feat: add SICO-style exemplar extraction system for few-shot writing

- New script: scripts/extract_exemplar.py
  Extracts style fingerprints from human-written articles (opening hook,
  emotional peak, transition/self-correction, closing) with statistical
  analysis (sentence stddev, vocab temperature, negative ratio, paragraph CV).
  Auto-detects category, supports batch import.

- SKILL.md: Add Step 4.4 exemplar injection
  Loads matching exemplars by category before writing, injects segments
  as few-shot style examples in the prompt.

- learn_edits.py: Auto-grow exemplar library
  After user edits, auto-extracts the final version into the exemplar
  library if humanness_score <= 50.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.gitignore

@@ -16,6 +16,10 @@ optimization-results.tsv
 output/
 !output/.gitkeep
 
+# Exemplar library (user-specific content)
+references/exemplars/*.md
+references/exemplars/index.yaml
+
 # Legacy client directories
 clients/
 

SKILL.md

@@ -198,6 +198,7 @@ WebSearch: "{选题关键词} 数据 报告 2025 2026"
 读取: {skill_dir}/playbook.md（如果存在，按 confidence 分级执行）
 读取: {skill_dir}/writing-config.yaml（如果存在，作为写作参数）
 读取: {skill_dir}/history.yaml（最近 3 篇的 dimensions 字段）
+读取: {skill_dir}/references/exemplars/index.yaml（如果存在）
 ```
 
 **4.1 历史最佳参数参考**（有 history.yaml 且包含 composite_score 时执行）：
@@ -219,7 +220,38 @@ WebSearch: "{选题关键词} 数据 报告 2025 2026"
 
 **优先级**：playbook.md（confidence ≥ 5 的规则）> persona > writing-guide.md。writing-guide 是底线（禁用词等），persona 在此基础上特化风格参数，playbook 中高置信度规则是用户个性化的最终覆盖。playbook 中 confidence < 5 的规则作为软性参考。
 
-**4.4 写文章**：
+**4.4 范文风格注入**（有 `references/exemplars/index.yaml` 时执行）：
+
+从 index.yaml 筛选 category 匹配当前框架类型的范文，按 humanness_score 升序（越低越人类）取 top 3。读取对应 .md 文件的片段内容。
+
+在写作 prompt 中注入：
+
+> 以下是该公众号风格的真实段落示例，模仿其句长节奏、情绪强度和口语化程度：
+>
+> 【开头风格】
+> {exemplar_1 的开头钩子段}
+>
+> 【情绪段风格】
+> {exemplar_2 的情绪高峰段}
+>
+> 【收尾风格】
+> {exemplar_3 的收尾段}
+
+Category 映射规则：
+
+| 框架类型 | exemplar category |
+|----------|-------------------|
+| 痛点型/深度解读 | tech-opinion |
+| 故事型 | story-emotional |
+| 清单型/对比型 | list-practical |
+| 热点解读型 | hot-take |
+| 其他 | general |
+
+如果匹配到的范文不足 3 篇，用 general category 补足。如果范文库为空，跳过此步。
+
+建库命令：`python3 {skill_dir}/scripts/extract_exemplar.py article.md`
+
+**4.5 写文章**：
 - H1 标题（20-28 字） + H2 结构，1500-2500 字
 - 真实素材锚定：Step 3.2 的素材分散嵌入各 H2 段落
 - **写作人格**：按 4.3 加载的人格参数写作（数据呈现方式、个人声音浓度、不确定性表达等）
@@ -375,6 +407,8 @@ python3 {skill_dir}/toolkit/cli.py preview {markdown} --theme {theme} --no-open
 | 做一个小绿书/图片帖 | `python3 {skill_dir}/toolkit/cli.py image-post img1.jpg img2.jpg -t "标题"` |
 | 诊断配置 / 检查反AI / 为什么AI检测没过 | `python3 {skill_dir}/scripts/diagnose.py --json` + LLM 交叉分析 |
 | 优化写作参数 / 优化参数 | 迭代循环：写测试短文 → 打分 → 调参（见辅助功能） |
+| 导入范文 / 建范文库 | `python3 {skill_dir}/scripts/extract_exemplar.py article.md` |
+| 查看范文库 | `python3 {skill_dir}/scripts/extract_exemplar.py --list` |
 
 ---
 

scripts/extract_exemplar.py

@@ -0,0 +1,373 @@
+#!/usr/bin/env python3
+"""
+Extract style exemplars from human-written articles for SICO-style few-shot injection.
+
+Takes a markdown article, analyzes it for style fingerprints, extracts key
+segments (opening hook, emotional peak, transition/self-correction, closing),
+and saves structured exemplar files to references/exemplars/.
+
+Usage:
+    python3 scripts/extract_exemplar.py article.md
+    python3 scripts/extract_exemplar.py article.md --category tech-opinion --source "公众号名"
+    python3 scripts/extract_exemplar.py article1.md article2.md article3.md  # batch
+    python3 scripts/extract_exemplar.py --list                                # list all exemplars
+"""
+
+import argparse
+import json
+import re
+import sys
+from datetime import datetime
+from pathlib import Path
+
+import yaml
+
+# Reuse analysis functions from humanness_score
+sys.path.insert(0, str(Path(__file__).parent))
+import humanness_score as hs
+
+SKILL_DIR = Path(__file__).parent.parent
+EXEMPLARS_DIR = SKILL_DIR / "references" / "exemplars"
+INDEX_FILE = EXEMPLARS_DIR / "index.yaml"
+
+CATEGORIES = ["tech-opinion", "story-emotional", "list-practical", "hot-take", "general"]
+
+# Category detection markers
+STORY_MARKERS = [
+    "我", "我们", "那天", "那年", "记得", "后来", "当时",
+    "第一次", "最后", "突然", "终于",
+]
+
+
+# ============================================================
+# Segment Extraction
+# ============================================================
+
+def extract_headings(text):
+    """Extract H2 headings from markdown."""
+    return re.findall(r'^##\s+(.+)$', text, re.MULTILINE)
+
+
+def extract_title(text):
+    """Extract H1 title from markdown."""
+    m = re.search(r'^#\s+(.+)$', text, re.MULTILINE)
+    return m.group(1).strip() if m else ""
+
+
+def extract_opening(paragraphs, max_chars=250):
+    """Extract opening hook — first non-empty paragraph(s) up to max_chars."""
+    result = []
+    total = 0
+    for p in paragraphs:
+        if total + len(p) > max_chars and result:
+            break
+        result.append(p)
+        total += len(p)
+    return "\n\n".join(result)
+
+
+def extract_emotional_peak(paragraphs):
+    """Find paragraph with highest negative emotion density."""
+    best_para, best_density = "", -1.0
+    for p in paragraphs:
+        if len(p) < 20:
+            continue
+        count = sum(1 for m in hs.NEGATIVE_MARKERS if m in p)
+        density = count / len(p) * 100
+        if density > best_density:
+            best_density = density
+            best_para = p
+    return best_para if best_density > 0 else ""
+
+
+def extract_transition(paragraphs):
+    """Find paragraph with most self-correction / transition patterns."""
+    transition_words = [
+        "但是", "不过", "然而", "话说回来", "换个角度",
+        "说回来", "但话又说回来", "不对", "算了",
+    ]
+    best_para, best_count = "", 0
+    for p in paragraphs:
+        if len(p) < 20:
+            continue
+        count = sum(len(re.findall(pat, p)) for pat in hs.SELF_CORRECTION_PATTERNS)
+        count += sum(p.count(w) for w in transition_words)
+        if count > best_count:
+            best_count = count
+            best_para = p
+    return best_para if best_count > 0 else ""
+
+
+def extract_closing(paragraphs, max_chars=250):
+    """Extract closing paragraph(s), reading backwards."""
+    result = []
+    total = 0
+    for p in reversed(paragraphs):
+        if total + len(p) > max_chars and result:
+            break
+        result.insert(0, p)
+        total += len(p)
+    return "\n\n".join(result)
+
+
+# ============================================================
+# Category Detection
+# ============================================================
+
+def detect_category(text, paragraphs, headings):
+    """Auto-detect article category from content features."""
+    data_count = sum(len(re.findall(p, text)) for p in hs.REAL_SOURCE_PATTERNS)
+    story_count = sum(text.count(m) for m in STORY_MARKERS)
+    h2_count = len(headings)
+    neg_count = sum(1 for m in hs.NEGATIVE_MARKERS if m in text)
+
+    scores = {
+        "tech-opinion": data_count * 2,
+        "story-emotional": story_count * 1.5,
+        "list-practical": h2_count * 3 if h2_count >= 5 else 0,
+        "hot-take": neg_count * 2 + data_count if len(text) < 2000 else 0,
+        "general": 5,
+    }
+    return max(scores, key=scores.get)
+
+
+# ============================================================
+# Statistical Fingerprint
+# ============================================================
+
+def compute_vocab_temperature(text):
+    """Compute vocabulary temperature band distribution."""
+    counts = {
+        "cold": sum(text.count(w) for w in hs.COLD_WORDS),
+        "warm": sum(text.count(w) for w in hs.WARM_WORDS),
+        "hot": sum(text.count(w) for w in hs.HOT_WORDS),
+        "wild": sum(text.count(w) for w in hs.WILD_WORDS),
+    }
+    total = sum(counts.values())
+    if total == 0:
+        return {k: 0.25 for k in counts}
+    return {k: round(v / total, 2) for k, v in counts.items()}
+
+
+def compute_paragraph_cv(paragraphs):
+    """Coefficient of variation for paragraph lengths."""
+    if len(paragraphs) < 3:
+        return 0.0
+    lengths = [len(p) for p in paragraphs]
+    mean = sum(lengths) / len(lengths)
+    if mean == 0:
+        return 0.0
+    variance = sum((l - mean) ** 2 for l in lengths) / len(lengths)
+    return round((variance ** 0.5) / mean, 2)
+
+
+def count_short_paragraphs(text):
+    """Count single-sentence short paragraphs (1-10 chars, non-heading)."""
+    return sum(1 for l in text.split('\n')
+               if l.strip() and 1 <= len(l.strip()) <= 10
+               and not l.strip().startswith('#'))
+
+
+# ============================================================
+# Main Extraction
+# ============================================================
+
+def extract_exemplar(text, category=None, source=None):
+    """Analyze article and return structured exemplar dict."""
+    clean = re.sub(r'^#+\s+.*$', '', text, flags=re.MULTILINE).strip()
+    paragraphs = hs._split_paragraphs(text)
+    sentences = hs._split_sentences(clean)
+    headings = extract_headings(text)
+    title = extract_title(text) or source or "untitled"
+
+    if not category:
+        category = detect_category(clean, paragraphs, headings)
+
+    score_result = hs.score_article(text)
+
+    # Sentence length stats
+    lengths = [len(s) for s in sentences]
+    if len(lengths) >= 2:
+        mean = sum(lengths) / len(lengths)
+        variance = sum((l - mean) ** 2 for l in lengths) / len(lengths)
+        sentence_stddev = round(variance ** 0.5, 1)
+    else:
+        sentence_stddev = 0.0
+
+    neg_count = sum(1 for s in sentences if any(m in s for m in hs.NEGATIVE_MARKERS))
+    negative_ratio = round(neg_count / len(sentences), 2) if sentences else 0.0
+
+    return {
+        "title": title,
+        "source": source or title,
+        "category": category,
+        "humanness_score": score_result["composite_score"],
+        "fingerprint": {
+            "sentence_stddev": sentence_stddev,
+            "vocab_temperature": compute_vocab_temperature(clean),
+            "negative_ratio": negative_ratio,
+            "paragraph_cv": compute_paragraph_cv(paragraphs),
+            "short_paragraphs": count_short_paragraphs(text),
+        },
+        "segments": {
+            "opening": extract_opening(paragraphs),
+            "emotional_peak": extract_emotional_peak(paragraphs),
+            "transition": extract_transition(paragraphs),
+            "closing": extract_closing(paragraphs),
+        },
+        "extracted_at": datetime.now().strftime("%Y-%m-%d"),
+        "char_count": len(clean),
+    }
+
+
+# ============================================================
+# Persistence
+# ============================================================
+
+def save_exemplar(exemplar):
+    """Save exemplar to markdown file and update index.yaml. Returns filepath."""
+    EXEMPLARS_DIR.mkdir(parents=True, exist_ok=True)
+
+    category = exemplar["category"]
+    num = 1
+    while (EXEMPLARS_DIR / f"{category}-{num:03d}.md").exists():
+        num += 1
+    filename = f"{category}-{num:03d}.md"
+    filepath = EXEMPLARS_DIR / filename
+
+    fp = exemplar["fingerprint"]
+    seg = exemplar["segments"]
+
+    frontmatter = {
+        "source": exemplar["source"],
+        "category": category,
+        "humanness_score": exemplar["humanness_score"],
+        "sentence_stddev": fp["sentence_stddev"],
+        "vocab_temperature": fp["vocab_temperature"],
+        "negative_ratio": fp["negative_ratio"],
+        "paragraph_cv": fp["paragraph_cv"],
+        "short_paragraphs": fp["short_paragraphs"],
+        "extracted_at": exemplar["extracted_at"],
+    }
+
+    content = "---\n"
+    content += yaml.dump(frontmatter, allow_unicode=True, default_flow_style=False)
+    content += "---\n\n"
+
+    section_map = [
+        ("opening", "开头钩子"),
+        ("emotional_peak", "情绪高峰"),
+        ("transition", "转折/自纠"),
+        ("closing", "收尾"),
+    ]
+    for key, label in section_map:
+        if seg.get(key):
+            content += f"## {label}\n\n{seg[key]}\n\n"
+
+    filepath.write_text(content, encoding="utf-8")
+    _update_index(filename, exemplar)
+    return filepath
+
+
+def _update_index(filename, exemplar):
+    """Add or update entry in index.yaml."""
+    index = []
+    if INDEX_FILE.exists():
+        with open(INDEX_FILE, "r", encoding="utf-8") as f:
+            index = yaml.safe_load(f) or []
+
+    entry = {
+        "file": filename,
+        "source": exemplar["source"],
+        "category": exemplar["category"],
+        "humanness_score": exemplar["humanness_score"],
+        "extracted_at": exemplar["extracted_at"],
+    }
+    index = [e for e in index if e.get("file") != filename]
+    index.append(entry)
+    index.sort(key=lambda x: (x["category"], x["humanness_score"]))
+
+    with open(INDEX_FILE, "w", encoding="utf-8") as f:
+        yaml.dump(index, f, allow_unicode=True, default_flow_style=False)
+
+
+# ============================================================
+# List / CLI
+# ============================================================
+
+def list_exemplars():
+    """Print all exemplars in the library."""
+    if not INDEX_FILE.exists():
+        print("范文库为空。用法: python3 scripts/extract_exemplar.py article.md")
+        return
+
+    with open(INDEX_FILE, "r", encoding="utf-8") as f:
+        index = yaml.safe_load(f) or []
+
+    if not index:
+        print("范文库为空。")
+        return
+
+    print(f"\n{'=' * 60}")
+    print(f"范文库 ({len(index)} 篇)")
+    print(f"{'=' * 60}")
+
+    by_cat = {}
+    for e in index:
+        by_cat.setdefault(e["category"], []).append(e)
+
+    for cat, entries in sorted(by_cat.items()):
+        print(f"\n  [{cat}] ({len(entries)} 篇)")
+        for e in entries:
+            score = e["humanness_score"]
+            bar = "█" * int((100 - score) / 10) + "░" * (10 - int((100 - score) / 10))
+            print(f"    {bar} {score:5.1f}  {e['source'][:40]}")
+
+
+def main():
+    parser = argparse.ArgumentParser(description="Extract style exemplars from articles")
+    parser.add_argument("inputs", nargs="*", help="Markdown article file(s)")
+    parser.add_argument("--category", "-c", choices=CATEGORIES,
+                        help="Article category (auto-detected if omitted)")
+    parser.add_argument("--source", "-s", help="Source name (e.g. account name)")
+    parser.add_argument("--list", "-l", action="store_true", help="List all exemplars")
+    parser.add_argument("--json", action="store_true", help="JSON output")
+    args = parser.parse_args()
+
+    if args.list:
+        list_exemplars()
+        return
+
+    if not args.inputs:
+        parser.print_help()
+        sys.exit(1)
+
+    for input_path in args.inputs:
+        path = Path(input_path)
+        if not path.exists():
+            print(f"Error: {input_path} not found", file=sys.stderr)
+            continue
+
+        text = path.read_text(encoding="utf-8")
+        exemplar = extract_exemplar(text, category=args.category, source=args.source)
+        filepath = save_exemplar(exemplar)
+
+        if args.json:
+            print(json.dumps(exemplar, ensure_ascii=False, indent=2))
+        else:
+            print(f"✓ {path.name}")
+            print(f"  Category:  {exemplar['category']}")
+            print(f"  Score:     {exemplar['humanness_score']:.1f}/100")
+            print(f"  Segments:  {sum(1 for v in exemplar['segments'].values() if v)}/4")
+            fp = exemplar["fingerprint"]
+            print(f"  Stddev:    {fp['sentence_stddev']}")
+            print(f"  Neg ratio: {fp['negative_ratio']:.0%}")
+            print(f"  Para CV:   {fp['paragraph_cv']}")
+            temp = fp["vocab_temperature"]
+            print(f"  Temp:      cold={temp['cold']} warm={temp['warm']} hot={temp['hot']} wild={temp['wild']}")
+            print(f"  Saved to:  {filepath}")
+            print()
+
+
+if __name__ == "__main__":
+    main()

scripts/learn_edits.py

@@ -325,6 +325,20 @@ def main():
     lesson_file = save_lesson(diff_result, args.draft, args.final)
     print(f"\nLesson saved to: {lesson_file}")
 
+    # Auto-grow exemplar library from edited finals
+    final_title = extract_title(final)
+    try:
+        import extract_exemplar
+        exemplar = extract_exemplar.extract_exemplar(final, source=final_title or "user-edited")
+        if exemplar["humanness_score"] <= 50:
+            exemplar_path = extract_exemplar.save_exemplar(exemplar)
+            print(f"\n✓ 终稿已加入范文库: {exemplar_path}")
+            print(f"  Score: {exemplar['humanness_score']:.1f}/100, Category: {exemplar['category']}")
+        else:
+            print(f"\n⚠ 终稿 humanness_score={exemplar['humanness_score']:.1f} > 50，未加入范文库")
+    except Exception as e:
+        print(f"\n⚠ 范文提取跳过: {e}")
+
     lesson_count = len(load_all_lessons())
     print(f"Total lessons: {lesson_count}")