# Autonovel > Top-level orchestrator for the autonomous novel pipeline. Drives Phase 1 (foundation), Phase 2 (drafting), Phase 3a (automated revision), and Phase 3b (Opus review loop) by delegating to the seed/gen-world/gen-characters/gen-outline/gen-canon/voice-discovery/draft-chapter/evaluate-*/adversarial-edit/apply-cuts/reader-panel/gen-brief/gen-revision/opus-review skills. State persists in /s… - **Type:** Skill - **Install:** `agentstack add skill-tchr-dev-autonovel-autonovel` - **Verified:** Yes — security-reviewed for prompt injection and unsafe behavior - **Seller:** [tchr-dev](https://agentstack.voostack.com/s/tchr-dev) - **Installs:** 0 - **Category:** [AI & ML](https://agentstack.voostack.com/c/ai-and-ml) - **Latest version:** 0.1.0 - **License:** MIT - **Upstream author:** [tchr-dev](https://github.com/tchr-dev) - **Source:** https://github.com/tchr-dev/autonovel/tree/main/.claude/skills/autonovel ## Install ```sh agentstack add skill-tchr-dev-autonovel-autonovel ``` Requires the [AgentStack CLI](https://agentstack.voostack.com/docs/cli). Works with Claude Code, Cursor, and any MCP-compatible agent. ## About # 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?" 2. Verify `state.json` has `phase: foundation`. 3. 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](https://github.com/tchr-dev) - **Source:** [tchr-dev/autonovel](https://github.com/tchr-dev/autonovel) - **License:** MIT Install and usage instructions live in the source repository linked above. ## Pricing - **Free** — Free ## Security capabilities Automated source analysis of v0.1.0 — what this tool can access: - **Network access:** no - **Filesystem access:** no - **Shell / process execution:** no - **Environment & secrets:** no - **Dynamic code execution:** no *"Yes" means the capability is present in the source — more access means more to trust, not that it is unsafe.* ## Versions - **0.1.0** — security scan: passed — Imported from the upstream source. ## Links - Listing page: https://agentstack.voostack.com/l/skill-tchr-dev-autonovel-autonovel - Seller: https://agentstack.voostack.com/s/tchr-dev - Browse the marketplace: https://agentstack.voostack.com/browse --- Listed on AgentStack — the marketplace for AI agent skills and MCP servers. 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