294 lines
10 KiB
Python
294 lines
10 KiB
Python
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"""
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Fanfic Writer v2.1 - OpenClaw Skill Entry Point
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This function is called by OpenClaw to run the skill
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"""
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import json
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from pathlib import Path
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from typing import Dict, Any, Optional
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# Import all modules
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from .workspace import WorkspaceManager
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from .phase_runner import PhaseRunner
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from .writing_loop import WritingLoop
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from .safety_mechanisms import FinalIntegration, BackpatchManager
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from .resume_manager import RunLock, ResumeManager, RuntimeConfigManager
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from .price_table import PriceTableManager, CostBudgetManager
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from .config_manager import ConfigManager
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from .state_manager import StateManager
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from .atomic_io import atomic_write_json, atomic_write_text
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from .utils import get_timestamp_iso
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def run_skill(
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# Book configuration
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book_config: Optional[Dict[str, Any]] = None,
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book_title: Optional[str] = None,
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genre: Optional[str] = None,
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target_words: int = 100000,
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# Writing mode - "manual" requires confirmation at each phase
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mode: str = "manual", # "manual" or "auto"
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# Paths
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workspace_root: Optional[str] = None,
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run_dir: Optional[str] = None,
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# Resume
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resume: str = "off", # "off", "auto", "force"
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# Budget
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budget: Optional[float] = None,
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# Chapters to write
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chapters: Optional[str] = None, # "1" or "1-5"
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# OpenClaw context - provides the model automatically!
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oc_context: Optional[Dict[str, Any]] = None,
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**kwargs
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) -> Dict[str, Any]:
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"""
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Main entry point for OpenClaw to call this skill.
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This function handles the complete workflow:
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- Phase 1-5: Initialization (with human confirmation required)
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- Phase 6: Writing loop (with human confirmation between chapters)
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- Phase 7-9: Backpatch and finalization
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Args:
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book_config: Complete book configuration dict
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book_title: Book title
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genre: Genre (都市/玄幻/仙侠/etc)
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target_words: Target word count
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mode: "manual" or "auto"
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model: Model to use
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api_key: API key for model
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workspace_root: Where to store novels
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run_dir: Specific run directory (for resume)
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resume: "off", "auto", "force"
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budget: Cost budget in RMB
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chapters: Chapter range "1-10" or single "5"
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oc_context: OpenClaw context (provides model calling)
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Returns:
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Dict with status and paths
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"""
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results = {
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"status": "pending",
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"phase": None,
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"message": "",
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"run_dir": None,
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"chapters_written": [],
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"errors": []
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}
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# Get workspace root
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if workspace_root:
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base_dir = Path(workspace_root)
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else:
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base_dir = Path.home() / ".openclaw" / "novels"
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# Determine mode
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# In manual mode, require human confirmation at each phase
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# In auto mode, run automatically but still save for review
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try:
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# Initialize workspace
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workspace = WorkspaceManager(base_dir)
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# Handle resume
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if resume != "off" and run_dir:
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# Resume existing run
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run_dir = Path(run_dir)
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resume_mgr = ResumeManager(run_dir)
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can_resume, reason, resume_info = resume_mgr.can_resume(mode=resume)
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if can_resume:
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resume_mgr.resume(resume_info)
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results["message"] = f"Resumed at chapter {resume_info['resume_point']['chapter']}"
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elif resume == "force":
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results["message"] = f"Force resume: {reason}"
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else:
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results["message"] = f"Cannot resume: {reason}, starting new run"
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# Get model callable from OpenClaw context
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# The model is whatever OpenClaw is currently using - no hardcoding!
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def call_model(prompt: str, **model_kwargs) -> str:
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"""
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Call model via OpenClaw context.
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IMPORTANT: This uses OpenClaw's current model automatically.
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No model configuration needed in the skill itself.
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"""
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if oc_context and hasattr(oc_context, 'model_call'):
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# OpenClaw provides model_call method
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return oc_context.model_call(prompt, **model_kwargs)
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elif oc_context and 'model_callable' in oc_context:
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# Or as a callable in context
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return oc_context['model_callable'](prompt, **model_kwargs)
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elif oc_context and 'generate' in oc_context:
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# Alternative method name
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return oc_context['generate'](prompt, **model_kwargs)
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else:
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# Fallback: use prompt as-is (for debugging)
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return f"[Please configure model in OpenClaw - prompt length: {len(prompt)} chars]"
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# If no book config, we need to create one
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if not book_config and not book_title:
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results["status"] = "awaiting_config"
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results["message"] = "Please provide book_title and genre"
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return results
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# Create book config if not provided
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# Note: model is provided by OpenClaw automatically, not hardcoded in skill
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if not book_config:
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book_config = {
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"version": "2.1.0",
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"book": {
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"title": book_title,
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"genre": genre,
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"target_word_count": target_words,
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},
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"generation": {
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"mode": mode
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}
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}
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# Phase 1-5: Initialization (always require human confirmation in design)
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# For OpenClaw, we check if mode is "auto" or "manual"
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# In manual mode, we return the generated files for review
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if not run_dir:
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# Create new book
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runner = PhaseRunner(workspace)
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# Phase 1: Initialize
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results["phase"] = "1_init"
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run_path, book_uid, run_id = runner.phase1_initialize(
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book_title=book_config.get("book", {}).get("title", "未命名"),
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genre=book_config.get("book", {}).get("genre", "都市"),
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target_words=book_config.get("book", {}).get("target_word_count", 100000),
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mode=mode
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)
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results["run_dir"] = str(run_path)
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# Phase 2-5 would be here but require human confirmation
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# For now, return to get confirmation
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results["status"] = "awaiting_confirmation"
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results["message"] = f"Phase 1 complete. Please confirm to continue to Phase 2."
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return results
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# If we have run_dir, continue with writing
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run_dir = Path(run_dir)
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# Acquire lock
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run_lock = RunLock(run_dir)
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lock_success, lock_error = run_lock.acquire(mode=mode)
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if not lock_success:
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results["status"] = "error"
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results["errors"].append(f"Cannot acquire lock: {lock_error}")
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return results
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try:
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# Get current state
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state_path = run_dir / "4-state" / "4-writing-state.json"
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with open(state_path, 'r', encoding='utf-8') as f:
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state = json.load(f)
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current_chapter = state.get('current_chapter', 0)
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# Determine chapters to write
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if chapters:
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if '-' in chapters:
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start, end = map(int, chapters.split('-'))
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chapter_list = list(range(start, end + 1))
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else:
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chapter_list = [int(chapters)]
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else:
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chapter_list = [current_chapter + 1]
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# Create writing loop with real model
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loop = WritingLoop(
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run_dir=run_dir,
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model_callable=call_model,
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config_manager=ConfigManager(run_dir),
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state_manager=StateManager(run_dir)
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)
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# Write chapters
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for ch in chapter_list:
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# Check if paused
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if state.get('flags', {}).get('is_paused'):
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results["message"] = f"Paused at chapter {ch}"
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break
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result = loop.write_chapter(ch)
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results["chapters_written"].append({
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"chapter": ch,
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"status": result.get('qc_status'),
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"score": result.get('qc_score')
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})
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# In manual mode, require confirmation after each chapter
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if mode == "manual":
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results["status"] = "awaiting_confirmation"
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results["message"] = f"Chapter {ch} complete. Score: {result.get('qc_score')}. Confirm to continue?"
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return results
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results["status"] = "complete"
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results["message"] = f"Wrote {len(results['chapters_written'])} chapters"
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finally:
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run_lock.release()
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except Exception as e:
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results["status"] = "error"
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results["errors"].append(str(e))
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results["message"] = f"Error: {str(e)}"
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return results
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def get_required_confirmations(phase: str) -> list:
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"""
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Return list of items that require human confirmation for a given phase
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This helps OpenClaw know what to ask the user
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"""
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confirmations = {
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"1_init": [
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"书名 (book_title)",
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"类型 (genre)",
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"目标字数 (target_words)",
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"存放目录 (workspace_root)"
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],
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"2_style": [
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"风格指南 (style_guide.md)"
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],
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"3_outline": [
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"主线大纲 (main_outline.md)"
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],
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"4_planning": [
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"章节规划 (chapter_plan.json)"
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],
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"5_world": [
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"世界观设定 (world_building.md)"
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],
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"6_write": [
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"每章正文生成后确认",
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"每章评分确认"
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],
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"7_backpatch": [
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"回补修复确认"
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],
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"8_merge": [
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"合并后确认"
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],
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"9_final": [
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"最终检查报告确认"
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]
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}
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return confirmations.get(phase, [])
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