4,
    flag for revision phase
  - state.phase = "revision"

Watch for:

• Freshness decay after Ch 6. If chapters 7+ score consistently lower, push the writer to vary chapter openings and endings explicitly via the brief.
• Compounding tics. If the same AI tell appears in 3+ early chapters, hand-fix in voice.md Part 2 by adding it as an anti-exemplar BEFORE drafting more chapters.

Phase 3a — Automated revision

Cycle structure (3-6 cycles, plateau-detect to stop):

CYCLE C:
  Diagnosis:
    1. `adversarial-edit all` (writes edit_logs/chNN_cuts.json for each)
    2. `apply-cuts all --types OVER-EXPLAIN REDUNDANT` (or --min-fat 17 first cycle)
    3. `compare-chapters` (Swiss tournament, writes tournament_results.json)
    4. `voice-fingerprint` (writes voice_fingerprint.json) — flag outliers
    5. `reader-panel` (parallel 4 personas, writes reader_panel.json)

  Structural fixes (act on consensus):
    For each consensus item from reader_panel.json (3-of-4 or 4-of-4):
      a. `gen-brief --chapter ` with shape inferred from question type
         - cut_candidate → COMPRESSION
         - missing_scene → EXPANSION
         - thinnest_character → DEEPENING
         - worst_scene / momentum_loss → DRAMATISATION
      b. Snapshot chapter to chapters/.history/ before rewrite
      c. `gen-revision  briefs/chXX_.md`
      d. `evaluate-chapter `
      e. If score improved → keep. Else → restore snapshot.

  Targeted fixes (act on eval callouts):
    1. `evaluate-full` → top_suggestion + weakest_chapter
    2. `gen-brief --chapter ` (shape inferred from weakest_dimension)
    3. `gen-revision`, evaluate, keep/discard same as above

  Plateau detection:
    novel_score = result of evaluate-full
    If |novel_score - state.novel_score| _review.md and reviews/_parsed.json
  2. Read parsed.json. If parsed.stop == true: break and report.
  3. For each top unqualified item (priority: major > moderate > minor):
       a. `gen-brief --chapter ` (chapter inferred from item title/text)
       b. snapshot, `gen-revision`, evaluate, keep/discard
       c. If pattern-level (e.g. "X recurs across 4 chapters"), do a
          mechanical apply-cuts pass with a custom filter
  4. Re-run opus-review.

Items that persist across 3+ rounds → accept. They're structural to the
novel's voice/approach, not bugs.

Stopping conditions are computed by scripts/parse_review.py:

• ★★★★½ with 0 major items
• ≥4 stars and >50% of items qualified
• ≤2 items found

Phase 4 — Export (out of scope for the core build)

Out of scope per the user's instruction. If reached, just note it and stop.

Output to user during run

Print one block per phase transition. Inside a phase, print short progress lines per iteration / chapter:

[Phase 1, iter 4] focused: lore_interconnection
  evaluate-foundation: overall=7.6 lore=7.1 ← exit threshold met
  Phase 1 complete in 4 iterations. Snapshot saved to .snapshots/foundation_final.

[Phase 2] drafting 23 chapters
  ch 01: drafted 3147w → eval 7.2 ✓ keep
  ch 02: drafted 2980w → eval 5.4 ✗ retry
  ch 02: drafted 3204w → eval 6.4 ✓ keep
  ...

Don't quote-dump the full eval JSON in the conversation — write it to disk and print the one-line summary.

Recovery / resume

If interrupted:

• state.json records phase, current_focus, iteration, chapters_drafted, novel_score
• /autonovel-resume reads state and continues from the appropriate point
• All snapshots are in /.snapshots/ and per-chapter history in chapters/.history/

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: tchr-dev
• Source: tchr-dev/autonovel
• License: MIT

Install and usage instructions live in the source repository linked above.