国内AI时讯

.env.example

@@ -32,6 +32,7 @@ DAILY_WECOM_GITHUB_TRENDING=10
 DAILY_WECOM_GITHUB_EMERGING=10
 DAILY_WECOM_GITHUB_TOPIC=10
 DAILY_WECOM_AI_NEWS=10
+DAILY_WECOM_CN_AI_NEWS=8
 # 企微 Skills 合并前扫描池大小（同 source 合并后仍凑满 Top N）
 # DAILY_WECOM_SKILL_POOL=200
 
@@ -43,6 +44,8 @@ DAILY_WECOM_CHUNK_BYTES=4096
 
 # 国际 AI 时讯（RSS，见 daily/news/feeds.py）
 DAILY_AI_NEWS=1
+# 国内 AI 时讯（RSS，见 daily/news/feeds_cn.py）
+DAILY_CN_AI_NEWS=1
 # 英文描述 → 简短中文（DAILY_CURSOR_EDITOR=0 时生效）
 # DAILY_ZH_DESC=1
 # DAILY_ZH_DESC_BATCH=20

README.md

@@ -84,7 +84,16 @@ WECOM_WEBHOOK_KEY=your-key
 | 研究 / 论文 | arXiv cs.CL/AI/LG、HF Papers |
 | 社区讨论 | HN、Reddit r/LocalLLaMA / ClaudeAI / ML 等 |
 
-环境变量：`DAILY_AI_NEWS=1` · `DAILY_AI_NEWS_HOURS=72` · `DAILY_WECOM_AI_NEWS=8`
+环境变量：`DAILY_AI_NEWS=1` · `DAILY_CN_AI_NEWS=1` · `DAILY_AI_NEWS_HOURS=72` · `DAILY_WECOM_AI_NEWS=10` · `DAILY_WECOM_CN_AI_NEWS=8`
+
+**国内 AI 时讯**（RSS，见 `daily/news/feeds_cn.py`）：
+
+| 类别 | 覆盖 |
+|------|------|
+| AI 专业媒体 | 量子位、InfoQ 中文 |
+| 综合科技 | 36氪、雷锋网、Google News 中文 |
+| 开发者社区 | 掘金（标题 AI 关键词过滤） |
+
 
 ### 生成架构（Tier B · Cursor 编辑层）
 

daily/format_wecom.py

@@ -18,6 +18,7 @@ ICONS = {
     "emerging": "🌱",
     "topic": "🤖",
     "ainews": "🌍",
+    "cnainews": "🇨🇳",
     "pick": "📦",
     "theme": "🎯",
     "file": "📄",
@@ -231,6 +232,7 @@ def build_wecom_report(
     topic_name: str,
     topic_repos: list[dict[str, Any]],
     ai_news: list[dict[str, Any]] | None = None,
+    cn_ai_news: list[dict[str, Any]] | None = None,
     pick_command: str,
 ) -> str:
     lines = [
@@ -250,6 +252,11 @@ def build_wecom_report(
         lines.extend(_ai_news_lines(ai_news))
         lines.append("")
 
+    if cn_ai_news:
+        lines.append(f"{ICONS['cnainews']} **国内 AI 时讯 Top {len(cn_ai_news)}**")
+        lines.extend(_ai_news_lines(cn_ai_news))
+        lines.append("")
+
     lines.append(f"{ICONS['trending']} **Skills Trending Top {len(trending)}**")
     for rank, item in enumerate(trending, 1):
         lines.extend(_skill_line(rank, item, badge=item.get("badge", "")))

daily/generate.py

@@ -42,7 +42,14 @@ from daily.github.auth import github_html_headers
 from daily.github.search import fetch_emerging_repos, fetch_topic_hot_repos
 from daily.github.trending import fetch_github_trending, trending_data_source_note
 from daily.localize import LocalizeJob, localize_descriptions, needs_chinese
-from daily.news.fetch import fetch_ai_news, format_news_section, prepare_wecom_news_items
+from daily.news.fetch import (
+    fetch_ai_news,
+    fetch_cn_ai_news,
+    format_cn_news_section,
+    format_news_section,
+    prepare_wecom_cn_news_items,
+    prepare_wecom_news_items,
+)
 from daily.report_data import (
     build_full_payload,
     build_llm_input,
@@ -90,6 +97,7 @@ def _localize_descriptions_in_place(
     github_emerging: list[dict[str, Any]],
     github_topic: list[dict[str, Any]],
     ai_news: dict[str, Any],
+    cn_ai_news: dict[str, Any] | None = None,
 ) -> None:
     full_limit = full_desc_limit()
     news_limit = news_summary_limit()
@@ -264,6 +272,7 @@ def _build_highlights(
     github_trending: list[dict[str, Any]],
     github_emerging: list[dict[str, Any]],
     ai_news: dict[str, Any] | None = None,
+    cn_ai_news: dict[str, Any] | None = None,
 ) -> list[str]:
     points: list[str] = []
     if ai_news and ai_news.get("enabled"):
@@ -278,6 +287,20 @@ def _build_highlights(
             n0 = ai_news["flat"][0]
             pub = f" · {n0['published_fmt']}" if n0.get("published_fmt") else ""
             points.append(f"🌍 AI 时讯 [{n0['title']}]({n0['link']})（`{n0.get('source_name', '?')}`{pub}）")
+    if cn_ai_news and cn_ai_news.get("enabled"):
+        top_cn = prepare_wecom_cn_news_items(cn_ai_news)
+        if top_cn:
+            n0 = top_cn[0]
+            pub = f" · {n0['published_fmt']}" if n0.get("published_fmt") else ""
+            points.append(
+                f"🇨🇳 国内 AI [{n0['title']}]({n0['link']})（`{n0.get('source_name', '?')}`{pub}）"
+            )
+        elif cn_ai_news.get("flat"):
+            n0 = cn_ai_news["flat"][0]
+            pub = f" · {n0['published_fmt']}" if n0.get("published_fmt") else ""
+            points.append(
+                f"🇨🇳 国内 AI [{n0['title']}]({n0['link']})（`{n0.get('source_name', '?')}`{pub}）"
+            )
     if trending:
         t0 = trending[0]
         points.append(f"📈 Skills 榜首 **{t0.get('title')}**（{_format_installs(t0.get('installs', 0))}）")
@@ -418,6 +441,7 @@ def generate_report() -> tuple[str, str, Path, Path]:
     seen_repos.update(r["repo"] for r in github_emerging)
     topic_name, github_topic = fetch_topic_hot_repos(topic_fetch_n, exclude=seen_repos)
     ai_news = fetch_ai_news()
+    cn_ai_news = fetch_cn_ai_news()
 
     wecom_limits = {
         "trending": wecom_trending,
@@ -428,6 +452,7 @@ def generate_report() -> tuple[str, str, Path, Path]:
         "emerging": wecom_emerging,
         "topic": wecom_topic,
         "ai_news": env_int("DAILY_WECOM_AI_NEWS", 10),
+        "cn_ai_news": env_int("DAILY_WECOM_CN_AI_NEWS", 8),
     }
     llm_input = build_llm_input(
         date_str=date_str,
@@ -439,6 +464,7 @@ def generate_report() -> tuple[str, str, Path, Path]:
         github_topic=github_topic,
         topic_name=topic_name,
         ai_news=ai_news,
+        cn_ai_news=cn_ai_news,
         wecom_limits=wecom_limits,
     )
     save_json(
@@ -476,6 +502,7 @@ def generate_report() -> tuple[str, str, Path, Path]:
                 github_emerging=github_emerging,
                 github_topic=github_topic,
                 ai_news=ai_news,
+                cn_ai_news=cn_ai_news,
                 descriptions=editorial.get("descriptions") or {},
             )
             editorial_theme = theme_line_from_editorial(editorial) or None
@@ -484,7 +511,7 @@ def generate_report() -> tuple[str, str, Path, Path]:
 
     # 完整版归档：中文化 + 加长摘要（已是中文的条目会跳过翻译）
     _localize_descriptions_in_place(
-        trending, hot, github_trending, github_emerging, github_topic, ai_news
+        trending, hot, github_trending, github_emerging, github_topic, ai_news, cn_ai_news
     )
 
     themes = _theme_clusters(feed)
@@ -494,7 +521,7 @@ def generate_report() -> tuple[str, str, Path, Path]:
         "",
         f"> 生成时间：{now.strftime('%Y-%m-%d %H:%M')} (UTC+8)  ",
         f"> skills 数据更新：{updated}  ",
-        "> 数据来源：[skills.sh/trending](https://skills.sh/trending) · [skills.sh/hot](https://skills.sh/hot) · 国际 AI RSS",
+        "> 数据来源：[skills.sh/trending](https://skills.sh/trending) · [skills.sh/hot](https://skills.sh/hot) · 国际/国内 AI RSS",
         "",
         "---",
         "",
@@ -538,6 +565,8 @@ def generate_report() -> tuple[str, str, Path, Path]:
     section_no = 6
     lines.extend(format_news_section(ai_news, section_no=section_no))
     section_no += 1
+    lines.extend(format_cn_news_section(cn_ai_news, section_no=section_no))
+    section_no += 1
 
     watch = (env("GITHUB_REPOS") or "").strip()
     if watch:
@@ -581,9 +610,10 @@ def generate_report() -> tuple[str, str, Path, Path]:
             time_str=time_str,
             updated=updated,
             highlights=editorial_highlights
-            or _build_highlights(trending, hot, github_trending, github_emerging, ai_news),
+            or _build_highlights(trending, hot, github_trending, github_emerging, ai_news, cn_ai_news),
             theme_line=editorial_theme or _detect_theme_line(feed),
             ai_news=prepare_wecom_news_items(ai_news),
+            cn_ai_news=prepare_wecom_cn_news_items(cn_ai_news),
             trending=[
                 _prepare_skill_item(item, prev_ids, r)
                 for r, item in enumerate(

daily/news/__init__.py

@@ -1,3 +1,13 @@
-from daily.news.fetch import fetch_ai_news, format_news_section
+from daily.news.fetch import (
+    fetch_ai_news,
+    fetch_cn_ai_news,
+    format_cn_news_section,
+    format_news_section,
+)
 
-__all__ = ["fetch_ai_news", "format_news_section"]
+__all__ = [
+    "fetch_ai_news",
+    "fetch_cn_ai_news",
+    "format_news_section",
+    "format_cn_news_section",
+]

daily/news/feeds.py

@@ -10,6 +10,7 @@ class NewsFeed:
     name: str
     url: str
     slow: bool = False  # 限速源（如 Reddit）串行抓取
+    ai_filter: bool = False  # 综合源仅保留标题命中 AI 关键词的条目
 
 
 @dataclass(frozen=True)

daily/news/feeds_cn.py

@@ -0,0 +1,71 @@
+"""国内 AI 时讯 RSS 源定义（按类别分组）。"""
+
+from __future__ import annotations
+
+from daily.news.feeds import NewsCategory, NewsFeed
+
+# 综合源 ai_filter=True 时，仅保留标题命中以下词之一的条目
+CN_AI_TITLE_KEYWORDS: tuple[str, ...] = (
+    "人工智能",
+    "大模型",
+    "智能体",
+    "多模态",
+    "AIGC",
+    "LLM",
+    "GPT",
+    "Claude",
+    "Gemini",
+    "ChatGPT",
+    "OpenAI",
+    "Anthropic",
+    "Copilot",
+    "Agent",
+    "AI ",
+    " AI",
+    "AI·",
+    "AI业务",
+    "AI模型",
+    "AI助手",
+    "AI工具",
+    "AI编程",
+    "AI 编程",
+    "AI版",
+    "AI Agent",
+    "推理模型",
+    "深度学习",
+    "机器学习",
+    "Function Calling",
+)
+
+CN_NEWS_CATEGORIES: tuple[NewsCategory, ...] = (
+    NewsCategory(
+        id="media",
+        name="AI 专业媒体",
+        icon="📰",
+        feeds=(
+            NewsFeed("量子位", "https://www.qbitai.com/feed"),
+            NewsFeed("InfoQ 中文", "https://www.infoq.cn/feed/AI"),
+        ),
+    ),
+    NewsCategory(
+        id="tech",
+        name="综合科技",
+        icon="📱",
+        feeds=(
+            NewsFeed("36氪", "https://36kr.com/feed", ai_filter=True),
+            NewsFeed("雷锋网", "https://www.leiphone.com/feed"),
+            NewsFeed(
+                "Google News · AI",
+                "https://news.google.com/rss/search?q=人工智能+OR+大模型+OR+Agent+OR+LLM&hl=zh-CN&gl=CN&ceid=CN:zh-Hans",
+            ),
+        ),
+    ),
+    NewsCategory(
+        id="dev",
+        name="开发者社区",
+        icon="💻",
+        feeds=(
+            NewsFeed("掘金", "https://juejin.cn/rss", ai_filter=True),
+        ),
+    ),
+)

daily/news/fetch.py

@@ -1,4 +1,4 @@
-"""抓取并整理国际 AI 时讯 RSS。"""
+"""抓取并整理国际 / 国内 AI 时讯 RSS。"""
 
 from __future__ import annotations
 
@@ -17,7 +17,8 @@ import certifi
 import httpx
 
 from daily.config import env, env_int, news_summary_limit
-from daily.news.feeds import NEWS_CATEGORIES, NewsCategory
+from daily.news.feeds import NEWS_CATEGORIES, NewsCategory, NewsFeed
+from daily.news.feeds_cn import CN_AI_TITLE_KEYWORDS, CN_NEWS_CATEGORIES
 
 logger = logging.getLogger(__name__)
 
@@ -39,6 +40,11 @@ def _enabled() -> bool:
     return raw not in {"0", "false", "no", "off"}
 
 
+def _cn_enabled() -> bool:
+    raw = (env("DAILY_CN_AI_NEWS") or "1").strip().lower()
+    return raw not in {"0", "false", "no", "off"}
+
+
 def _hours_window() -> int:
     return max(1, env_int("DAILY_AI_NEWS_HOURS", 72))
 
@@ -55,6 +61,27 @@ def _wecom_limit() -> int:
     return max(1, env_int("DAILY_WECOM_AI_NEWS", 10))
 
 
+def _wecom_cn_limit() -> int:
+    return max(1, env_int("DAILY_WECOM_CN_AI_NEWS", 8))
+
+
+def _matches_cn_ai_title(title: str) -> bool:
+    text = title.strip()
+    if not text:
+        return False
+    lower = text.lower()
+    for keyword in CN_AI_TITLE_KEYWORDS:
+        if keyword.lower() in lower:
+            return True
+    return False
+
+
+def _filter_ai_entries(entries: list[dict[str, Any]], *, ai_filter: bool) -> list[dict[str, Any]]:
+    if not ai_filter:
+        return entries
+    return [item for item in entries if _matches_cn_ai_title(item.get("title", ""))]
+
+
 def _now_utc() -> datetime:
     return datetime.now(timezone.utc)
 
@@ -255,18 +282,23 @@ def _reddit_fetch_urls(feed_url: str) -> list[str]:
     return [primary, fallback]
 
 
-def _fetch_one(client: httpx.Client, category: NewsCategory, feed_url: str, feed_name: str) -> list[dict[str, Any]]:
+def _fetch_one(
+    client: httpx.Client,
+    category: NewsCategory,
+    feed: NewsFeed,
+) -> list[dict[str, Any]]:
     last_exc: Exception | None = None
-    for url in _reddit_fetch_urls(feed_url):
+    for url in _reddit_fetch_urls(feed.url):
         try:
             headers = _request_headers(dict(client.headers), url)
             resp = client.get(url, headers=headers)
             resp.raise_for_status()
-            return _parse_feed(resp.text, feed_name, category)
+            entries = _parse_feed(resp.text, feed.name, category)
+            return _filter_ai_entries(entries, ai_filter=feed.ai_filter)
         except Exception as exc:
             last_exc = exc
             continue
-    logger.warning("RSS fetch failed [%s] %s: %s", feed_name, feed_url, last_exc)
+    logger.warning("RSS fetch failed [%s] %s: %s", feed.name, feed.url, last_exc)
     return []
 
 
@@ -299,33 +331,29 @@ def _sort_key(item: dict[str, Any]) -> tuple[int, datetime]:
     return (0, dt)
 
 
-def fetch_ai_news() -> dict[str, Any]:
+def _fetch_news(categories: tuple[NewsCategory, ...]) -> dict[str, Any]:
-    """按类别抓取 AI 时讯，返回 {enabled, hours, categories, flat, stats}。"""
-    if not _enabled():
-        return {"enabled": False, "categories": [], "flat": [], "stats": {}}
-
     hours = _hours_window()
     per_feed = _per_feed_limit()
     per_category = _per_category_limit()
     cutoff = _now_utc() - timedelta(hours=hours)
 
     headers = {"User-Agent": USER_AGENT, "Accept": "application/rss+xml, application/atom+xml, application/xml, text/xml, */*"}
-    tasks: list[tuple[NewsCategory, str, str, bool]] = []
+    tasks: list[tuple[NewsCategory, NewsFeed]] = []
-    for category in NEWS_CATEGORIES:
+    for category in categories:
         for feed in category.feeds:
-            tasks.append((category, feed.url, feed.name, feed.slow))
+            tasks.append((category, feed))
 
-    raw_by_category: dict[str, list[dict[str, Any]]] = {c.id: [] for c in NEWS_CATEGORIES}
+    raw_by_category: dict[str, list[dict[str, Any]]] = {c.id: [] for c in categories}
     stats = {"feeds_total": len(tasks), "feeds_ok": 0, "items_raw": 0}
 
     with httpx.Client(timeout=15.0, verify=certifi.where(), follow_redirects=True, headers=headers) as client:
-        fast_tasks = [t for t in tasks if not t[3]]
+        fast_tasks = [t for t in tasks if not t[1].slow]
-        slow_tasks = [t for t in tasks if t[3]]
+        slow_tasks = [t for t in tasks if t[1].slow]
 
         with ThreadPoolExecutor(max_workers=8) as pool:
             futures = {
-                pool.submit(_fetch_one, client, cat, url, name): (cat.id, name)
+                pool.submit(_fetch_one, client, cat, feed): (cat.id, feed.name)
-                for cat, url, name, _slow in fast_tasks
+                for cat, feed in fast_tasks
             }
             for future in as_completed(futures):
                 cat_id, feed_name = futures[future]
@@ -339,13 +367,13 @@ def fetch_ai_news() -> dict[str, Any]:
                 stats["items_raw"] += len(entries)
                 raw_by_category[cat_id].extend(entries[:per_feed])
 
-        for cat, url, name, _slow in slow_tasks:
+        for cat, feed in slow_tasks:
-            entries = _fetch_one(client, cat, url, name)
+            entries = _fetch_one(client, cat, feed)
             if entries:
                 stats["feeds_ok"] += 1
             stats["items_raw"] += len(entries)
             raw_by_category[cat.id].extend(entries[:per_feed])
-            if _is_reddit_url(url):
+            if _is_reddit_url(feed.url):
                 time.sleep(2.0)
             else:
                 time.sleep(1.0)
@@ -353,7 +381,7 @@ def fetch_ai_news() -> dict[str, Any]:
     categories_out: list[dict[str, Any]] = []
     flat: list[dict[str, Any]] = []
 
-    for category in NEWS_CATEGORIES:
+    for category in categories:
         items = raw_by_category[category.id]
         items = [i for i in items if _within_window(i, cutoff)]
         items.sort(key=_sort_key, reverse=True)
@@ -384,16 +412,57 @@ def fetch_ai_news() -> dict[str, Any]:
     }
 
 
+def fetch_ai_news() -> dict[str, Any]:
+    """按类别抓取国际 AI 时讯，返回 {enabled, hours, categories, flat, stats}。"""
+    if not _enabled():
+        return {"enabled": False, "categories": [], "flat": [], "stats": {}}
+    return _fetch_news(NEWS_CATEGORIES)
+
+
+def fetch_cn_ai_news() -> dict[str, Any]:
+    """按类别抓取国内 AI 时讯，返回 {enabled, hours, categories, flat, stats}。"""
+    if not _cn_enabled():
+        return {"enabled": False, "categories": [], "flat": [], "stats": {}}
+    return _fetch_news(CN_NEWS_CATEGORIES)
+
+
 def format_news_section(news: dict[str, Any], *, section_no: int, wecom_limit: int | None = None) -> list[str]:
+    return _format_news_section(
+        news,
+        section_no=section_no,
+        title="国际 AI 时讯",
+        disabled_hint="AI 时讯已关闭（`DAILY_AI_NEWS=0`）。",
+        wecom_limit=wecom_limit,
+    )
+
+
+def format_cn_news_section(news: dict[str, Any], *, section_no: int, wecom_limit: int | None = None) -> list[str]:
+    return _format_news_section(
+        news,
+        section_no=section_no,
+        title="国内 AI 时讯",
+        disabled_hint="国内 AI 时讯已关闭（`DAILY_CN_AI_NEWS=0`）。",
+        wecom_limit=wecom_limit,
+    )
+
+
+def _format_news_section(
+    news: dict[str, Any],
+    *,
+    section_no: int,
+    title: str,
+    disabled_hint: str,
+    wecom_limit: int | None = None,
+) -> list[str]:
     if not news.get("enabled"):
-        return ["---", "", f"## {section_no}、国际 AI 时讯", "", "*AI 时讯已关闭（`DAILY_AI_NEWS=0`）。*", ""]
+        return ["---", "", f"## {section_no}、{title}", "", f"*{disabled_hint}*", ""]
 
     categories = news.get("categories") or []
     hours = news.get("hours", 72)
     lines = [
         "---",
         "",
-        f"## {section_no}、国际 AI 时讯",
+        f"## {section_no}、{title}",
         "",
         f"> 近 **{hours}h** · {news.get('stats', {}).get('feeds_ok', 0)}/{news.get('stats', {}).get('feeds_total', 0)} 源可用",
         "",
@@ -460,3 +529,52 @@ def prepare_wecom_news_items(news: dict[str, Any]) -> list[dict[str, Any]]:
             }
         )
     return items
+
+
+def prepare_wecom_cn_news_items(news: dict[str, Any]) -> list[dict[str, Any]]:
+    if not news.get("enabled"):
+        return []
+    limit = _wecom_cn_limit()
+    flat = _dedupe_items(news.get("flat") or [])
+    flat.sort(key=_sort_key, reverse=True)
+    preferred = ("media", "tech", "dev")
+    picked: list[dict[str, Any]] = []
+    seen_links: set[str] = set()
+    seen_sources: set[str] = set()
+
+    for cat in preferred:
+        for item in flat:
+            link = _normalize_link(item.get("link", ""))
+            source = item.get("source_name", "?")
+            if item.get("category_id") != cat or not link or link in seen_links or source in seen_sources:
+                continue
+            picked.append(item)
+            seen_links.add(link)
+            seen_sources.add(source)
+            if len(picked) >= limit:
+                break
+        if len(picked) >= limit:
+            break
+
+    if len(picked) < limit:
+        for item in flat:
+            link = _normalize_link(item.get("link", ""))
+            if not link or link in seen_links:
+                continue
+            picked.append(item)
+            seen_links.add(link)
+            if len(picked) >= limit:
+                break
+
+    items: list[dict[str, Any]] = []
+    for item in picked[:limit]:
+        items.append(
+            {
+                "title": item.get("title", "?"),
+                "link": item.get("link", ""),
+                "source_name": item.get("source_name", "?"),
+                "published_fmt": item.get("published_fmt", ""),
+                "desc_short": _clean_text(item.get("summary", ""), 36),
+            }
+        )
+    return items

daily/report_data.py

@@ -8,7 +8,7 @@ from typing import Any
 
 from daily.config import OUTPUT_DIR, env_int
 from daily.delta import build_movement_baseline, build_movement_context, compare_depth
-from daily.news.fetch import prepare_wecom_news_items
+from daily.news.fetch import prepare_wecom_cn_news_items, prepare_wecom_news_items
 from daily.skills_group import group_skills_by_source
 
 
@@ -54,9 +54,14 @@ def _slim_github(item: dict[str, Any]) -> dict[str, Any]:
     }
 
 
-def _slim_news_items(ai_news: dict[str, Any], limit: int) -> list[dict[str, Any]]:
+def _slim_news_items(
+    ai_news: dict[str, Any],
+    limit: int,
+    *,
+    prepare=prepare_wecom_news_items,
+) -> list[dict[str, Any]]:
     items: list[dict[str, Any]] = []
-    for item in prepare_wecom_news_items(ai_news):
+    for item in prepare(ai_news):
         items.append(
             {
                 "link": item.get("link", ""),
@@ -98,10 +103,12 @@ def build_llm_input(
     github_topic: list[dict[str, Any]],
     topic_name: str,
     ai_news: dict[str, Any],
+    cn_ai_news: dict[str, Any],
     wecom_limits: dict[str, int],
 ) -> dict[str, Any]:
     """供 Cursor 编辑的精简 JSON（不含完整 markdown）。"""
     news_limit = wecom_limits.get("ai_news", 10)
+    cn_news_limit = wecom_limits.get("cn_ai_news", 8)
     depth = compare_depth()
     trend_cmp = trending[:depth]
     hot_cmp = hot[:depth]
@@ -153,6 +160,11 @@ def build_llm_input(
             "repos": [_slim_github(x) for x in topic_slice],
         },
         "ai_news": _slim_news_items(ai_news, news_limit) if ai_news.get("enabled") else [],
+        "cn_ai_news": _slim_news_items(
+            cn_ai_news, cn_news_limit, prepare=prepare_wecom_cn_news_items
+        )
+        if cn_ai_news.get("enabled")
+        else [],
         "movement": movement,
         "movement_baseline": movement_baseline,
     }

skills/daily-agent/SKILL.md

@@ -29,6 +29,8 @@ Step 2 基于趋势 + 原始数据 → 写企微 Markdown 早报
 | `github_topic.topic` | Topic 名称，用于区块标题（如 `llm`） |
 | `movement.*_moves` | 较昨日新增（**仅**用于 opening / signals，**不**用于列表区块） |
 | `movement.*_summary` | 新增摘要（可选写入 signals） |
+| `ai_news` | 国际 AI 时讯 Top N |
+| `cn_ai_news` | 国内 AI 时讯 Top N |
 
 **禁止**使用已合并的 `skills_moves` / `github_moves` 自行扩写；**禁止**排名变化、安装涨跌。
 
@@ -98,6 +100,10 @@ Step 1 的 `signals` 与 `top_picks` **优先引用 Top 榜榜首/前列条目
 1. [{title_zh}]({link}) — {why 或摘要}
 2. ...（**必须 10 条**，来自 `ai_news`，按重要性排序）
 
+🇨🇳 **国内 AI · 精选 8**
+1. [{title}]({link}) — {why 或摘要}
+2. ...（**必须 8 条**，来自 `cn_ai_news`，按重要性排序；标题已是中文，可微调润色）
+
 <!-- **不要写** Skills Trending / Skills Hot 区块，Python 会在推送前按 source 合并后自动插入 -->
 
 🐙 **GitHub Trending Top {N}**
@@ -120,8 +126,9 @@ Step 1 的 `signals` 与 `top_picks` **优先引用 Top 榜榜首/前列条目
 3. **禁止**改用 `movement.*_moves` 作为列表来源；movement 仅用于 opening / signals 描述「今日新增」
 4. **禁止**在条目后写 `（新入 … #n）` 类括号标注
 5. **即使某榜较昨日无新增，仍须完整列出 Top 榜条目**
-6. **国际 AI 必须 10 条**
+6. **国际 AI 必须 10 条**（来自 `ai_news`）
-7. 禁止排名变化、安装涨跌、连霸描述
+7. **国内 AI 必须 8 条**（来自 `cn_ai_news`；无数据时写「暂无可用条目」）
+8. 禁止排名变化、安装涨跌、连霸描述
 
 ```markdown
 📈 **Skills Trending Top 10**