Turn ePub books into training examples for models that reproduce an author's writing style. Covers text extraction, coherent segmentation, varied instructions, model training, and checks for copied phrases and style transfer to new scenarios.
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This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.
安装前可以查看交给 AI 代理的指令内容。
A complete system for converting books into SFT datasets and training style-transfer models. This skill teaches the pipeline from raw ePub to a model that writes in any author's voice.
Activate this skill when:
1. Intelligent Segmentation Text chunks must be semantically coherent. Breaking mid-sentence teaches the model to produce fragmented output. Target: 150-400 words per chunk, always at natural boundaries.
2. Diverse Instruction Generation Use multiple prompt templates and system prompts to prevent overfitting. A single prompt style leads to memorization. Use 15+ prompt templates with 5+ system prompts.
3. Style Over Content The goal is learning the author's rhythm and vocabulary patterns, not memorizing plots. Synthetic instructions describe what happens without quoting the text.
┌─────────────────────────────────────────────────────────────────┐
│ ORCHESTRATOR AGENT │
│ Coordinates pipeline phases, manages state, handles failures │
└──────────────────────┬──────────────────────────────────────────┘
│
┌───────────────┼───────────────┬───────────────┐
▼ ▼ ▼ ▼
┌──────────────┐ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐
│ EXTRACTION │ │ SEGMENTATION │ │ INSTRUCTION │ │ DATASET │
│ AGENT │ │ AGENT │ │ AGENT │ │ BUILDER │
│ ePub → Text │ │ Text → Chunks│ │ Chunks → │ │ Pairs → │
│ │ │ 150-400 words│ │ Prompts │ │ JSONL │
└──────────────┘ └──────────────┘ └──────────────┘ └──────────────┘
│
┌───────────────┴───────────────┐
▼ ▼
┌──────────────┐ ┌──────────────┐
│ TRAINING │ │ VALIDATION │
│ AGENT │ │ AGENT │
│ LoRA on │ │ AI detector │
│ Tinker │ │ Originality │
└──────────────┘ └──────────────┘
<p> tags to preserve breaks# Extract text from ePub paragraphs
from epub2 import EPub
from bs4 import BeautifulSoup
def extract_epub(path):
book = EPub(path)
chapters = []
for item in book.flow:
html = book.get_chapter(item.id)
soup = BeautifulSoup(html, 'html.parser')
paragraphs = [p.get_text().strip() for p in soup.find_all('p')]
chapters.append('\n\n'.join(p for p in paragraphs if p))
return '\n\n'.join(chapters)
Smaller chunks (150-400 words) produce more training examples and better style transfer than larger chunks (250-650).
def segment(text, min_words=150, max_words=400):
paragraphs = text.split('\n\n')
chunks, buffer, buffer_words = [], [], 0
for para in paragraphs:
words = len(para.split())
if buffer_words + words > max_words and buffer_words >= min_words:
chunks.append('\n\n'.join(buffer))
# Keep last paragraph for overlap
buffer = [buffer[-1], para] if buffer else [para]
buffer_words = sum(len(p.split()) for p in buffer)
else:
buffer.append(para)
buffer_words += words
if buffer:
chunks.append('\n\n'.join(buffer))
return chunks
For an 86,000-word book:
Using a single prompt template causes memorization. Diverse templates teach the underlying style.
SYSTEM_PROMPTS = [
"You are an expert creative writer capable of emulating specific literary styles.",
"You are a literary writer with deep knowledge of classic prose styles.",
"You are a creative writer skilled at emulating distinctive authorial voices.",
"You write prose that captures the essence of modernist literature.",
"You are a talented writer who can channel classic American authors.",
]
PROMPT_TEMPLATES = [
"Write a passage in the style of {author}: {desc}",
"Channel {author}'s voice to write about: {desc}",
"In {author}'s distinctive prose style, describe: {desc}",
"Write this scene as {author} would have: {desc}",
"Using {author}'s repetitive technique, describe: {desc}",
"Capture the rhythm of {author} in this passage: {desc}",
"Write like {author}: {desc}",
"In the voice of {author}, write: {desc}",
"This is a literary exercise. Write like {author}: {desc}",
"Can you write in {author}'s style? {desc}",
]
INSTRUCTION_PROMPT = """Describe what is happening in this excerpt in 2-3 sentences.
Focus on: characters present, actions, emotions, setting.
Do NOT quote the text directly.
Excerpt:
{text}
"""
# Use a fast, cheap LLM (e.g., Gemini Flash)
instruction = llm_call(INSTRUCTION_PROMPT.format(text=chunk))
{
"messages": [
{"role": "system", "content": "You are an expert creative writer..."},
{"role": "user", "content": "Write in the style of Author: Scene description..."},
{"role": "assistant", "content": "The actual book text from chunk..."}
]
}
def build_examples(chunk, instruction, author, variants=2):
examples = []
for i in range(variants):
system = SYSTEM_PROMPTS[i % len(SYSTEM_PROMPTS)]
template = PROMPT_TEMPLATES[(chunk.id + i) % len(PROMPT_TEMPLATES)]
user = template.format(author=author, desc=instruction)
examples.append({"messages": [
{"role": "system", "content": system},
{"role": "user", "content": user},
{"role": "assistant", "content": chunk.text}
]})
return examples
CONFIG = {
"model_name": "Qwen/Qwen3-8B-Base", # Base, not instruct
"lora_rank": 32, # 352MB adapter
"learning_rate": 5e-4, # Higher for LoRA
"batch_size": 4,
"epochs": 3,
}
Use base (pretrained) models, not instruction-tuned versions:
import tinker
from tinker import types
training_client = await service_client.create_lora_training_client_async(
base_model="Qwen/Qwen3-8B-Base",
rank=32
)
for epoch in range(3):
for batch in batches:
await training_client.forward_backward_async(batch, loss_fn="cross_entropy")
await training_client.optim_step_async(types.AdamParams(learning_rate=5e-4))
result = await training_client.save_weights_for_sampler_async(name="final")
Test with scenarios that couldn't exist in the original book:
TEST_PROMPTS = [
"Write about a barista making lattes",
"Describe lovers communicating through text messages",
"Write about someone anxious about climate change",
]
If the model applies style markers to modern scenarios, it learned style, not content.
# Search training data for output phrases
grep "specific phrase from output" dataset.jsonl
# Should return: No matches
Test outputs with GPTZero, Pangram, or ZeroGPT.
Symptom: Model uses original character names in new scenarios. Cause: Limited name diversity from one book. Solution: Train on multiple books or add synthetic examples.
Symptom: Outputs contain exact sentences from training data. Cause: Too few prompt variations or too many epochs. Solution: Use 15+ templates, limit to 3 epochs.
Symptom: Sentences feel incomplete. Cause: Poor segmentation breaking mid-thought. Solution: Always break at paragraph boundaries.
| Metric | Value |
|---|---|
| Training examples | 500-1000 per book |
| Model | Qwen/Qwen3-8B-Base |
| LoRA rank | 32 |
| Adapter size | ~350 MB |
| Training time | ~15 min |
| Loss reduction | 90%+ |
| Style transfer success | ~50% perfect |
| Component | Cost |
|---|---|
| LLM (instruction generation) | ~$0.50 |
| Tinker training (15 min) | ~$1.50 |
| Total | ~$2.00 |
This example applies several skills from the Agent Skills for Context Engineering collection:
The pipeline follows the staged, idempotent architecture pattern:
Each phase is resumable and produces intermediate artifacts for debugging.
Segmentation is a form of context compression for training. The core insight from context-compression applies: information density matters more than information quantity. Smaller, coherent chunks (150-400 words) produce better style transfer than larger, diluted chunks.
The two-tier strategy mirrors context compression evaluation:
The pipeline uses the supervisor/orchestrator pattern:
This matches the principle that sub-agents exist primarily to isolate context rather than simulate roles.
Validation follows the end-state evaluation pattern:
The "modern scenario" test is a form of out-of-distribution evaluation that proves generalization.
Prompt diversity prevents attention collapse on single patterns. When training with identical prompt structures, the model memorizes the instruction-response mapping. Diverse templates force attention across the style patterns themselves.
Internal references:
Related skills from Agent Skills for Context Engineering:
External resources:
Created: 2025-12-26 Last Updated: 2025-12-28 Author: Muratcan Koylan Version: 2.0.0 Standalone: Yes (separate from main context-engineering collection)
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Design systems that use language models to judge AI responses. Covers scoring criteria, comparisons between two answers, bias reduction, confidence calibration, and checks against human judgments to make automated quality assessments more consistent and traceable.
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Model what an agent believes, wants, and commits to doing. Connect external facts to goals and plans using structured relationships, with evidence and validity periods that help trace why the agent chose an action.
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Guide multi-source research through source checks, comparison of key claims, and recovery from failed tool calls. Produce reports that distinguish retrieved evidence from unresolved questions and clearly document remaining gaps.
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Design structured summaries for long agent sessions that preserve goals, file changes, decisions, and next actions. Choose compression methods and timing, then test whether the agent can continue accurately without repeatedly retrieving information it already processed.
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Diagnose why an AI agent becomes less reliable as conversations and documents accumulate. Distinguish missed information, persistent false claims, irrelevant material, mixed tasks, and conflicting sources, then choose ways to clean, reorganize, or separate the context.
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Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.
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