新增 query 搜索 + OSS 转存 + run 隔离产出（入口二）

- search.py：关键词搜索 → content_id（翻页/去重/截断），复用 crawler.RateLimiter
- oss.py：媒体直链 → OSS cdn_url，to_oss 兜底永不抛（绕防盗链/临时签名链时效）
- config.py：Settings 加 5 字段(带默认)+读 CK_OSS_*/CK_SEARCH_* env
- video_extract.py：extract_video 加 oss_video_url，非空跳过下载+base64（已验证 Gemini 直读 OSS http 视频链）
- decompose.py：QUERIES 常量 + 抽 process_one + expand_queries(跨query去重) + read_one 媒体走 OSS + slugify + 写 web/runs/<slug>.json + manifest；main 双入口 `python scripts/decompose.py queries`
- web/index.html：run 选择器（fetch /runs/index.json，默认回退 frameworks.json）
- 测试：test_oss/test_search/test_decompose_helpers + 视频直喂测试（共 50 passed）
- 文档：开发文档/产品文档.md + 技术文档.md；框架 md 移入 开发文档/
- 清理：删 2 个过时 subagent；pyproject 补 numpy（scope_link 实际依赖）

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

.claude/agents/extraction-reviewer.md

@@ -1,18 +0,0 @@
----
-name: extraction-reviewer
-description: 审「真实产出的颗」质量——读 web/frameworks.json 的 knowledges[]，逐颗核对粒度、类型判定、组件颗判据、作用域不串味、创作非制作。给问题清单 + 改进建议。用于 v2 产出的质量交叉验证。
-tools: Read, Bash, Grep, Glob
-model: sonnet
----
-
-你是「解构质量评审员」。读 `web/frameworks.json` 真实产出的 `knowledges[]`（约 9 帖、~24 颗），逐颗按《创作知识拆解框架.md》核对质量，给具体问题（哪帖哪颗哪处）+ 建议。**只读。**
-
-评审维度：
-1. **粒度**：how 颗是不是一个完整框架（非碎片、非把内部步骤拆成独立颗）；有没有「硬造假 how」（本该是 what 清单却掰成 how）。
-2. **类型判定**：how/what/why 判得对吗？纯构成/清单→what(概要+kind+body)、纯原理→why(一段阐述)、有不可跳步工序→how。what 的 kind(子集/多维关系/序列)判对吗、子集的维度拆分规则是单一轴或"类型枚举"吗；why 的阐述是否忠实整合、没擅自加"怎么用"。
-3. **组件颗判据**：标 role=组件 的 what/why，parent 指向的真的是一个 how 颗吗？它「脱离这个 how 还独立可复用」吗（够格单列）？有没有把一次性指令也单列（不够格）？有没有该单列却漏的？
-4. **作用域不串味**：抽查若干作用域值，实质/形式/感受/作用/意图 有没有站错列（实质=对象 vs 作用=功能；作用=手段 vs 意图=目的；感受=观众 vs 意图=作者）。值是否原子短词、非泛词。
-5. **创作非制作**：content / 步骤里有没有混进纯工艺执行（器材/软件操作/参数/导出）。
-6. **忠实**：directive 里的原帖示例是否像原文保留（`『』`/`例：`），有没有疑似编造。
-
-输出：按帖列「问题点」（定位 + 一句问题 + 建议），最后给一个总体质量判断（可用 / 需小修 / 需重做 哪几颗）。

.claude/agents/framework-auditor.md

@@ -1,20 +0,0 @@
----
-name: framework-auditor
-description: 审计「创作知识拆解 v2」是否在真实代码+数据+前端里彻底落实——读《创作知识拆解框架.md》v2 作为规约，逐条核对 skill phase 文档、frameworks.json/payloads.json、web/index.html、scripts/decompose.py，给 PASS/FAIL + 证据。用于 v2 实现的交叉验证。
-tools: Read, Bash, Grep, Glob
-model: sonnet
----
-
-你是「框架落实审计员」。规约 = `创作知识拆解框架.md`（v2）。逐条核对实现是否真落实，每条给 PASS/FAIL + 文件:行 或命令输出证据。**只读，不改。**
-
-核对清单：
-1. **三 lane 对等**：`创作知识提取-skill/extraction/phase1-frame.md` 是否对 How/What/Why 各给完整成形模板（how:目标+steps；what:概要+kind+维度拆分规则+body条目；why:阐述一段[是什么+为什么,忠实整合,不含"怎么用"]），而非 how 默认+兜底。
-2. **类型闸 + 组件颗**：phase1-frame 是否先判类型、再「how 抽骨架 → 内部 what/why 单列为组件颗(parent cross-ref) → orphan → 无 how 则纯 what/why」。
-3. **作用域分流**：phase2-scope 是否 how 逐步 / what·why 颗级。
-4. **schema 落地**：`web/frameworks.json` 是否 `posts[].knowledges[]`（不是单 `framework`）；每颗有 type/role/parent；至少出现「how 主颗 + what 组件颗(带 parent)」+ 某帖有 orphan 主颗(what/why)。统计：几帖、共几颗、类型分布、组件颗数。
-5. **payload 按类型分拼**：`web/payloads.json` 每颗一条；how 颗 custom_ext 有创作阶段/动作，what 颗业务阶段+概要、why 颗业务阶段(+组件颗有「出自」)；what 的 content 是结构化对象 {name,kind,维度拆分规则,body}、why 的 content 是阐述一段字符串；dim_attributes 与 type 一致。跑 `python3 创作知识提取-skill/tools/lint-payload.py web/payloads.json`，须 0 ERROR。
-6. **前端三渲染**：`web/index.html` 是否按 knowledges[] 渲染、how→工序表/what→构成卡(概要+kind+body条目表)/why→阐述卡(一段)、组件颗 cross-ref 可跳、定位 drawer + 提示词溯源。grep 关键组件（HowTable/WhatCard/WhyCard/kref）。
-7. **无 how-only 残留**：grep 确认没有"只认 post.framework 单颗"的旧逻辑残留在 web/index.html 或 decompose。
-8. **decompose 引擎**：`scripts/decompose.py` 是否把 phase 文档当 prompt 喂 LLM（不另写提示词）、① extractor 读图 → ② 判颗 → ③ 作用域定位 → ⑤ 组装。
-
-最后给「残留清单」：任何没落实/半落实/与规约不符的点（没有就写「无」）。

creation_knowledge/config.py

@@ -88,6 +88,12 @@ class Settings:
     frames_dir: str
     douyin_ratio: str  # 视频下载偏好码率（控成本/体积），如 540p
     data_dir: str  # 媒体本地落盘根目录；空字符串=关闭媒体保存（解耦开关）
+    # query 搜索 + OSS 转存（默认值必填：tests/test_video_extract.py 用关键字构造 Settings 且不传这些）
+    oss_upload_url: str = "http://crawler-upload-v2.aiddit.com/crawler/oss/upload_stream"
+    oss_upload_timeout_seconds: int = 60
+    search_default_limit: int = 5
+    search_content_type: str = "图文"
+    search_sort_type: str = "综合"
 
     @classmethod
     def from_env(cls, env_file: str | Path = ".env") -> "Settings":
@@ -122,4 +128,14 @@ class Settings:
             frames_dir=env_value("CK_FRAMES_DIR", file_env, "runtime/frames"),
             douyin_ratio=env_value("CK_DOUYIN_RATIO", file_env, "540p"),
             data_dir=env_value("CK_DATA_DIR", file_env, "data"),
+            oss_upload_url=env_value(
+                "CK_OSS_UPLOAD_URL", file_env,
+                "http://crawler-upload-v2.aiddit.com/crawler/oss/upload_stream",
+            ),
+            oss_upload_timeout_seconds=int(
+                env_value("CK_OSS_UPLOAD_TIMEOUT_SECONDS", file_env, "60")
+            ),
+            search_default_limit=int(env_value("CK_SEARCH_DEFAULT_LIMIT", file_env, "5")),
+            search_content_type=env_value("CK_SEARCH_CONTENT_TYPE", file_env, "图文"),
+            search_sort_type=env_value("CK_SEARCH_SORT_TYPE", file_env, "综合"),
         )

creation_knowledge/integrations/oss.py

@@ -0,0 +1,80 @@
+"""OSS 转存：把帖子媒体直链转存到公网 CDN，返回 cdn_url（绕过防盗链 / 临时签名链时效）。
+
+接口（实测 2026-06-26）：
+    POST settings.oss_upload_url  body{src_url, src_type:"video"|"image"}
+      → {status:0, oss_object:{cdn_url:"https://res.cybertogether.net/...", oss_object_key, ...}}
+
+纯 parser（parse_oss_response）可离线测；upload_stream 负责 HTTP（失败抛 OssError）；
+to_oss 是兜底包装——任何失败返回原 src_url、永不抛，供 decompose 降级不阻断。
+"""
+from __future__ import annotations
+
+from typing import Any, Callable, Optional
+
+import httpx
+
+from creation_knowledge.config import Settings
+
+
+class OssError(RuntimeError):
+    pass
+
+
+def parse_oss_response(response: Any) -> str:
+    """{status:0, oss_object:{cdn_url}} → cdn_url。status!=0 或缺 cdn_url 抛 OssError。"""
+    if not isinstance(response, dict):
+        raise OssError("bad_response: not a dict")
+    status = response.get("status")
+    if status not in (0, "0"):
+        raise OssError(f"business_error: status={status} msg={response.get('msg')}")
+    obj = response.get("oss_object") or {}
+    cdn = obj.get("cdn_url")
+    if not cdn:
+        raise OssError("empty_cdn_url: oss_object.cdn_url missing")
+    return cdn
+
+
+def upload_stream(
+    src_url: str,
+    *,
+    src_type: str,
+    settings: Optional[Settings] = None,
+    http_post: Callable[..., Any] = httpx.post,
+    env_file: str = ".env",
+) -> str:
+    """转存一条直链到 OSS，返回 cdn_url；HTTP/解析失败抛 OssError。"""
+    settings = settings or Settings.from_env(env_file)
+    if src_type not in ("video", "image"):
+        raise OssError(f"bad_src_type: {src_type}")
+    try:
+        resp = http_post(
+            settings.oss_upload_url,
+            json={"src_url": src_url, "src_type": src_type},
+            headers={"Content-Type": "application/json"},
+            timeout=settings.oss_upload_timeout_seconds,
+        )
+        resp.raise_for_status()
+        data = resp.json()
+    except httpx.HTTPError as exc:
+        raise OssError(f"http_error: {exc}") from exc
+    except ValueError as exc:
+        raise OssError("bad_json") from exc
+    return parse_oss_response(data)
+
+
+def to_oss(
+    src_url: str,
+    src_type: str,
+    *,
+    settings: Optional[Settings] = None,
+    http_post: Callable[..., Any] = httpx.post,
+    env_file: str = ".env",
+) -> str:
+    """兜底包装：成功返回 cdn_url，任何失败（含空入参）返回原 src_url，永不抛。"""
+    if not src_url:
+        return src_url
+    try:
+        return upload_stream(src_url, src_type=src_type, settings=settings,
+                             http_post=http_post, env_file=env_file)
+    except Exception:
+        return src_url

creation_knowledge/integrations/search.py

@@ -0,0 +1,102 @@
+"""关键词搜索：query → content_id 列表（翻页 + 去重 + 截断）。
+
+接口（实测 2026-06-26，host = settings.crawler_base_url）：
+    POST /crawler/xiao_hong_shu/keyword
+      body{keyword, content_type:"图文"|"视频", sort_type:"综合", publish_time:"", cursor:""}
+      → {code:0, data:{has_more, next_cursor, data:[{id:<content_id>, note_card:{...预览...}}]}}
+
+搜索只给预览；全文/全图/视频仍需 crawler.fetch_post_detail 逐 id 取详情。
+parse_search_response 纯函数可离线测；search_keyword 负责 HTTP + 翻页。
+抖音 /keyword 实测 code:10000（疑需 cookie/鉴权）→ 抛 SearchError，由调用方跳过。
+"""
+from __future__ import annotations
+
+from typing import Any, Optional
+from urllib.parse import urljoin
+
+import httpx
+
+from creation_knowledge.config import Settings
+from creation_knowledge.integrations.crawler import RateLimiter
+
+SEARCH_PATHS = {
+    "xiaohongshu": "/crawler/xiao_hong_shu/keyword",
+    "douyin": "/crawler/dou_yin/keyword",   # TODO: 实测 code 10000，需 cookie/鉴权
+}
+
+
+class SearchError(RuntimeError):
+    pass
+
+
+def parse_search_response(response: Any, *, platform: str = "xiaohongshu") -> tuple[list[str], bool, str]:
+    """信封 {code,msg,data:{has_more,next_cursor,data:[...]}} → (content_ids, has_more, next_cursor)。
+    code!=0 抛 SearchError（如抖音 10000）。"""
+    if not isinstance(response, dict):
+        raise SearchError("bad_response: not a dict")
+    code = response.get("code")
+    if code not in (0, "0"):
+        raise SearchError(f"business_error: code={code} msg={response.get('msg')}")
+    data = response.get("data") or {}
+    items = data.get("data") or []
+    ids = [it.get("id") for it in items if isinstance(it, dict) and it.get("id")]
+    return ids, bool(data.get("has_more")), str(data.get("next_cursor") or "")
+
+
+def search_keyword(
+    keyword: str,
+    *,
+    platform: str = "xiaohongshu",
+    content_type: str = "图文",
+    sort_type: str = "综合",
+    limit: int = 5,
+    publish_time: str = "",
+    settings: Optional[Settings] = None,
+    http_client: Any = None,
+    rate_limiter: Optional[RateLimiter] = None,
+    env_file: str = ".env",
+    max_pages: int = 10,
+) -> list[str]:
+    """搜索 → content_id 列表（翻页直到够 limit 或 has_more=False；按 id 去重、截断 limit）。
+    复用 crawler.RateLimiter（'{platform}_keyword' bucket）。失败抛 SearchError。"""
+    settings = settings or Settings.from_env(env_file)
+    path = SEARCH_PATHS.get(platform)
+    if not path:
+        raise SearchError(f"unsupported platform: {platform}")
+    rate_limiter = rate_limiter or RateLimiter()
+
+    owns_client = http_client is None
+    client = http_client or httpx.Client()
+    out: list[str] = []
+    seen: set[str] = set()
+    cursor = ""
+    try:
+        for _ in range(max_pages):
+            rate_limiter.wait(f"{platform}_keyword")
+            url = urljoin(settings.crawler_base_url, path)
+            body = {"keyword": keyword, "content_type": content_type,
+                    "sort_type": sort_type, "publish_time": publish_time, "cursor": cursor}
+            try:
+                resp = client.post(url, json=body,
+                                   headers={"Content-Type": "application/json"},
+                                   timeout=settings.crawler_timeout)
+                resp.raise_for_status()
+                data = resp.json()
+            except httpx.HTTPError as exc:
+                raise SearchError(f"http_error: {exc}") from exc
+            except ValueError as exc:
+                raise SearchError("bad_json") from exc
+            ids, has_more, next_cursor = parse_search_response(data, platform=platform)
+            for cid in ids:
+                if cid not in seen:
+                    seen.add(cid)
+                    out.append(cid)
+                    if len(out) >= limit:
+                        return out
+            if not has_more or not next_cursor or next_cursor == cursor:
+                break
+            cursor = next_cursor
+    finally:
+        if owns_client:
+            client.close()
+    return out[:limit]

creation_knowledge/integrations/video_extract.py

@@ -83,53 +83,64 @@ def extract_video(
     timeout: float = 600.0,
     save_path: Optional[Path] = None,
     public_url: Optional[str] = None,
+    oss_video_url: Optional[str] = None,
 ) -> ExtractedContent:
     """对视频帖做原生整段提炼，就地写好 post.cards（段卡），返回 ExtractedContent。
 
     save_path/public_url 给定时：把下载到的整段 mp4 写到 save_path（单次下载，复用
     内存字节），并把每张段卡 url 设为 public_url（整段共用，前端按 start/end 加 #t 播片段）。
     两者默认 None＝不落盘、行为不变。
+
+    oss_video_url 给定时：直接把该公网直链喂模型（已验证 Gemini 可直读 OSS http 视频链），
+    跳过下载/落盘/base64；段卡 url 默认用它。默认 None＝走原 下载+base64 路径（fixture/兜底）。
     """
     key = settings.openrouter_api_key
     if not key:
         raise VideoExtractError("missing OPENROUTER_API_KEY")
 
-    # B站留接口位：DASH 分轨（video m4s + voice_data 音轨 m4s）需 curl 不跟随重定向下载
-    # 两轨 + ffmpeg 合并，单 URL 下载器拿不到音频。待接入；可先传入预合并的本地 video_path。
-    if post.platform == "bilibili" and not (video_path and os.path.exists(video_path)):
-        raise VideoExtractError(
-            "B站视频暂未接入：需取 voice_data 音轨 + ffmpeg 合并（见 数据接口与来源/接口台账/B站.md）")
-
-    # 1) 拿到 mp4 字节
-    if video_path and os.path.exists(video_path):
-        data = open(video_path, "rb").read()
-        logger.info("video_extract 用本地文件 %s (%d bytes)", video_path, len(data))
+    if oss_video_url:
+        # OSS 直喂：公网直链直接进 video_url，跳过下载+落盘+base64
+        media_url = oss_video_url
+        public_url = public_url or oss_video_url   # 段卡指向 OSS 直链
     else:
-        if not post.video_urls:
-            raise VideoExtractError(f"post {post.id} 无 video_urls 且未提供 video_path")
-        url = post.video_urls[0]
-        if post.platform == "douyin" and "ratio=" in url:  # 偏好较小码率控成本
-            url = re.sub(r"ratio=[^&]+", f"ratio={settings.douyin_ratio}", url)
-        dl = downloader or _default_download
-        try:
-            data = dl(url, post.platform)
-        except Exception as exc:
-            raise VideoExtractError(f"视频下载失败: {exc}") from exc
-    if not data:
-        raise VideoExtractError("视频字节为空")
-
-    # 1.5) 可选落盘：复用内存字节，单次下载即保存（避免二次下载/抖音链接过期）
-    if save_path is not None:
-        save_path.parent.mkdir(parents=True, exist_ok=True)
-        Path(save_path).write_bytes(data)
-        logger.info("video_extract 保存整段视频 %s (%d bytes)", save_path, len(data))
-
-    # 2) base64 + 3) OpenRouter 原生视频
-    data_url = "data:video/mp4;base64," + base64.b64encode(data).decode()
+        # B站留接口位：DASH 分轨（video m4s + voice_data 音轨 m4s）需 curl 不跟随重定向下载
+        # 两轨 + ffmpeg 合并，单 URL 下载器拿不到音频。待接入；可先传入预合并的本地 video_path。
+        if post.platform == "bilibili" and not (video_path and os.path.exists(video_path)):
+            raise VideoExtractError(
+                "B站视频暂未接入：需取 voice_data 音轨 + ffmpeg 合并（见 数据接口与来源/接口台账/B站.md）")
+
+        # 1) 拿到 mp4 字节
+        if video_path and os.path.exists(video_path):
+            data = open(video_path, "rb").read()
+            logger.info("video_extract 用本地文件 %s (%d bytes)", video_path, len(data))
+        else:
+            if not post.video_urls:
+                raise VideoExtractError(f"post {post.id} 无 video_urls 且未提供 video_path")
+            url = post.video_urls[0]
+            if post.platform == "douyin" and "ratio=" in url:  # 偏好较小码率控成本
+                url = re.sub(r"ratio=[^&]+", f"ratio={settings.douyin_ratio}", url)
+            dl = downloader or _default_download
+            try:
+                data = dl(url, post.platform)
+            except Exception as exc:
+                raise VideoExtractError(f"视频下载失败: {exc}") from exc
+        if not data:
+            raise VideoExtractError("视频字节为空")
+
+        # 1.5) 可选落盘：复用内存字节，单次下载即保存（避免二次下载/抖音链接过期）
+        if save_path is not None:
+            save_path.parent.mkdir(parents=True, exist_ok=True)
+            Path(save_path).write_bytes(data)
+            logger.info("video_extract 保存整段视频 %s (%d bytes)", save_path, len(data))
+
+        # 2) base64
+        media_url = "data:video/mp4;base64," + base64.b64encode(data).decode()
+
+    # 3) OpenRouter 原生视频
     prompt = load_prompt("extract_video").format()
     body = {"model": settings.video_model, "messages": [{"role": "user", "content": [
         {"type": "text", "text": prompt},
-        {"type": "video_url", "video_url": {"url": data_url}},
+        {"type": "video_url", "video_url": {"url": media_url}},
     ]}]}
     try:
         resp = http_post(

pyproject.toml

@@ -11,6 +11,7 @@ dependencies = [
   "uvicorn>=0.30.0",
   "pytest>=8.2.0",
   "imageio-ffmpeg>=0.4.9",
+  "numpy>=1.26",
 ]
 
 [tool.pytest.ini_options]

scripts/decompose.py

@@ -13,6 +13,8 @@ from __future__ import annotations
 import json
 import os
 import re
+import sys
+import time
 from pathlib import Path
 
 from creation_knowledge.config import Settings
@@ -20,6 +22,8 @@ from creation_knowledge.integrations import video_extract
 from creation_knowledge.integrations.crawler import fetch_post_detail, parse_detail_response
 from creation_knowledge.integrations.extractor import GeminiExtractor
 from creation_knowledge.integrations.llm import chat_json
+from creation_knowledge.integrations.oss import to_oss, upload_stream
+from creation_knowledge.integrations.search import search_keyword
 from creation_knowledge.prompts import load_prompt
 from scripts.scope_link import ScopeLinker
 
@@ -56,6 +60,12 @@ SOURCES = [
     {"cid": "7589257893544165455", "platform": "douyin", "from": "live"},        # 抖音视频（倒数第2）
     {"cid": "6a33655e000000000f0055af", "platform": "xiaohongshu", "from": "live"},  # 无知识 Voodoo（最后）
 ]
+# 入口二：按关键词搜索召回（与 SOURCES 并存）。跑法：python scripts/decompose.py queries
+# 每条 {query, platform, content_type, limit}；limit 缺省取 settings.search_default_limit。
+# 抖音 /keyword 实测 code:10000（需鉴权），本迭代只上小红书。
+QUERIES = [
+    {"query": "分镜脚本", "platform": "xiaohongshu", "content_type": "图文", "limit": 5},
+]
 SRC2CN = {"substance": "实质", "form": "形式", "feeling": "感受", "effect": "作用", "intent": "意图"}
 TYPE2ATTR = {"how": "how", "what": "what", "why": "why"}
 CSTAGE = {"定向", "构思", "结构", "成文", "打磨"}  # 创作阶段受控 5 值
@@ -77,40 +87,37 @@ def load_post(src: dict, settings: Settings):
 
 
 # ---------- ① 读懂（图文/视频分流） ----------
-def _download_images(post) -> list[str]:
-    """live 图文帖：把 CDN 图下载到本地 /data（避开小红书防盗链），返回本地公开路径；
-    单张失败则退回 CDN 直链（至少有 url）。复用 video_extract 的下载器（iOS UA + 平台 Referer）。"""
-    base = DATA / "xiaohongshu" / post.id
-    base.mkdir(parents=True, exist_ok=True)
-    out = []
-    for n, url in enumerate(post.image_urls, 1):
-        dst = base / f"image_{n}.webp"
-        pub = f"/data/demo/xiaohongshu/{post.id}/image_{n}.webp"
-        try:
-            if not dst.exists():
-                dst.write_bytes(video_extract._default_download(url, post.platform))
-            out.append(pub)
-        except Exception:
-            out.append(url)
-    return out
-
-
 def read_one(src: dict, post, settings: Settings, extractor: GeminiExtractor):
-    cid = src["cid"]
-    if post.video_urls:  # 视频帖：下载 mp4 + 原生整段提炼
-        save = DATA / "douyin" / f"dy_{cid}" / "video.mp4"
-        pub = f"/data/demo/douyin/dy_{cid}/video.mp4"
-        ec = video_extract.extract_video(post, settings=settings, save_path=save, public_url=pub)
+    live = src["from"] == "live"
+    if post.video_urls:  # 视频帖
+        oss_url = ""
+        if live:
+            try:  # live：转存视频到 OSS 拿公网直链直喂（已验证 Gemini 可直读）；失败→显式回退
+                oss_url = upload_stream(post.video_urls[0], src_type="video", settings=settings)
+            except Exception as exc:
+                print(f"  ⚠ 视频 OSS 转存失败，回退下载+base64：{exc}")
+        if oss_url:  # 转存成功：OSS 直链直喂，跳过下载+base64
+            ec = video_extract.extract_video(post, settings=settings, oss_video_url=oss_url, public_url=oss_url)
+            pub = oss_url
+        else:        # fixture / 转存失败：下载 mp4 + base64 兜底（落盘按平台分目录，post.id 已含平台前缀）
+            save = DATA / post.platform / post.id / "video.mp4"
+            pub = f"/data/demo/{post.platform}/{post.id}/video.mp4"
+            ec = video_extract.extract_video(post, settings=settings, save_path=save, public_url=pub)
         media = {"type": "video", "video_url": pub, "images": []}
         cmap = {c.index: c.content for c in ec.cards}
         cards = [{"index": c.index, "content": cmap.get(c.index, ""), "video_url": pub,
                   "start": c.start, "end": c.end} for c in post.cards]  # 段卡：时间戳 + 读到的内容
     else:                # 图文帖：读图
-        ec = extractor.extract(post)
-        if src["from"] == "fixture":
+        if live:  # live 帖：每张 OSS 转存绕防盗链，cdn_url 写回 post 再喂 extractor（直读 card.url）
+            imgs = [to_oss(u, "image", settings=settings) for u in post.image_urls]
+            post.image_urls = imgs
+            for c in post.cards:
+                if c.kind == "image" and 1 <= c.index <= len(imgs):
+                    c.url = imgs[c.index - 1]
+            ec = extractor.extract(post)
+        else:     # fixture：读图后用预下载的本地 /data 路径展示
+            ec = extractor.extract(post)
             imgs = [f"/data/demo/xiaohongshu/{post.id}/image_{n}.webp" for n in range(1, len(post.image_urls) + 1)]
-        else:
-            imgs = _download_images(post)  # live 帖：下载 CDN 图到本地 /data，避开防盗链
         media = {"type": "image", "video_url": None, "images": imgs}
         cmap = {c.index: c.content for c in ec.cards}
         cards = [{"index": n, "content": cmap.get(n, ""), "image_url": imgs[n - 1] if n <= len(imgs) else None}
@@ -433,58 +440,140 @@ def build_payload(post, k: dict, how_titles: dict | None = None) -> dict:
             "scopes": scopes, "custom_ext": ext}
 
 
-def main() -> None:
-    settings = Settings.from_env()
-    extractor = GeminiExtractor.from_env()
-    linker = ScopeLinker()
-    posts_out, payloads = [], []
-    for src in SOURCES:
-        cid = src["cid"]
-        print(f"\n=== {src['platform']} {cid[:10]} ({src['from']}) ===")
+def process_one(src: dict, settings: Settings, extractor: GeminiExtractor,
+                linker: ScopeLinker) -> tuple[dict, list[dict]]:
+    """跑通一帖：取数→读懂→闸→拆颗→作用域→组装。返回 (post_out_entry, payloads)。
+
+    从 main 循环体原样抽出（零行为变更）；SOURCES 与 QUERIES 两条入口共用。"""
+    cid = src["cid"]
+    print(f"\n=== {src['platform']} {cid[:10]} ({src['from']}) ===")
+    try:
+        post = load_post(src, settings)
+        read, is_empty, media, cards = read_one(src, post, settings, extractor)
+    except Exception as exc:
+        print(f"  ✗ 取数/读懂失败：{exc}")
+        return ({"post_id": cid, "source_id": cid, "title": f"(取数失败 {cid})",
+                 "platform": src["platform"], "url": "", "media": {"type": "image", "images": []},
+                 "cards": [], "error": str(exc)[:200], "knowledges": []}, [])
+    meta = {"post_id": cid, "source_id": post.id, "title": post.title or "",
+            "platform": post.platform, "url": post.url, "media": media, "cards": cards}
+    if is_empty:  # ① 总闸：纯展示/无可提取
+        print(f"  ① 读懂 {len(read)} 字 → ① 判纯展示/无知识，跳过")
+        return ({**meta, "no_knowledge": True, "knowledges": []}, [])
+    in_scope, gate_why = creation_gate(read)  # ①.5 创作判定闸（创作 vs 制作 vs 越界）
+    if not in_scope:
+        print(f"  ① 读懂 {len(read)} 字 → ①.5 闸判【非创作】（{gate_why}）→ 整帖排除")
+        return ({**meta, "no_knowledge": True, "knowledges": []}, [])
+    print(f"  ① 读懂 {len(read)} 字（{media['type']}）· ①.5 闸：创作（{gate_why}）")
+    knowledges = drop_fake_whys(fix_fake_hows(shape(post, read)))   # ② 拆颗 → ②.5 假how根治 → ②.7 why轻闸
+    how_ids = {k.get("id") for k in knowledges if k.get("type") == "how"}
+    how_titles = {k.get("id"): k.get("title") for k in knowledges if k.get("type") == "how"}
+    for k in knowledges:
+        k["业务阶段"] = [b for b in (k.get("业务阶段") or []) if b in ("灵感", "选题", "脚本")]  # 守卫：只留合法业务阶段
+        if k.get("type") == "what" and k.get("kind") not in KINDS:  # 守卫：what.kind 受控值，LLM 偶发变体归一
+            k["kind"] = KIND_FIX.get((k.get("kind") or "").strip(), k.get("kind"))
+        for s in k.get("steps", []):  # 守卫：创作阶段只留合法 5 值，非法（如"定稿/输出"）丢弃
+            if s.get("创作阶段") not in CSTAGE:
+                s["创作阶段"] = None
+        if k.get("role") == "组件" and (k.get("parent") or {}).get("how_id") not in how_ids:  # 守卫：组件 parent 必指向同帖 how
+            k["role"] = "主"; k["parent"] = None
+    print(f"  ② {len(knowledges)} 颗：" + ", ".join(f"{k.get('type')}/{k.get('role')}" for k in knowledges))
+    apply_scopes(knowledges, nounify_scopes(scope_candidates(knowledges)), linker)  # ③名词化+①兜底 → 定位
+    print("  ③⑤ 作用域定位 + 组装")
+    return ({**meta, "knowledges": knowledges},
+            [build_payload(post, k, how_titles) for k in knowledges])
+
+
+# ---------- 入口二：query 搜索召回 + run 隔离产出 ----------
+def expand_queries(queries: list[dict], settings: Settings) -> list[dict]:
+    """逐 query 搜索 → content_id；跨 query 按 id 去重。搜索失败跳过该 query（不阻断整批）。"""
+    seen: set[str] = set()
+    out: list[dict] = []
+    for q in queries:
+        platform = q.get("platform", "xiaohongshu")
         try:
-            post = load_post(src, settings)
-            read, is_empty, media, cards = read_one(src, post, settings, extractor)
+            ids = search_keyword(
+                q["query"], platform=platform,
+                content_type=q.get("content_type") or settings.search_content_type,
+                sort_type=q.get("sort_type") or settings.search_sort_type,
+                limit=q.get("limit") or settings.search_default_limit,
+                settings=settings)
         except Exception as exc:
-            print(f"  ✗ 取数/读懂失败：{exc}")
-            posts_out.append({"post_id": cid, "source_id": cid, "title": f"(取数失败 {cid})",
-                              "platform": src["platform"], "url": "", "media": {"type": "image", "images": []},
-                              "cards": [], "error": str(exc)[:200], "knowledges": []})
+            print(f"  ✗ 搜索失败 [{q.get('query')}]：{exc}")
             continue
-        meta = {"post_id": cid, "source_id": post.id, "title": post.title or "",
-                "platform": post.platform, "url": post.url, "media": media, "cards": cards}
-        if is_empty:  # ① 总闸：纯展示/无可提取
-            print(f"  ① 读懂 {len(read)} 字 → ① 判纯展示/无知识，跳过")
-            posts_out.append({**meta, "no_knowledge": True, "knowledges": []})
-            continue
-        in_scope, gate_why = creation_gate(read)  # ①.5 创作判定闸（创作 vs 制作 vs 越界）
-        if not in_scope:
-            print(f"  ① 读懂 {len(read)} 字 → ①.5 闸判【非创作】（{gate_why}）→ 整帖排除")
-            posts_out.append({**meta, "no_knowledge": True, "knowledges": []})
-            continue
-        print(f"  ① 读懂 {len(read)} 字（{media['type']}）· ①.5 闸：创作（{gate_why}）")
-        knowledges = drop_fake_whys(fix_fake_hows(shape(post, read)))   # ② 拆颗 → ②.5 假how根治 → ②.7 why轻闸
-        how_ids = {k.get("id") for k in knowledges if k.get("type") == "how"}
-        how_titles = {k.get("id"): k.get("title") for k in knowledges if k.get("type") == "how"}
-        for k in knowledges:
-            k["业务阶段"] = [b for b in (k.get("业务阶段") or []) if b in ("灵感", "选题", "脚本")]  # 守卫：只留合法业务阶段
-            if k.get("type") == "what" and k.get("kind") not in KINDS:  # 守卫：what.kind 受控值，LLM 偶发变体归一
-                k["kind"] = KIND_FIX.get((k.get("kind") or "").strip(), k.get("kind"))
-            for s in k.get("steps", []):  # 守卫：创作阶段只留合法 5 值，非法（如"定稿/输出"）丢弃
-                if s.get("创作阶段") not in CSTAGE:
-                    s["创作阶段"] = None
-            if k.get("role") == "组件" and (k.get("parent") or {}).get("how_id") not in how_ids:  # 守卫：组件 parent 必指向同帖 how
-                k["role"] = "主"; k["parent"] = None
-        print(f"  ② {len(knowledges)} 颗：" + ", ".join(f"{k.get('type')}/{k.get('role')}" for k in knowledges))
-        apply_scopes(knowledges, nounify_scopes(scope_candidates(knowledges)), linker)  # ③名词化+①兜底 → 定位
-        print("  ③⑤ 作用域定位 + 组装")
-        posts_out.append({**meta, "knowledges": knowledges})
-        payloads += [build_payload(post, k, how_titles) for k in knowledges]
-    suffix = os.environ.get("OUT", "")  # OUT=_v2 → 写 frameworks_v2.json，不覆盖原版
-    (ROOT / f"web/frameworks{suffix}.json").write_text(
-        json.dumps({"count": len(posts_out), "posts": posts_out}, ensure_ascii=False, indent=1), encoding="utf-8")
-    (ROOT / f"web/payloads{suffix}.json").write_text(
+        new = 0
+        for cid in ids:
+            if cid in seen:
+                continue
+            seen.add(cid)
+            out.append({"cid": cid, "platform": platform, "from": "live", "query": q["query"]})
+            new += 1
+        print(f"  🔎 [{q['query']}] 命中 {len(ids)} → 新增 {new}（去重后）")
+    return out
+
+
+def slugify(text: str) -> str:
+    """run 文件名：保留中英文字与数字，其余压成 -。空则 'run'。"""
+    s = re.sub(r"[^\w一-鿿]+", "-", text or "").strip("-").lower()
+    return s or "run"
+
+
+def write_run(posts_out: list[dict], payloads: list[dict], queries: list[dict]) -> str:
+    """写 web/runs/<slug>.json + <slug>.payloads.json + 追加 web/runs/index.json 清单。
+    同名 slug 已存在则加时间戳保留历史。返回最终 slug。"""
+    runs_dir = ROOT / "web" / "runs"
+    runs_dir.mkdir(parents=True, exist_ok=True)
+    slug = slugify("-".join(q["query"] for q in queries))
+    if (runs_dir / f"{slug}.json").exists():
+        slug = f"{slug}-{int(time.time())}"
+    (runs_dir / f"{slug}.json").write_text(
+        json.dumps({"count": len(posts_out), "posts": posts_out, "queries": queries},
+                   ensure_ascii=False, indent=1), encoding="utf-8")
+    (runs_dir / f"{slug}.payloads.json").write_text(
         json.dumps(payloads, ensure_ascii=False, indent=2), encoding="utf-8")
-    print(f"\nwrote {len(posts_out)} posts, {len(payloads)} payloads → web/frameworks{suffix}.json")
+    idx_path = runs_dir / "index.json"
+    try:
+        idx = json.loads(idx_path.read_text("utf-8")) if idx_path.exists() else []
+    except Exception:
+        idx = []
+    idx = [r for r in idx if r.get("slug") != slug]  # 同 slug 去重
+    idx.insert(0, {"slug": slug, "queries": [q["query"] for q in queries],
+                   "count": len(posts_out), "created_at": int(time.time())})
+    idx_path.write_text(json.dumps(idx, ensure_ascii=False, indent=1), encoding="utf-8")
+    print(f"\nwrote {len(posts_out)} posts, {len(payloads)} payloads → web/runs/{slug}.json")
+    return slug
+
+
+def _run(srcs: list[dict], settings: Settings, extractor: GeminiExtractor,
+         linker: ScopeLinker) -> tuple[list[dict], list[dict]]:
+    posts_out, payloads = [], []
+    for src in srcs:
+        entry, pl = process_one(src, settings, extractor, linker)
+        posts_out.append(entry)
+        payloads += pl
+    return posts_out, payloads
+
+
+def main() -> None:
+    settings = Settings.from_env()
+    extractor = GeminiExtractor.from_env()
+    linker = ScopeLinker()
+    mode = sys.argv[1] if len(sys.argv) > 1 else "sources"
+    if mode == "queries":  # 入口二：搜索召回 → run 隔离产出（不动 frameworks.json 样本）
+        srcs = expand_queries(QUERIES, settings)
+        if not srcs:
+            print("没有召回到任何帖子，结束。")
+            return
+        posts_out, payloads = _run(srcs, settings, extractor, linker)
+        write_run(posts_out, payloads, QUERIES)
+    else:                  # 入口一（默认）：写死 SOURCES → web/frameworks.json
+        posts_out, payloads = _run(SOURCES, settings, extractor, linker)
+        suffix = os.environ.get("OUT", "")  # OUT=_v2 → 写 frameworks_v2.json，不覆盖原版
+        (ROOT / f"web/frameworks{suffix}.json").write_text(
+            json.dumps({"count": len(posts_out), "posts": posts_out}, ensure_ascii=False, indent=1), encoding="utf-8")
+        (ROOT / f"web/payloads{suffix}.json").write_text(
+            json.dumps(payloads, ensure_ascii=False, indent=2), encoding="utf-8")
+        print(f"\nwrote {len(posts_out)} posts, {len(payloads)} payloads → web/frameworks{suffix}.json")
 
 
 if __name__ == "__main__":

tests/fixtures/xhs_search_分镜脚本.json

@@ -0,0 +1,36 @@
+{
+  "code": 0,
+  "msg": null,
+  "data": {
+    "has_more": true,
+    "next_cursor": "2",
+    "data": [
+      {
+        "id": "6a2bce890000000006034be4",
+        "model_type": "note",
+        "note_card": {
+          "type": "normal",
+          "display_title": "接定制分镜头，有成品分镜。",
+          "desc": "白菜价出分镜成品，分镜头加剧本。",
+          "image_list": [
+            {"image_url": "https://ci.xiaohongshu.com/a?imageView2/2/w/1080/format/webp"}
+          ],
+          "interact_info": {"liked_count": 26}
+        }
+      },
+      {
+        "id": "68282529000000002100e28c",
+        "model_type": "note",
+        "note_card": {
+          "type": "normal",
+          "display_title": "分镜脚本怎么写",
+          "desc": "新手向分镜脚本教程。",
+          "image_list": [
+            {"image_url": "https://ci.xiaohongshu.com/b?imageView2/2/w/1080/format/webp"}
+          ],
+          "interact_info": {"liked_count": 88}
+        }
+      }
+    ]
+  }
+}

tests/test_decompose_helpers.py

@@ -0,0 +1,51 @@
+"""decompose 入口二的纯逻辑离线测：slugify + expand_queries 跨 query 去重。"""
+from __future__ import annotations
+
+import scripts.decompose as d
+from creation_knowledge.config import PgConfig, Settings
+
+
+def _settings() -> Settings:
+    return Settings(
+        pg=PgConfig(host="h", port=5432, user="u", password="p", database="d"),
+        crawler_base_url="x", crawler_key="", crawler_timeout=30,
+        video_model="m", gemini_api_key="",
+        openrouter_base_url="b", openrouter_api_key="k",
+        llm_model="m", max_cards=12, frames_dir="f", douyin_ratio="540p", data_dir="")
+
+
+def test_slugify():
+    assert d.slugify("分镜脚本") == "分镜脚本"      # 中文保留
+    assert d.slugify("A B!c") == "a-b-c"            # 空格/标点→- 且小写
+    assert d.slugify("") == "run"                   # 空→run
+    assert d.slugify("!!!") == "run"                # 全标点→run
+
+
+def test_expand_queries_dedup(monkeypatch):
+    ret = {"q1": ["a", "b"], "q2": ["b", "c"]}  # q1/q2 共享 id "b"
+
+    def fake_search(keyword, **kw):
+        return ret[keyword]
+
+    monkeypatch.setattr(d, "search_keyword", fake_search)
+    srcs = d.expand_queries(
+        [{"query": "q1", "platform": "xiaohongshu"},
+         {"query": "q2", "platform": "xiaohongshu"}], _settings())
+
+    assert [s["cid"] for s in srcs] == ["a", "b", "c"]          # b 去重，只出现一次
+    assert all(s["from"] == "live" for s in srcs)
+    assert srcs[0]["query"] == "q1" and srcs[2]["query"] == "q2"  # b 归属首次命中的 q1
+
+
+def test_expand_queries_skips_failed(monkeypatch):
+    def fake_search(keyword, **kw):
+        if keyword == "bad":
+            raise RuntimeError("search down")
+        return ["x"]
+
+    monkeypatch.setattr(d, "search_keyword", fake_search)
+    srcs = d.expand_queries(
+        [{"query": "bad", "platform": "xiaohongshu"},
+         {"query": "ok", "platform": "xiaohongshu"}], _settings())
+
+    assert [s["cid"] for s in srcs] == ["x"]  # bad 整条跳过，不阻断 ok

tests/test_oss.py

@@ -0,0 +1,90 @@
+"""OSS 转存离线测试：纯 parser + upload_stream + to_oss 兜底（注入 http_post）。"""
+from __future__ import annotations
+
+import httpx
+import pytest
+
+from creation_knowledge.config import PgConfig, Settings
+from creation_knowledge.integrations.oss import (
+    OssError, parse_oss_response, to_oss, upload_stream,
+)
+
+
+def _settings() -> Settings:
+    return Settings(
+        pg=PgConfig(host="h", port=5432, user="u", password="p", database="d"),
+        crawler_base_url="x", crawler_key="", crawler_timeout=30,
+        video_model="m", gemini_api_key="",
+        openrouter_base_url="b", openrouter_api_key="k",
+        llm_model="m", max_cards=12, frames_dir="f", douyin_ratio="540p", data_dir="",
+        oss_upload_url="http://oss.test/upload_stream", oss_upload_timeout_seconds=60)
+
+
+class _Resp:
+    def __init__(self, payload): self._p = payload
+    def raise_for_status(self): return None
+    def json(self): return self._p
+
+
+def test_parse_ok():
+    assert parse_oss_response(
+        {"status": 0, "oss_object": {"cdn_url": "https://res/x.mp4"}}) == "https://res/x.mp4"
+
+
+def test_parse_status_error():
+    with pytest.raises(OssError):
+        parse_oss_response({"status": 1, "msg": "fail"})
+
+
+def test_parse_missing_cdn():
+    with pytest.raises(OssError):
+        parse_oss_response({"status": 0, "oss_object": {}})
+
+
+def test_upload_stream_posts_and_returns_cdn():
+    captured = {}
+
+    def post(url, json=None, headers=None, timeout=None):
+        captured["url"] = url
+        captured["json"] = json
+        return _Resp({"status": 0, "oss_object": {"cdn_url": "https://res/y.webp"}})
+
+    cdn = upload_stream("http://src/y.webp", src_type="image",
+                        settings=_settings(), http_post=post)
+    assert cdn == "https://res/y.webp"
+    assert captured["url"] == "http://oss.test/upload_stream"
+    assert captured["json"] == {"src_url": "http://src/y.webp", "src_type": "image"}
+
+
+def test_upload_stream_bad_src_type():
+    with pytest.raises(OssError):
+        upload_stream("http://src/a", src_type="audio", settings=_settings())
+
+
+def test_to_oss_fallback_on_http_error():
+    def post(url, json=None, headers=None, timeout=None):
+        raise httpx.ConnectError("boom")
+
+    # 失败返回原 url，永不抛
+    assert to_oss("http://src/z.mp4", "video",
+                  settings=_settings(), http_post=post) == "http://src/z.mp4"
+
+
+def test_to_oss_fallback_on_business_error():
+    def post(url, json=None, headers=None, timeout=None):
+        return _Resp({"status": 500, "msg": "boom"})
+
+    assert to_oss("http://src/z.mp4", "video",
+                  settings=_settings(), http_post=post) == "http://src/z.mp4"
+
+
+def test_to_oss_success():
+    def post(url, json=None, headers=None, timeout=None):
+        return _Resp({"status": 0, "oss_object": {"cdn_url": "https://res/z.mp4"}})
+
+    assert to_oss("http://src/z.mp4", "video",
+                  settings=_settings(), http_post=post) == "https://res/z.mp4"
+
+
+def test_to_oss_empty_url_passthrough():
+    assert to_oss("", "image", settings=_settings()) == ""

tests/test_search.py

@@ -0,0 +1,90 @@
+"""关键词搜索离线测试：纯 parser + search_keyword（去重/翻页/截断/SearchError）。"""
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+from creation_knowledge.config import PgConfig, Settings
+from creation_knowledge.integrations.crawler import RateLimiter
+from creation_knowledge.integrations.search import (
+    SearchError, parse_search_response, search_keyword,
+)
+
+FIX = Path(__file__).parent / "fixtures"
+
+
+def _settings() -> Settings:
+    return Settings(
+        pg=PgConfig(host="h", port=5432, user="u", password="p", database="d"),
+        crawler_base_url="http://crawler.test", crawler_key="", crawler_timeout=30,
+        video_model="m", gemini_api_key="",
+        openrouter_base_url="b", openrouter_api_key="k",
+        llm_model="m", max_cards=12, frames_dir="f", douyin_ratio="540p", data_dir="")
+
+
+def _no_wait() -> RateLimiter:
+    return RateLimiter(min_interval_seconds=0.0)
+
+
+class _Resp:
+    def __init__(self, payload): self._p = payload
+    def raise_for_status(self): return None
+    def json(self): return self._p
+
+
+class _FakeClient:
+    """按调用次序返回预置 payload；记录每次 post 的 body。"""
+    def __init__(self, pages): self.pages = list(pages); self.calls = []; self.closed = False
+    def post(self, url, json=None, headers=None, timeout=None):
+        self.calls.append(json)
+        return _Resp(self.pages.pop(0))
+    def close(self): self.closed = True
+
+
+def test_parse_from_fixture():
+    resp = json.loads((FIX / "xhs_search_分镜脚本.json").read_text("utf-8"))
+    ids, has_more, cursor = parse_search_response(resp)
+    assert ids == ["6a2bce890000000006034be4", "68282529000000002100e28c"]
+    assert has_more is True and cursor == "2"
+
+
+def test_parse_business_error_raises():
+    with pytest.raises(SearchError):
+        parse_search_response({"code": 10000, "msg": "未知错误", "data": None})
+
+
+def test_search_dedup_and_limit():
+    # page1 含重复 id，page2 提供更多；limit=3 → 截断且去重
+    page1 = {"code": 0, "data": {"has_more": True, "next_cursor": "2",
+             "data": [{"id": "a"}, {"id": "a"}, {"id": "b"}]}}
+    page2 = {"code": 0, "data": {"has_more": True, "next_cursor": "3",
+             "data": [{"id": "b"}, {"id": "c"}, {"id": "d"}]}}
+    client = _FakeClient([page1, page2])
+    out = search_keyword("分镜", settings=_settings(), http_client=client,
+                         rate_limiter=_no_wait(), limit=3)
+    assert out == ["a", "b", "c"]
+
+
+def test_search_stops_on_no_more():
+    page1 = {"code": 0, "data": {"has_more": False, "next_cursor": "",
+             "data": [{"id": "a"}, {"id": "b"}]}}
+    client = _FakeClient([page1])
+    out = search_keyword("分镜", settings=_settings(), http_client=client,
+                         rate_limiter=_no_wait(), limit=10)
+    assert out == ["a", "b"]   # has_more=False → 不再翻页，不报错
+    assert client.calls[0]["keyword"] == "分镜"
+
+
+def test_search_business_error_raises():
+    client = _FakeClient([{"code": 10000, "msg": "未知错误", "data": None}])
+    with pytest.raises(SearchError):
+        search_keyword("x", settings=_settings(), http_client=client,
+                       rate_limiter=_no_wait())
+
+
+def test_search_unsupported_platform():
+    with pytest.raises(SearchError):
+        search_keyword("x", platform="bilibili", settings=_settings(),
+                       rate_limiter=_no_wait())

tests/test_video_extract.py

@@ -86,3 +86,28 @@ def test_video_extract_saves_and_sets_url(tmp_path):
 
     assert save_path.exists() and save_path.read_bytes() == vf.read_bytes()
     assert all(c.url == "/data/B/douyin/dy_1/video.mp4" for c in post.cards)
+
+
+def test_extract_video_oss_direct_feed():
+    """传 oss_video_url：不下载、不 base64，请求体直接用该 http 直链；段卡 url 指向它。"""
+    fake, captured = _fake_post()
+    downloads = []
+
+    def spy_downloader(url, platform):
+        downloads.append(url)
+        return b"should-not-be-called"
+
+    post = Post(id="dy_2", platform="douyin", url="u", content_id="2",
+                content_type="video", video_urls=["http://cdn/raw.mp4?sig=tmp"])
+
+    out = extract_video(post, settings=_settings(), http_post=fake,
+                        downloader=spy_downloader,
+                        oss_video_url="https://res.cybertogether.net/crawler/video/x.mp4")
+
+    assert downloads == []  # 未触发任何下载
+    parts = captured["json"]["messages"][0]["content"]
+    vparts = [p for p in parts if p["type"] == "video_url"]
+    assert vparts and vparts[0]["video_url"]["url"] == "https://res.cybertogether.net/crawler/video/x.mp4"
+    assert not vparts[0]["video_url"]["url"].startswith("data:")  # 不是 base64
+    assert out.cards and len(post.cards) == 2
+    assert all(c.url == "https://res.cybertogether.net/crawler/video/x.mp4" for c in post.cards)

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web/index.html

@@ -352,6 +352,8 @@ function App(){
   const [items,setItems]=useState([]);
   const [sel,setSel]=useState(0);
   const [scope,setScope]=useState(null);
+  const [runs,setRuns]=useState([]);     // web/runs/index.json 清单（搜索批次）
+  const [run,setRun]=useState(null);     // 选中的 run slug；null=默认样本 frameworks.json
   const [pm,setPm]=useState(null);
   const [json,setJson]=useState(null);
   const [zoom,setZoom]=useState(null);
@@ -359,8 +361,13 @@ function App(){
   const [hl,setHl]=useState(false);
   const [gloss,setGloss]=useState(false);
   const [cstage,setCstage]=useState(false);
-  useEffect(()=>{Promise.all([j("/frameworks.json"),j("/payloads.json").catch(()=>[])]).then(([f,pl])=>{
-    setItems((f.posts||[]).map(p=>({...p,payloads:(pl||[]).filter(x=>(x.source||{}).id===(p.source_id||("xhs_"+p.post_id)))})));});},[]);
+  useEffect(()=>{j("/runs/index.json").then(r=>setRuns(Array.isArray(r)?r:[])).catch(()=>setRuns([]));},[]);
+  useEffect(()=>{
+    const fw=run?("/runs/"+run+".json"):"/frameworks.json";
+    const pls=run?("/runs/"+run+".payloads.json"):"/payloads.json";
+    Promise.all([j(fw),j(pls).catch(()=>[])]).then(([f,pl])=>{
+      setSel(0);setScope(null);
+      setItems((f.posts||[]).map(p=>({...p,payloads:(pl||[]).filter(x=>(x.source||{}).id===(p.source_id||("xhs_"+p.post_id)))})));});},[run]);
   useEffect(()=>{document.body.classList.toggle("hl",hl);},[hl]);
   function openPrompt(key){
     const st=STEPMAP[key]; if(!st) return;
@@ -383,6 +390,12 @@ function App(){
   return <div className="app">
     <header className="top">
       <h1>创作知识的解构</h1>
+      {runs.length>0 && <div className="picker"><span className="lab">批次：</span>
+        <select className="runsel" value={run||""} onChange={e=>setRun(e.target.value||null)}>
+          <option value="">样本（frameworks）</option>
+          {runs.map((r,i)=><option key={i} value={r.slug}>{(r.queries||[]).join("+")||r.slug}（{r.count}帖）</option>)}
+        </select>
+      </div>}
       <div className="picker"><span className="lab">帖子：</span>
         {items.map((x,i)=><div key={i} className={"pill"+(sel===i?" on":"")} onClick={()=>{setSel(i);setScope(null);}}>{x.title.slice(0,16)}<span className="n">{(x.knowledges||[]).length}颗</span></div>)}
       </div>

开发文档/产品文档.md

@@ -0,0 +1,102 @@
+# 创作知识解构 · 产品文档
+
+> 配套：本目录《技术文档.md》（工程实现）、《创作知识拆解框架.md》（方法论设计依据）。
+> 本文讲**产品全貌 + 下一迭代要补的新能力**，不讲代码细节。
+
+---
+
+## 一、产品是什么
+
+**一句话**：把一篇创作帖（小红书 / 抖音的图文或视频教程），自动拆成 **N 颗**可被下游单独检索复用的「创作知识」，每颗按自己的类型成形，收敛成入库请求体，并在前端按类型可视化。
+
+- **输入**：一篇创作内容（正文 + 图 / 视频）。
+- **输出**：一个或多个 ingest payload（**一颗知识 = 一个 payload**，一帖通常 1–N 颗，共享同一 `source.id`），落到本地 JSON 供前端查看、供下游入库检索。
+
+### 单位：颗
+
+「颗」= 一条独立可交付、能被下游**单独检索复用**的创作知识。一帖 → N 颗（可混类型）。
+
+### 三类对等 lane（无主从）
+
+| 类型 | 它是什么 | 成形 |
+|---|---|---|
+| **How 工序** | 不可跳步地把输入做成成品 | 目标 + 有序步骤（输入/方法/产出） |
+| **What 构成** | 界定 / 列举 / 分类某创作对象 | 概要 + kind（子集/多维关系/序列）+ 扁平条目 |
+| **Why 原理** | 解释某做法 / 判断背后的道理 | 一段「阐述」（是什么 + 为什么，忠实整合原帖） |
+
+> 边界：只收**指导「怎么想 / 怎么判断 / 怎么搭框架」**的创作知识；剔除**「怎么操作设备软件」**的纯工艺（制作：器材/打光/剪辑/调色/导出）。
+
+---
+
+## 二、给谁用、解决什么
+
+**直接使用者**：内容创作知识库的建设方 —— 把散落在帖子里的创作方法论结构化、标签化、可检索化。
+
+**下游消费者**：「脚本构建主 Agent」的取数 subagent。主 Agent 分阶段构建脚本（起步 → 产生脉络 → 产生元素 → 填入脉络），各阶段**只读检索**复用这些知识颗。
+
+> ⚠️ 两种「阶段 / 作用域」别混：
+> - 我们解构时打的**作用域**（实质 / 形式 / 感受 / 作用 / 意图，5 棵分类树）—— 描述这颗知识本身。
+> - 下游消费时的**消费 scope**（这条知识在脚本构建哪个阶段有用）—— 是另一回事。
+
+---
+
+## 三、端到端用户流
+
+### 现状（已落地）
+
+```
+人工选定帖子 ID（写死 9 个） → 取数读懂 → 拆颗+判类型+成形+打标签 → 作用域定位 → 组装 payload
+  → web/frameworks.json + web/payloads.json → 前端一页多颗、按类型渲染
+```
+
+痛点：**入口靠人肉填 content_id**，无法按主题量产；live 帖媒体受平台防盗链限制，要本地下载中转。
+
+### 新能力（下一迭代）
+
+```
+输入关键词(query) → 自动搜索召回一批帖 → 逐帖取数
+  → OSS 转存帖子媒体（图/视频）拿到公网 CDN 直链 → 直接把直链丢给模型分析
+  → 同一套拆解管线 → 结果落到独立的 run JSON（web/runs/<slug>.json）
+  → 前端「run 选择器」切换查看不同主题批次
+```
+
+从「我知道是哪几篇 → 填 ID」升级为「我想要某主题的创作知识 → 给关键词 → 自动产出一批」。
+
+---
+
+## 四、新能力的价值
+
+| 维度 | 现状 | 新能力后 |
+|---|---|---|
+| **召回** | 手工填 content_id，一次几篇 | 关键词搜索，按主题批量召回 |
+| **媒体可达** | 本地下载躲防盗链 / 视频下载+base64 转码 | OSS 转存成公网 CDN 直链，模型直接读 |
+| **时效** | 抖音等临时签名链易过期 | 取数后**立即转存**固化，绕过时效 |
+| **产物组织** | 单文件、9 帖样本 | 每个主题批次独立 run，互不覆盖、可回看历史 |
+
+> 关键验证：已实测**模型能直接读 OSS 上的 http 视频链**——视频不必再下载到本地、不必 base64 转码，省带宽省时间。
+
+---
+
+## 五、质量护栏（为什么产出可信）
+
+产物不是「LLM 一把梭」，而是多道**对抗投票闸**层层过滤，边界一律取保守：
+
+- **创作 vs 制作闸**：admit + refute 两票判定，分歧上 tiebreak，边界**倾向排除**——把「用 AI 一键生成视频」这类工具操作帖挡在外面。
+- **假 how 根治**：代码信号（步骤是否真有先后依赖）+ 对抗投票，把「7 种叙事结构」这种被硬串成步骤的离散构成，重拆回 What/Why。
+- **why 轻闸**：放之四海皆准的废话直接 drop；疑似错分类的标记待人工，不丢内容。
+- **作用域定位复用**：每个标签值用向量最近邻对到 1855 个真实分类树节点——对得上复用原名（不重复造词），对不上才新建（顺手丰富树）。
+
+设计原则：**失败不丢内容**（API 抖动有兜底、判错只标记不删）、**先判类型再成形**（不默认 how、不硬造假 how）。
+
+---
+
+## 六、现状与路线
+
+**已落地**：9 帖样本 → 24 颗（What 15 / Why 5 / How 4），0 ERROR；前端按类型渲染 + 作用域定位 drawer + 提示词溯源。
+
+**本次迭代**：query 搜索 + OSS 转存 + run 隔离产出（先上小红书）。
+
+**后续路线**：
+- 抖音 / 更多平台搜索接入（抖音 keyword 接口需鉴权，待打通）。
+- 真正入库 `POST /api/v1/knowledge/ingest`（当前止于本地 JSON）。
+- payload schema 与代码产出对齐（`dim_attributes` 等细节，见技术文档「已知问题」）。

开发文档/技术文档.md

@@ -0,0 +1,249 @@
+# 创作知识解构 · 技术文档
+
+> 配套：本目录《产品文档.md》《创作知识拆解框架.md》。
+> 本文 = **A 现有系统架构** + **B 下一迭代详细开发步骤** + **C 测试核验** + **D 实施顺序** + **E 已知问题**。
+> 所有 `文件:行号` 可点开核对（行号对应当前 main，实现时以实际为准）。
+
+---
+
+# A. 现有系统架构
+
+## A.1 总览
+
+一条 Python 编排流水线，把帖子拆成知识颗并组装 ingest payload。入口 `scripts/decompose.py`，方法论真源是 `创作知识提取-skill/`（phase 文档直接当 LLM system prompt 喂），产物给 `web/index.html` 渲染。
+
+```
+① 读懂 → ①.5 创作闸 → ② 成形 → ②.5 假how根治 → ②.7 why轻闸 → ③ 作用域 → ⑤ 组装
+```
+
+主循环：[`scripts/decompose.py:436`](../scripts/decompose.py) `main()`。每帖逐阶段跑，任一总闸不过则短路跳过。
+
+## A.2 流水线各阶段
+
+| 阶段 | 函数 / 位置 | 干什么 | 用的 prompt |
+|---|---|---|---|
+| ① 读懂 | `read_one` [decompose.py:98](../scripts/decompose.py) | 图文→`GeminiExtractor` 读图；视频→`video_extract` 下 mp4+base64 喂 Gemini | `extract.txt` / `extract_video.txt` |
+| ① 总闸 | [decompose.py:453](../scripts/decompose.py) | `is_empty`（纯展示/无知识）→ 短路 | — |
+| ①.5 创作闸 | `creation_gate` [decompose.py:142](../scripts/decompose.py) | admit+refute 两票，分歧 tiebreak，边界倾向排除 | `gate_admit/refute/tiebreak.txt` |
+| ② 成形 | `shape` [decompose.py:126](../scripts/decompose.py) | 一次出全部颗+类型+轻标签，作用域留空 | `phase1-frame.md` |
+| ②.5 假how | `fix_fake_hows` [decompose.py:216](../scripts/decompose.py) | 代码信号+对抗投票；假how→`reshape_nonhow` 重拆 what/why | `gate_how_admit/refute/tiebreak.txt` |
+| ②.7 why闸 | `drop_fake_whys` [decompose.py:232](../scripts/decompose.py) | 废话 drop；疑似错分类标 `inferred` 保留 | `gate_why_refute.txt` |
+| 守卫 | [decompose.py:466](../scripts/decompose.py) | 业务阶段/创作阶段/kind 受控值归一、组件 parent 校验 | — |
+| ③ 作用域 | `scope_candidates`→`nounify_scopes`→`apply_scopes` [decompose.py:270](../scripts/decompose.py) | 5 类带值候选 → 名词化 → 连接词拆原子 → 向量定位 | `phase2-scope.md` / `normalize_scope.txt` |
+| ⑤ 组装 | `build_payload` [decompose.py:398](../scripts/decompose.py) | content 按类型分拼、scopes 并集、custom_ext | — |
+
+**关键常量**（[decompose.py:48-64](../scripts/decompose.py)）：`SOURCES`（写死 9 帖入口）、`CSTAGE`（创作阶段 5 值）、`KINDS`（what.kind 3 值）、`REUSE_THRESHOLD=0.90`、`OUT` env 后缀（`OUT=_v2`→写 `frameworks_v2.json` 不覆盖）。
+
+## A.3 关键模块
+
+| 模块 | 职责 | 要点 |
+|---|---|---|
+| `integrations/crawler.py` | 取数 | `PLATFORMS` 路由 detail path（[:24](../creation_knowledge/integrations/crawler.py)）；`fetch_post_detail(content_id)`（[:153](../creation_knowledge/integrations/crawler.py)）；`parse_detail_response` 纯函数可离线测；`RateLimiter` 15s/bucket（[:64](../creation_knowledge/integrations/crawler.py)） |
+| `integrations/extractor.py` | 读图 | `GeminiExtractor`，**图片直传 `image_url`**（[:105](../creation_knowledge/integrations/extractor.py)），走 OpenRouter |
+| `integrations/video_extract.py` | 读视频 | `extract_video` 下载 mp4 → **base64 data_url** 喂模型（[:128](../creation_knowledge/integrations/video_extract.py)）；签名 已有 `save_path/public_url`（[:76](../creation_knowledge/integrations/video_extract.py)）；`_default_download` 带 iOS UA + 平台 Referer 躲防盗链 |
+| `integrations/llm.py` | 文本 LLM | `chat_json(system,user)` 强约束 JSON，**3 轮重试 + self-repair**（[:46](../creation_knowledge/integrations/llm.py)），注入式 `http_post` 可离线测 |
+| `embedding.py` | 向量 | `embed_text` 走火山方舟 Doubao multimodal，2048 维 |
+| `scripts/scope_link.py` | 作用域定位 | `ScopeLinker.link(value, source_type, top_k)` numpy 暴力余弦最近邻；树缓存 `scope_trees/`（trees_index.json + trees_embeddings.npy，**1855 节点**：实质972/形式624/作用185/感受46/意图28） |
+| `config.py` | 配置 | `Settings.from_env`（[:93](../creation_knowledge/config.py)）；`crawler_base_url` 默认 `http://crawler.aiddit.com` |
+
+## A.4 数据契约
+
+### frameworks.json（前端主数据，[web/frameworks.json](../web/frameworks.json)）
+```
+{ count, posts:[ {
+    post_id, source_id, title, platform, url,
+    media:{ type:"image"|"video", video_url, images:[] },
+    cards:[ {index, content, image_url|video_url, start, end} ],
+    knowledges:[ {
+      id, type:"how"|"what"|"why", role:"主"|"组件", parent:null|{how_id,step}, title, 业务阶段:[],
+      // how:  purpose, steps:[{id,input,directive,output,出处,创作阶段,动作,作用域:[]}]
+      // what: 概要, kind, 维度拆分规则, body:[{item_name,item_desc,作用域:[]}]
+      // why:  阐述
+      作用域:[ {scope_type,value,candidate,link:"复用"|"新建",score,top:[{name,score,path}]} ]
+    } ]
+} ] }
+```
+
+### payloads.json（入库体，[web/payloads.json](../web/payloads.json)）
+```
+[ { source:{id,source_type:"post",title,author,source_metadata:{platform,url}},
+    title, content, dim_creations:["创作"], dim_attributes:["how"|"what"|"why"],
+    scopes:[{scope_type,value}], custom_ext:[{key,type:"str",value}] } ]
+```
+- `content`：How=拍平字符串；What=`{name,kind,维度拆分规则,body[]}`；Why=`{name,desc}`（[`build_content` decompose.py:362](../scripts/decompose.py)）。
+
+### Ingest schema（最终裁判）
+`创作知识提取-skill/format/ingest-payload-{how,what,why}.schema.json` —— 字段约束与受控词表的权威定义。
+
+## A.5 前端数据绑定（[web/index.html](../web/index.html)）
+
+- 加载：[`index.html:362`](../web/index.html) `Promise.all([j("/frameworks.json"), j("/payloads.json")])`，`cache:"no-store"`。
+- 渲染：HowTable（工序表）/ WhatCard（构成卡）/ WhyCard（阐述卡）；组件颗 cross-ref 跳转；作用域 drawer（候选→top-K→复用/新建）；提示词溯源（点开产出该阶段的 prompt）。
+- payload 按 `source.id` 匹配挂到帖子（[:363](../web/index.html)）。
+- `web/prompt-read`→`../prompts`、`web/prompt-skill`→`../创作知识提取-skill/extraction` 是**符号链接**（前端静态读 prompt）。
+
+## A.6 运行环境
+
+- Python 3.12，**uv** 管理（`.venv` / `uv.lock`）。跑：`PYTHONPATH=. uv run python scripts/decompose.py`；测：`uv run pytest`。
+- 须在能访问内网 `crawler.aiddit.com` 的环境（云端 / VPN）跑取数。
+- 测试全离线：`pytest` 用 fixture + 注入假 `http_post`，`testpaths=["tests"]`、`pythonpath=["."]`（pyproject.toml）。
+
+---
+
+# B. 下一迭代 · 详细开发步骤
+
+**目标**：`query 搜索 → OSS 转存 → 模型直读 CDN → run 隔离产出`。**仅小红书**。已实测：小红书搜索 `code:0`、OSS `status:0` 回 `cdn_url`、Gemini 直读 OSS http 视频链 HTTP 200。新 env 已写入 `.env`（`CK_OSS_UPLOAD_URL` 等 5 个）。
+
+## B.0 实测确认的接口契约
+
+**搜索**（host = `settings.crawler_base_url`）：
+```
+POST /crawler/xiao_hong_shu/keyword
+body { keyword, content_type:"图文"|"视频", sort_type:"综合", publish_time:"", cursor:"" }
+→ { code:0, data:{ has_more, next_cursor, data:[ {id:<content_id>, note_card:{...预览...}} ] } }
+```
+content_id = `data.data[].id`；翻页 = `has_more` / `next_cursor`；搜索只给预览，**全文/全图/视频仍需 `fetch_post_detail` 逐 id 取详情**。
+
+**OSS 转存**：
+```
+POST http://crawler-upload-v2.aiddit.com/crawler/oss/upload_stream
+body { src_url, src_type:"video"|"image" }
+→ { status:0, oss_object:{ cdn_url:"https://res.cybertogether.net/...", oss_object_key } }
+```
+成功 = `status==0`，取 `oss_object.cdn_url`。
+
+## B.1 新增 2 模块（`creation_knowledge/integrations/`）
+
+风格对齐 crawler：**纯 parser 函数（离线可测）+ 注入式 http**。
+
+### `search.py`
+```python
+SEARCH_PATHS = {
+    "xiaohongshu": "/crawler/xiao_hong_shu/keyword",
+    "douyin":      "/crawler/dou_yin/keyword",   # TODO: 实测 code 10000，疑需 cookie/鉴权
+}
+class SearchError(RuntimeError): ...
+
+def parse_search_response(response: dict, *, platform="xiaohongshu") -> list[dict]:
+    """纯函数。信封 {code,msg,data:{has_more,next_cursor,data:[...]}}。
+    取 data.data[].id（+note_card 预览）；code!=0 抛 SearchError。"""
+
+def search_keyword(keyword, *, platform="xiaohongshu", content_type="图文",
+                   sort_type="综合", limit=5, publish_time="",
+                   settings=None, http_client=None, rate_limiter=None, env_file=".env") -> list[str]:
+    """搜索 → content_id 列表（翻页直到够 limit 或 has_more=False；内部按 id 去重、截断 limit）。
+    用 settings.crawler_base_url + SEARCH_PATHS[platform]；复用 crawler.RateLimiter('{platform}_keyword')。"""
+```
+
+### `oss.py`
+```python
+class OssError(RuntimeError): ...
+
+def parse_oss_response(response: dict) -> str:
+    """纯函数。{status:0,oss_object:{cdn_url}} → cdn_url；status!=0 或缺字段抛 OssError。"""
+
+def upload_stream(src_url, *, src_type, settings=None, http_post=httpx.post, env_file=".env") -> str:
+    """POST settings.oss_upload_url body{src_url,src_type}；timeout=settings.oss_upload_timeout_seconds。"""
+
+def to_oss(src_url, src_type, *, settings, http_post=httpx.post) -> str:
+    """兜底包装：成功返回 cdn_url，任何失败返回原 src_url（永不抛）——decompose 用它，OSS 挂了降级不阻断。"""
+```
+
+## B.2 改 4 处
+
+### ① `config.py` — Settings 加 5 字段（**必须带默认值**）
+```python
+# dataclass 字段（加在 data_dir 后）
+oss_upload_url: str = "http://crawler-upload-v2.aiddit.com/crawler/oss/upload_stream"
+oss_upload_timeout_seconds: int = 60
+search_default_limit: int = 5
+search_content_type: str = "图文"
+search_sort_type: str = "综合"
+# from_env 加读取
+oss_upload_url=env_value("CK_OSS_UPLOAD_URL", file_env, "http://crawler-upload-v2.aiddit.com/crawler/oss/upload_stream"),
+oss_upload_timeout_seconds=int(env_value("CK_OSS_UPLOAD_TIMEOUT_SECONDS", file_env, "60")),
+search_default_limit=int(env_value("CK_SEARCH_DEFAULT_LIMIT", file_env, "5")),
+search_content_type=env_value("CK_SEARCH_CONTENT_TYPE", file_env, "图文"),
+search_sort_type=env_value("CK_SEARCH_SORT_TYPE", file_env, "综合"),
+```
+> ⚠️ **默认值是硬约束**：[`tests/test_video_extract.py:27`](../tests/test_video_extract.py) 用关键字逐字段构造 `Settings(...)` 且不传新字段，无默认会立刻断测。
+
+### ② `video_extract.py` — `extract_video` 加 `oss_video_url`（增量、向后兼容）
+```python
+def extract_video(post, *, ..., oss_video_url: Optional[str] = None) -> ExtractedContent:
+    if oss_video_url:
+        media_part = {"type": "video_url", "video_url": {"url": oss_video_url}}   # 跳过下载+base64
+        public_url = public_url or oss_video_url                                  # 段卡指向 OSS url
+    else:
+        ... 原 下载→save_path→base64 ...                                          # fixture/兜底不变
+        media_part = {"type": "video_url", "video_url": {"url": data_url}}
+```
+
+### ③ `decompose.py` — 入口 + 媒体 + 产出
+- **QUERIES 常量**（`SOURCES` 后，并存）：`QUERIES = [{"query":"分镜脚本","platform":"xiaohongshu","content_type":"图文","limit":5}]`。
+- **抽 `process_one(src, settings, extractor, linker) -> (post_meta, payloads)`**：把 `main()` 循环体（[:445-481](../scripts/decompose.py)）原样抽出，**零行为变更**；先用 SOURCES 跑出与现 `frameworks.json` diff 为空验证 parity。
+- **`expand_queries(QUERIES, settings)`**：逐 query `search_keyword` → ids；`seen` 集**跨 query 去重**；搜索失败打印并跳过该 query。产出 `[{cid,platform,from:"live",query}]`。
+- **`read_one` 媒体改 OSS**（取数后**立即**转存躲时效）：
+  - 图文（[:108-117](../scripts/decompose.py)）：live 帖每张 `to_oss(url,"image",settings=...)` 替掉 `_download_images`；cdn_url 写回 `post.image_urls` / `post.cards[*].url` **再喂** `extractor.extract`（extractor 直读 `card.url`）。fixture 仍走本地 `/data` 路径。
+  - 视频（[:100-107](../scripts/decompose.py)）：`oss_url = to_oss(post.video_urls[0],"video",...)` → `extract_video(post,...,oss_video_url=oss_url)`；`media={"type":"video","video_url":oss_url,...}`。`to_oss` 失败返回原 url → 传 `oss_video_url=None` 回 base64 兜底。
+  - 媒体类型：复用现有 `if post.video_urls:` 判定。
+- **run 隔离产出**：`slugify(text)`（中英→`-`，去空）；写 `web/runs/<slug>.json`（同名存在则加 `-{timestamp}` 保留历史）+ 配套 `web/runs/<slug>.payloads.json`（前端按 `source.id` 配对，须同行）+ 追加 `web/runs/index.json` 清单 `[{slug,query,count,created_at}]`。**不覆盖 `web/frameworks.json`**。
+
+### ④ `web/index.html` — run 选择器（小改）
+- data-load `useEffect`（[:361](../web/index.html)）加：fetch `/runs/index.json` 填一个 `<select>`/pill 行（近 header）。
+- 选中某 run → 加载 `/runs/<slug>.json` + `/runs/<slug>.payloads.json`；**默认不选仍加载 `/frameworks.json`**（9 帖样本不动）。
+- 渲染树形状兼容（run posts 与 frameworks posts 同形），卡片组件零改。
+
+## B.3 跨切面
+
+| 关注点 | 处理 |
+|---|---|
+| 去重 | `search_keyword` 内按 id；`expand_queries` 的 `seen` 集跨 query |
+| 时效 | 取数后**立即** OSS 转存（抖音/CDN 临时签名链） |
+| 限流 | 复用 `RateLimiter` 15s（搜索 `{platform}_keyword`、详情 `{platform}_detail`）；OSS 用 `oss_upload_timeout_seconds`=60 |
+| 兜底 | OSS 失败→原 url；搜索失败→跳过该 query；视频 OSS 失败→回 base64；详情失败→现有 error-post 路径不变 |
+
+---
+
+# C. 测试与核验
+
+## C.1 必须保持绿（`uv run pytest`，全离线）
+`test_crawler` / `test_extractor` / `test_video_extract` / `test_media` / `test_video_frames`。
+保障：`Settings` 新字段带默认（护住 [test_video_extract.py:27](../tests/test_video_extract.py) 的 `Settings(...)`）+ `extract_video` 改动为增量（`oss_video_url=None` 默认走原路）。
+
+## C.2 新增离线单测（沿用 `_Resp` + 注入式 http 模式）
+- **`test_search.py`**：`parse_search_response` 取 id；`search_keyword` 去重+翻页+截断（假 client 返回重复 id + `has_more`）；`code 10000` → `SearchError`。新增 fixture `tests/fixtures/xhs_search_*.json`（裸 `{code,msg,data}`）。
+- **`test_oss.py`**：`parse_oss_response` 取 cdn_url；`status!=0` → `OssError`；`to_oss` 注入异常 → 返回原 url。
+- **`test_decompose_helpers.py`**：`slugify`（中英/防碰撞）；`expand_queries` 跨 query 去重（monkeypatch `search_keyword` 返回重叠 id）。
+- **`test_video_extract.py` 补** `test_extract_video_oss_direct_feed`：传 `oss_video_url`，断言**未调用下载** + 请求体 `video_url.url` == 该 http url（用假 `http_post` 捕获）。
+
+## C.3 端到端手测（真网，跑一次）
+1. 真实小红书 `QUERIES` → `PYTHONPATH=. uv run python scripts/decompose.py`。
+2. 确认 `web/runs/<slug>.json` + `.payloads.json` + `index.json` 生成；`media.images`/`media.video_url` 是 `res.cybertogether.net` cdn_url。
+3. 起 `web/` 静态服务 → run 选择器列出新 run → 图/视频无 403 → 三类知识渲染如常。
+
+---
+
+# D. 实施顺序（每步独立可验证）
+
+1. `config.py` 加字段（带默认）+ 读 env → `pytest` 全绿（证默认护住旧构造）。
+2. `oss.py` + `test_oss.py`（独立）。
+3. `search.py` + `test_search.py` + fixture（独立）。
+4. `video_extract.py` 加 `oss_video_url` → 视频测试绿 + 新直喂测试。
+5. `decompose.py` 抽 `process_one` 零行为重构 → SOURCES 跑出与现 `frameworks.json` diff 为空。
+6. `decompose.py` 接入 QUERIES/expand/OSS/slug/run 写盘+manifest → helper 测 + 小红书 dry run。
+7. `web/index.html` run 选择器 + manifest 加载，默认回退 frameworks.json。
+8. 端到端手测。
+
+> 步骤 2–4 互相独立可并行；每步可单独提交、单独测。
+
+---
+
+# E. 已知问题 / 暂缓项
+
+| 问题 | 现象 | 处置 |
+|---|---|---|
+| **dim_attributes 不一致** | 代码发 `["why"]`（[`TYPE2ATTR` decompose.py:60](../scripts/decompose.py)），但 `format/ingest-payload-why.schema.json` 要 `const:"why原理"`（how/what 同类：`how工序`/`what构成`） | 本迭代**不改**，待入库 ingest 时统一对齐（确定以哪边为准） |
+| **抖音搜索鉴权** | `POST /crawler/dou_yin/keyword` 实测 `code:10000 未知错误` | 标 TODO；`search.py` 让抖音抛 `SearchError`，`expand_queries` 干净跳过；本迭代只上小红书 |
+| **OSS 串行延迟** | 每张图一次 OSS POST（60s timeout），多图帖串行慢 | 暂接受；后续可并行/限流 |
+| **B站视频** | `video_extract` 对 B站抛错（需 voice_data 音轨 + ffmpeg 合并） | 与本迭代无关，沿用现状 |