# Skill: Satire News Scraper for Short Drama

> **技能ID**: skill_satire_news_scraper
> **版本**: v1.0.0
> **分类**: L1-Content / Satire Analysis
> **置信度**: 0.89
> **创建时间**: 2026-03-25
> **创建者**: Evolver Agent

---

## 1. 技能描述

从讽刺新闻网站抓取内容，识别荒诞元素，评估短剧改编潜力，输出结构化数据供短剧创作使用。

### 1.1 核心能力
- **多源抓取**: 支持 The Onion、ClickHole、Babylon Bee 等8个主流讽刺新闻源
- **讽刺识别**: 自动识别7种讽刺类型，计算荒诞指数(absurdity_score)
- **短剧适配**: 评估内容的短剧改编潜力，提取场景/人物/对话元素
- **智能输出**: 生成标准化的JSON输出，包含完整的短剧改编建议

### 1.2 支持的数据源

| 网站 | 类型 | 抓取方式 | 优先级 |
|------|------|----------|--------|
| The Onion | 死板式新闻 | 网页抓取 | P0 |
| ClickHole | 病毒内容 | RSS | P0 |
| Babylon Bee | 保守讽刺 | RSS | P1 |
| Reductress | 女性媒体讽刺 | RSS | P1 |
| The Hard Times | 朋克文化 | RSS | P1 |
| McSweeney's | 文学幽默 | RSS | P2 |
| The Beaverton | 加拿大讽刺 | RSS | P2 |
| Waterford Whispers | 爱尔兰讽刺 | RSS | P2 |

---

## 2. 输入参数

```json
{
  "sources": {
    "type": "array",
    "description": "目标讽刺新闻源列表",
    "default": ["the_onion", "clickhole", "babylon_bee"],
    "items": {
      "type": "string",
      "enum": ["the_onion", "clickhole", "babylon_bee", "reductress", "the_hard_times", "mcsweeneys", "the_beaverton", "waterford_whispers"]
    }
  },
  "fetch_mode": {
    "type": "string",
    "description": "抓取模式",
    "default": "rss",
    "enum": ["rss", "web", "auto"]
  },
  "limit_per_source": {
    "type": "integer",
    "description": "每源抓取数量",
    "default": 20,
    "minimum": 1,
    "maximum": 100
  },
  "min_absurdity_score": {
    "type": "number",
    "description": "最小荒诞分数阈值",
    "default": 6.0,
    "minimum": 0,
    "maximum": 10
  },
  "adaptation_filter": {
    "type": "string",
    "description": "短剧适配等级筛选",
    "default": "B",
    "enum": ["A", "B", "C", "D", "all"]
  },
  "include_political": {
    "type": "boolean",
    "description": "是否包含政治讽刺内容(需本地化)",
    "default": false
  },
  "extract_drama_elements": {
    "type": "boolean",
    "description": "是否提取短剧元素(场景/人物/对话)",
    "default": true
  }
}
```

---

## 3. 输出Schema

```json
{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "type": "object",
  "required": ["metadata", "articles"],
  "properties": {
    "metadata": {
      "type": "object",
      "required": ["fetch_time", "total_articles", "sources"],
      "properties": {
        "fetch_time": { "type": "string", "format": "date-time" },
        "total_articles": { "type": "integer" },
        "sources": { "type": "array", "items": { "type": "string" } },
        "filter_stats": {
          "type": "object",
          "properties": {
            "fetched": { "type": "integer" },
            "filtered_by_absurdity": { "type": "integer" },
            "filtered_by_adaptation": { "type": "integer" },
            "included": { "type": "integer" }
          }
        }
      }
    },
    "articles": {
      "type": "array",
      "items": {
        "$ref": "#/definitions/SatireArticle"
      }
    }
  },
  "definitions": {
    "SatireArticle": {
      "type": "object",
      "required": ["id", "title", "content", "source", "satire_analysis"],
      "properties": {
        "id": { "type": "string", "description": "文章唯一ID" },
        "title": { "type": "string", "description": "文章标题" },
        "content": { "type": "string", "description": "文章正文内容" },
        "source": { "type": "string", "description": "来源网站" },
        "category": { "type": "string", "description": "文章分类" },
        "url": { "type": "string", "description": "原文链接" },
        "published_at": { "type": "string", "format": "date-time" },
        "satire_analysis": {
          "type": "object",
          "required": ["absurdity_score", "satire_type", "markers"],
          "properties": {
            "absurdity_score": {
              "type": "number",
              "minimum": 0,
              "maximum": 10,
              "description": "荒诞指数 0-10"
            },
            "satire_type": {
              "type": "string",
              "enum": ["deadpan_news", "viral_content", "conservative_satire", "feminist_media_satire", "subculture_satire", "literary_humor", "national_satire", "mild_satire", "none"]
            },
            "markers": {
              "type": "array",
              "items": { "type": "string" },
              "description": "命中的讽刺标记词"
            },
            "premise": { "type": "string", "description": "荒诞前提摘要" },
            "twist": { "type": "string", "description": "反转/笑点说明" }
          }
        },
        "drama_adaptation": {
          "type": "object",
          "required": ["adaptability_rating", "recommended_duration"],
          "properties": {
            "adaptability_rating": {
              "type": "string",
              "enum": ["A", "B", "C", "D"],
              "description": "A=直接改编, B=需调整, C=大幅改写, D=不适合"
            },
            "recommended_duration": {
              "type": "string",
              "enum": ["15s", "60s", "3-5min"],
              "description": "建议短剧时长"
            },
            "adaptation_notes": { "type": "string", "description": "改编建议" },
            "localization_required": { "type": "boolean" }
          }
        },
        "drama_elements": {
          "type": "object",
          "properties": {
            "setting": {
              "type": "object",
              "properties": {
                "location": { "type": "string" },
                "mood": { "type": "string" }
              }
            },
            "characters": {
              "type": "array",
              "items": {
                "type": "object",
                "properties": {
                  "role": { "type": "string", "enum": ["protagonist", "antagonist", "supporting"] },
                  "type": { "type": "string" },
                  "trait": { "type": "string" }
                }
              }
            },
            "dialogue": {
              "type": "array",
              "items": { "type": "string" },
              "description": "可直接使用的对话"
            },
            "visual_gags": {
              "type": "array",
              "items": { "type": "string" },
              "description": "视觉笑点建议"
            }
          }
        }
      }
    }
  }
}
```

---

## 4. 执行逻辑

### 4.1 主流程

```
┌─────────────────┐
│  1. 参数校验     │
└────────┬────────┘
         ▼
┌─────────────────┐
│  2. 数据源抓取   │ ← RSS/网页抓取
└────────┬────────┘
         ▼
┌─────────────────┐
│  3. 数据清洗     │ ← 去重、格式化
└────────┬────────┘
         ▼
┌─────────────────┐
│  4. 讽刺分析     │ ← 计算absurdity_score
└────────┬────────┘
         ▼
┌─────────────────┐
│  5. 短剧适配评估 │ ← 评级A/B/C/D
└────────┬────────┘
         ▼
┌─────────────────┐
│  6. 元素提取     │ ← 场景/人物/对话
└────────┬────────┘
         ▼
┌─────────────────┐
│  7. 输出结果     │ ← 生成JSON
└─────────────────┘
```

### 4.2 数据源抓取策略

**RSS 模式(推荐)**
```python
RSS_FEEDS = {
    "clickhole": "https://clickhole.com/feed/",
    "babylon_bee": "https://babylonbee.com/feed/",
    "reductress": "https://reductress.com/feed/",
    "the_hard_times": "https://thehardtimes.net/feed/",
    "mcsweeneys": "http://feeds.feedburner.com/mcsweeneys",
    "the_beaverton": "https://www.thebeaverton.com/feed/",
    "waterford_whispers": "https://waterfordwhispersnews.com/feed/"
}
```

**网页抓取模式(The Onion)**
```python
WEB_SELECTORS = {
    "container": "article.post",
    "title": "h1.headline",
    "content": ".entry-content",
    "category": ".category-tag"
}
```

### 4.3 Absurdity Score 计算算法

```python
def calculate_absurdity_score(article):
    """
    计算荒诞指数 (0-10)
    基于四大维度加权计算
    """
    # 1. 标题讽刺标记 (0-3分)
    headline_markers = {
        "local_man": ["Local Man", "Area Man", "Area Woman"],
        "study_finds": ["Study Finds", "Report:", "Experts Say"],
        "absurd_escalation": ["somehow", "unexpectedly", "tragically"],
        "literal_interpretation": ["literally", "actually", "definitely"],
        "emotional_manipulation": ["Heartbreaking", "Devastating", "Inspiring"]
    }

    headline_score = 0
    title_lower = article.title.lower()
    for marker_type, markers in headline_markers.items():
        if any(m.lower() in title_lower for m in markers):
            headline_score += 0.6
    headline_score = min(3, headline_score)

    # 2. 内容荒诞密度 (0-2.5分)
    absurdity_keywords = [
        "aliens", "time travel", "talking", "supernatural",
        "billion", "million", "37", "entire family",
        "turns out", "little did they know", "shocked to discover"
    ]
    content_lower = article.content.lower()
    keyword_count = sum(1 for kw in absurdity_keywords if kw in content_lower)
    density_score = min(2.5, keyword_count * 0.3)

    # 3. 叙事反转强度 (0-2.5分)
    twist_indicators = [
        ("somehow", 1.0),
        ("turns out", 0.8),
        ("unexpectedly", 0.8),
        ("but actually", 0.7),
        ("discovered that", 0.6)
    ]
    twist_score = sum(score for indicator, score in twist_indicators
                      if indicator in content_lower)
    twist_score = min(2.5, twist_score)

    # 4. 短剧适配度 (0-2分)
    adaptability_indicators = {
        "scene_clear": ["office", "home", "restaurant", "party"],
        "dialogue_rich": ['"', "said", "told"],
        "character_distinct": ["local man", "dad", "boss", "teacher"]
    }
    adapt_score = 0
    for indicator_type, keywords in adaptability_indicators.items():
        if any(kw in content_lower for kw in keywords):
            adapt_score += 0.7
    adapt_score = min(2, adapt_score)

    total = headline_score + density_score + twist_score + adapt_score
    return round(total, 1)
```

### 4.4 讽刺类型识别规则

| 类型 | 识别规则 | 代表源 |
|------|----------|--------|
| `deadpan_news` | AP新闻格式 + 荒谬内容 | The Onion |
| `viral_content` | 列表/测验/点击诱饵格式 | ClickHole |
| `conservative_satire` | 传统价值观视角反转 | Babylon Bee |
| `feminist_media_satire` | 女性媒体风格 + relatability | Reductress |
| `subculture_satire` | 亚文化黑话 + 内部梗 | The Hard Times |
| `literary_humor` | 高品位文学风格 | McSweeney's |
| `national_satire` | 国家/地域特色讽刺 | Beaverton/Waterford |

**识别函数**
```python
def detect_satire_type(article):
    source_mapping = {
        "the_onion": "deadpan_news",
        "clickhole": "viral_content",
        "babylon_bee": "conservative_satire",
        "reductress": "feminist_media_satire",
        "the_hard_times": "subculture_satire",
        "mcsweeneys": "literary_humor",
        "the_beaverton": "national_satire",
        "waterford_whispers": "national_satire"
    }

    # 基于来源初步判断
    base_type = source_mapping.get(article.source, "unknown")

    # 基于内容微调
    title_lower = article.title.lower()

    if "quiz" in title_lower or "which" in title_lower:
        return "viral_content"
    elif any(x in title_lower for x in ["study finds", "report:", "local man"]):
        return "deadpan_news"
    elif "open letter" in title_lower:
        return "literary_humor"

    return base_type
```

### 4.5 短剧适配评级算法

```python
def assess_drama_adaptation(article, absurdity_score):
    """
    评估短剧改编潜力，返回 A/B/C/D 评级
    """
    scores = {
        "scene_visualization": 0,  # 场景可视化 (0-2.5)
        "dialogue_richness": 0,    # 对话丰富度 (0-2.5)
        "conflict_clarity": 0,     # 冲突明确性 (0-2.0)
        "twist_impact": 0,         # 反转冲击力 (0-2.0)
        "performance_feasibility": 0  # 表演可行性 (0-1.0)
    }

    content = article.content.lower()
    title = article.title.lower()

    # 场景可视化
    locations = ["office", "home", "restaurant", "party", "wedding", "hospital"]
    if any(loc in content for loc in locations):
        scores["scene_visualization"] = 2.0
    elif "local" in title:
        scores["scene_visualization"] = 1.5
    else:
        scores["scene_visualization"] = 1.0

    # 对话丰富度
    quote_count = content.count('"')
    if quote_count >= 4:
        scores["dialogue_richness"] = 2.5
    elif quote_count >= 2:
        scores["dialogue_richness"] = 1.8
    elif "said" in content or "told" in content:
        scores["dialogue_richness"] = 1.2
    else:
        scores["dialogue_richness"] = 0.8

    # 冲突明确性
    conflict_keywords = ["refuses", "demanding", "accused", "argument", "fight"]
    if any(kw in content for kw in conflict_keywords):
        scores["conflict_clarity"] = 2.0
    elif absurdity_score > 7:
        scores["conflict_clarity"] = 1.5
    else:
        scores["conflict_clarity"] = 1.0

    # 反转冲击力
    if absurdity_score >= 8:
        scores["twist_impact"] = 2.0
    elif absurdity_score >= 6.5:
        scores["twist_impact"] = 1.5
    else:
        scores["twist_impact"] = 0.8

    # 表演可行性
    if any(x in content for x in ["dragon", "alien", "explosion"]):
        scores["performance_feasibility"] = 0.3  # 需要特效
    else:
        scores["performance_feasibility"] = 1.0

    total = sum(scores.values())

    # 评级标准
    if total >= 8 and absurdity_score >= 7:
        return "A"  # 直接改编
    elif total >= 6.5:
        return "B"  # 需少量调整
    elif total >= 4.5:
        return "C"  # 需大幅改写
    else:
        return "D"  # 不适合
```

---

## 5. 错误处理

### 5.1 错误码定义

| 错误码 | 描述 | 处理建议 |
|--------|------|----------|
| `RSS_FETCH_ERROR` | RSS feed获取失败 | 切换至网页抓取模式 |
| `WEB_SCRAPE_ERROR` | 网页抓取失败 | 检查选择器，降低请求频率 |
| `RATE_LIMITED` | 请求频率超限 | 降低频率至20req/min，使用代理 |
| `PARSE_ERROR` | 内容解析失败 | 检查编码(强制UTF-8) |
| `NO_RESULTS` | 无匹配结果 | 放宽筛选条件 |
| `TIMEOUT_ERROR` | 请求超时 | 增加超时时间，分批请求 |

### 5.2 重试策略

```python
retry_policy = {
    "max_attempts": 3,
    "backoff_factor": 2,
    "initial_delay": 1,
    "retryable_errors": ["TIMEOUT_ERROR", "RATE_LIMITED", "RSS_FETCH_ERROR"]
}
```

---

## 6. 示例代码

### 6.1 Python 调用示例

```python
import requests
import json

# 调用 Skill API
response = requests.post(
    "https://api.lemonai.com/v1/skills/satire-scraper/execute",
    headers={"Authorization": "Bearer YOUR_API_KEY"},
    json={
        "sources": ["the_onion", "clickhole", "babylon_bee"],
        "fetch_mode": "auto",
        "limit_per_source": 10,
        "min_absurdity_score": 7.0,
        "adaptation_filter": "B",
        "extract_drama_elements": True
    }
)

result = response.json()

# 处理高适配度内容
for article in result["articles"]:
    if article["drama_adaptation"]["adaptability_rating"] == "A":
        print(f"[{article['satire_analysis']['absurdity_score']}] {article['title']}")
        print(f"讽刺类型: {article['satire_analysis']['satire_type']}")
        print(f"荒诞前提: {article['satire_analysis']['premise']}")
        print(f"建议时长: {article['drama_adaptation']['recommended_duration']}")
        print("---")
```

### 6.2 直接抓取示例

```python
import feedparser

# RSS 抓取示例
def fetch_from_rss(source_url):
    feed = feedparser.parse(source_url)
    articles = []

    for entry in feed.entries[:20]:
        article = {
            "title": entry.title,
            "content": entry.get("summary", entry.get("description", "")),
            "url": entry.link,
            "published_at": entry.get("published", "")
        }

        # 应用 absurdity_score 算法
        article["absurdity_score"] = calculate_absurdity_score(article)

        if article["absurdity_score"] >= 6.0:
            articles.append(article)

    return articles

# 抓取 ClickHole
clickhole_articles = fetch_from_rss("https://clickhole.com/feed/")
```

---

## 7. 性能指标

| 指标 | 基准值 | 说明 |
|------|--------|------|
| 单次请求耗时 | < 45s | 抓取3个源，每源20条 |
| 日调用上限 | 500次 | 受数据源限制 |
| 成功率 | > 95% | 包括重试后成功 |
| absurdity_score MAE | < 1.5 | 与人工评分平均绝对误差 |
| 类型分类准确率 | > 75% | 讽刺类型识别准确率 |

---

## 8. 版本历史

| 版本 | 日期 | 变更内容 |
|------|------|----------|
| v1.0.0 | 2026-03-25 | 初始版本，支持8个讽刺新闻源 |

---

## 9. 参考资料

- [The Onion](https://www.theonion.com/)
- [ClickHole](https://clickhole.com/)
- [Babylon Bee](https://babylonbee.com/)
- [Reductress](https://reductress.com/)
- [The Hard Times](https://thehardtimes.net/)

---

*技能定义版本: v1.0.0*
*最后更新: 2026-03-25*
*维护者: Evolver Agent*