feat: expand oneliner orchestration flows

collector-service/app/oneliner_features.py

@@ -1,6 +1,7 @@
 from __future__ import annotations
 
 import json
+import re
 from typing import Any
 
 from fastapi import Depends, HTTPException, Query
@@ -700,6 +701,262 @@ def register_oneliner_routes(app: Any, legacy: Any) -> None:
         )
         return str((row or {}).get("content") or "").strip()
 
+    def _extract_first_url(text: str) -> str:
+        cleaned = str(text or "").strip()
+        if not cleaned:
+            return ""
+        match = re.search(r"https?://[^\s<>'\"]+", cleaned)
+        if not match:
+            return ""
+        return match.group(0).rstrip("，。；;,.)]》】!?！？")
+
+    def _find_creator_source_by_url(
+        account: dict[str, Any],
+        *,
+        project_id: str,
+        platform: str,
+        source_url: str,
+    ) -> dict[str, Any] | None:
+        return legacy.db.fetch_one(
+            """
+            SELECT * FROM content_sources
+            WHERE user_id = ? AND project_id = ? AND platform = ? AND source_kind = 'creator_account' AND source_url = ?
+            ORDER BY updated_at DESC, created_at DESC
+            LIMIT 1
+            """,
+            (account["id"], project_id, platform, source_url),
+        )
+
+    def _latest_platform_account(
+        account: dict[str, Any],
+        *,
+        project_id: str,
+        platform: str,
+    ) -> dict[str, Any] | None:
+        return legacy.db.fetch_one(
+            """
+            SELECT * FROM content_sources
+            WHERE user_id = ? AND project_id = ? AND platform = ? AND source_kind = 'creator_account'
+            ORDER BY updated_at DESC, created_at DESC
+            LIMIT 1
+            """,
+            (account["id"], project_id, platform),
+        )
+
+    def _load_owned_job(account: dict[str, Any], job_id: str) -> dict[str, Any] | None:
+        normalized_job_id = str(job_id or "").strip()
+        if not normalized_job_id:
+            return None
+        return legacy.db.fetch_one(
+            "SELECT * FROM jobs WHERE id = ? AND user_id = ?",
+            (normalized_job_id, account["id"]),
+        )
+
+    def _latest_derivable_job(
+        account: dict[str, Any],
+        *,
+        project_id: str,
+        exclude_line_types: set[str] | None = None,
+    ) -> dict[str, Any] | None:
+        excluded = {item.strip() for item in (exclude_line_types or set()) if str(item or "").strip()}
+        rows = legacy.db.fetch_all(
+            """
+            SELECT * FROM jobs
+            WHERE user_id = ? AND project_id = ? AND status IN ('completed', 'done', 'succeeded')
+            ORDER BY updated_at DESC, created_at DESC
+            LIMIT 24
+            """,
+            (account["id"], project_id),
+        )
+        for row in rows:
+            if str(row.get("line_type") or "").strip() in excluded:
+                continue
+            return row
+        return rows[0] if rows else None
+
+    def _job_performance_score(job_row: dict[str, Any] | None) -> float:
+        if not job_row:
+            return 0.0
+        result_map = _parse_json(job_row.get("result_json") or "{}", {})
+        artifacts_map = _parse_json(job_row.get("artifacts_json") or "{}", {})
+        candidates = [
+            result_map.get("performance_score"),
+            (result_map.get("analysis") or {}).get("performance_score"),
+            (result_map.get("scores") or {}).get("performance_score"),
+            artifacts_map.get("performance_score"),
+            (artifacts_map.get("scores") or {}).get("performance_score"),
+        ]
+        for value in candidates:
+            try:
+                return float(value)
+            except (TypeError, ValueError):
+                continue
+        return 0.0
+
+    def _linked_platform_videos(
+        account: dict[str, Any],
+        *,
+        project_id: str,
+        platform: str,
+        account_row: dict[str, Any],
+        limit: int = 8,
+    ) -> list[dict[str, Any]]:
+        rows = legacy.db.fetch_all(
+            """
+            SELECT * FROM content_sources
+            WHERE user_id = ? AND project_id = ? AND platform = ? AND source_kind = 'video_link'
+            ORDER BY updated_at DESC, created_at DESC
+            """,
+            (account["id"], project_id, platform),
+        )
+        account_source_url = str(account_row.get("source_url") or "").strip()
+        items: list[dict[str, Any]] = []
+        for row in rows:
+            payload = legacy.content_source_payload(row)
+            metadata = payload.get("metadata") or {}
+            if metadata.get("origin_content_source_id") != account_row["id"] and metadata.get("source_account_url") != account_source_url:
+                continue
+            latest_job = legacy.db.fetch_one(
+                "SELECT * FROM jobs WHERE content_source_id = ? ORDER BY updated_at DESC, created_at DESC LIMIT 1",
+                (row["id"],),
+            )
+            result_map = _parse_json((latest_job or {}).get("result_json") or "{}", {})
+            published_at = (
+                metadata.get("published_at")
+                or metadata.get("publish_time")
+                or metadata.get("created_at")
+                or payload.get("updated_at")
+                or payload.get("created_at")
+                or ""
+            )
+            items.append(
+                {
+                    "id": payload["id"],
+                    "title": payload.get("title") or payload.get("handle") or payload.get("source_url") or payload["id"],
+                    "source_url": payload.get("source_url", ""),
+                    "published_at": published_at,
+                    "score": {
+                        "performance_score": _job_performance_score(latest_job),
+                    },
+                    "latest_job_id": (latest_job or {}).get("id", ""),
+                    "latest_job_status": (latest_job or {}).get("status", ""),
+                    "summary": str(result_map.get("summary") or result_map.get("headline_summary") or "")[:240],
+                }
+            )
+        items.sort(key=lambda item: (float((item.get("score") or {}).get("performance_score") or 0), item.get("published_at") or ""), reverse=True)
+        return items[: max(1, min(int(limit or 8), 16))]
+
+    def _fallback_platform_videos(
+        account: dict[str, Any],
+        *,
+        project_id: str,
+        platform: str,
+        requested_account_id: str = "",
+        limit: int = 8,
+    ) -> tuple[dict[str, Any] | None, list[dict[str, Any]]]:
+        safe_limit = max(1, min(int(limit or 8), 16))
+        if platform == "douyin":
+            target_account = None
+            if requested_account_id:
+                target_account = legacy.db.fetch_one(
+                    "SELECT * FROM douyin_accounts WHERE id = ? AND user_id = ?",
+                    (requested_account_id, account["id"]),
+                )
+            if not target_account:
+                target_account = legacy.db.fetch_one(
+                    "SELECT * FROM douyin_accounts WHERE user_id = ? ORDER BY updated_at DESC, created_at DESC LIMIT 1",
+                    (account["id"],),
+                )
+            if not target_account:
+                return None, []
+            rows = legacy.db.fetch_all(
+                """
+                SELECT * FROM douyin_videos
+                WHERE account_id = ?
+                ORDER BY COALESCE(published_at, updated_at) DESC, updated_at DESC
+                LIMIT ?
+                """,
+                (target_account["id"], safe_limit),
+            )
+            items: list[dict[str, Any]] = []
+            for row in rows:
+                stats = _parse_json(row.get("stats_json") or "{}", {})
+                play_count = float(stats.get("play_count") or stats.get("play") or 0)
+                like_count = float(stats.get("digg_count") or stats.get("like_count") or 0)
+                comment_count = float(stats.get("comment_count") or 0)
+                share_count = float(stats.get("share_count") or 0)
+                score = min(
+                    100.0,
+                    play_count / 10000 * 55
+                    + like_count / 1000 * 25
+                    + comment_count / 100 * 10
+                    + share_count / 100 * 10,
+                )
+                items.append(
+                    {
+                        "id": row["id"],
+                        "title": row.get("title") or row.get("description") or row.get("share_url") or row["id"],
+                        "source_url": row.get("share_url", ""),
+                        "published_at": row.get("published_at") or "",
+                        "score": {"performance_score": round(score, 2)},
+                        "latest_job_id": "",
+                        "latest_job_status": "",
+                        "summary": "",
+                    }
+                )
+            account_payload = {
+                "id": target_account["id"],
+                "title": target_account.get("nickname") or target_account.get("douyin_id") or "抖音账号",
+                "handle": target_account.get("douyin_id") or "",
+                "source_url": target_account.get("canonical_profile_url") or target_account.get("profile_url") or "",
+                "platform": "douyin",
+            }
+            items.sort(key=lambda item: (float((item.get("score") or {}).get("performance_score") or 0), item.get("published_at") or ""), reverse=True)
+            return account_payload, items[:safe_limit]
+
+        source_account = _latest_platform_account(account, project_id=project_id, platform=platform)
+        if not source_account:
+            return None, []
+        source_payload = legacy.content_source_payload(source_account)
+        metadata = source_payload.get("metadata") or {}
+        summary_videos = ((metadata.get("video_summary") or {}).get("videos") or [])[:safe_limit]
+        items = []
+        for item in summary_videos:
+            score = float(item.get("performance_score") or item.get("score") or 0)
+            items.append(
+                {
+                    "id": str(item.get("id") or item.get("aweme_id") or item.get("video_id") or make_id(f"{platform}_video")),
+                    "title": str(item.get("title") or item.get("description") or item.get("share_url") or "平台作品"),
+                    "source_url": str(item.get("share_url") or item.get("url") or ""),
+                    "published_at": str(item.get("published_at") or item.get("publish_time") or ""),
+                    "score": {"performance_score": score},
+                    "latest_job_id": "",
+                    "latest_job_status": "",
+                    "summary": "",
+                }
+            )
+        items.sort(key=lambda item: (float((item.get("score") or {}).get("performance_score") or 0), item.get("published_at") or ""), reverse=True)
+        return source_payload, items[:safe_limit]
+
+    def _assistant_brief_from_job(job_row: dict[str, Any] | None) -> str:
+        if not job_row:
+            return ""
+        result_map = _parse_json(job_row.get("result_json") or "{}", {})
+        artifacts_map = _parse_json(job_row.get("artifacts_json") or "{}", {})
+        candidates = [
+            result_map.get("summary"),
+            result_map.get("headline_summary"),
+            artifacts_map.get("summary"),
+            artifacts_map.get("objective"),
+            artifacts_map.get("brief"),
+            job_row.get("title"),
+        ]
+        for value in candidates:
+            cleaned = str(value or "").strip()
+            if cleaned:
+                return cleaned[:480]
+        return ""
+
     def _load_owned_session(session_id: str, account: dict[str, Any]) -> dict[str, Any]:
         row = legacy.db.fetch_one(
             "SELECT * FROM oneliner_sessions WHERE id = ? AND user_id = ?",
@@ -1090,6 +1347,20 @@ def register_oneliner_routes(app: Any, legacy: Any) -> None:
                     "platform": plan.get("platform", ""),
                 }
             )
+        first_url = _extract_first_url(message)
+        if plan.get("intent_key") in {"import_homepage", "custom"} and first_url:
+            secondary_actions.append(
+                {
+                    "key": "run-oneliner-action",
+                    "label": "直接导入主页",
+                    "kind": "api_action",
+                    "executor_key": "import-homepage",
+                    "platform": plan.get("platform", ""),
+                    "payload": {
+                        "source_url": first_url,
+                    },
+                }
+            )
         if plan.get("intent_key") in {"storage_status", "custom"}:
             secondary_actions.append(
                 {
@@ -1110,6 +1381,40 @@ def register_oneliner_routes(app: Any, legacy: Any) -> None:
                     "platform": plan.get("platform", ""),
                 }
             )
+            if first_url:
+                secondary_actions.append(
+                    {
+                        "key": "run-oneliner-action",
+                        "label": "直接保存录制源",
+                        "kind": "api_action",
+                        "executor_key": "save-live-recorder-source",
+                        "platform": plan.get("platform", ""),
+                        "payload": {
+                            "source_url": first_url,
+                            "auto_start": True,
+                        },
+                    }
+                )
+        latest_platform_account = None
+        if plan.get("platform"):
+            latest_platform_account = _latest_platform_account(
+                account,
+                project_id=project_id or "",
+                platform=plan.get("platform", ""),
+            )
+        if plan.get("platform") and latest_platform_account and plan.get("intent_key") in {"analyze_top_videos", "analyze_account", "custom"}:
+            secondary_actions.append(
+                {
+                    "key": "run-oneliner-action",
+                    "label": "直接分析高分作品",
+                    "kind": "api_action",
+                    "executor_key": "analyze-top-videos",
+                    "platform": plan.get("platform", ""),
+                    "payload": {
+                        "target_account_id": latest_platform_account["id"],
+                    },
+                }
+            )
         if context.get("assistant"):
             secondary_actions.append(
                 {
@@ -1131,6 +1436,32 @@ def register_oneliner_routes(app: Any, legacy: Any) -> None:
                     "platform": plan.get("platform", ""),
                 }
             )
+        derivable_job = _latest_derivable_job(account, project_id=project_id or "", exclude_line_types={"ai_video", "real_cut"})
+        if derivable_job and plan.get("intent_key") in {"ai_video", "real_cut", "review", "custom"}:
+            secondary_actions.append(
+                {
+                    "key": "run-oneliner-action",
+                    "label": "直接创建 AI 视频",
+                    "kind": "api_action",
+                    "executor_key": "create-ai-video",
+                    "platform": plan.get("platform", ""),
+                    "payload": {
+                        "source_job_id": derivable_job["id"],
+                    },
+                }
+            )
+            secondary_actions.append(
+                {
+                    "key": "run-oneliner-action",
+                    "label": "直接创建实拍剪辑",
+                    "kind": "api_action",
+                    "executor_key": "create-real-cut",
+                    "platform": plan.get("platform", ""),
+                    "payload": {
+                        "source_job_id": derivable_job["id"],
+                    },
+                }
+            )
         if account.get("role") == "super_admin":
             secondary_actions.append(
                 {
@@ -1708,6 +2039,7 @@ def register_oneliner_routes(app: Any, legacy: Any) -> None:
         if not action_key:
             raise HTTPException(status_code=400, detail="Action key is required")
         latest_user_message = _last_user_message_text(request.session_id, account["id"]) if request.session_id else ""
+        requested_payload = request.payload or {}
 
         async def _run_platform_self_check() -> dict[str, Any]:
             if not normalized_platform:
@@ -1820,6 +2152,272 @@ def register_oneliner_routes(app: Any, legacy: Any) -> None:
                 "payload": payload,
             }
 
+        async def _run_import_homepage() -> dict[str, Any]:
+            source_url = str(
+                requested_payload.get("source_url")
+                or requested_payload.get("sourceUrl")
+                or _extract_first_url(latest_user_message)
+                or ""
+            ).strip()
+            if not source_url:
+                raise HTTPException(status_code=400, detail="No homepage URL available for import")
+            inferred_platform = normalize_platform_from_text(
+                requested_payload.get("platform")
+                or requested_payload.get("platform_label")
+                or normalized_platform
+                or legacy.infer_platform_from_url(source_url)
+            ) or _safe_platform(legacy.infer_platform_from_url(source_url), fallback="douyin")
+            assistant = _resolve_execution_assistant(account, project_id=project["id"], platform=inferred_platform)
+            existing_source = _find_creator_source_by_url(
+                account,
+                project_id=project["id"],
+                platform=inferred_platform,
+                source_url=source_url,
+            )
+            sync_job = await legacy.create_content_source_sync_job(
+                legacy.ContentSourceSyncRequest(
+                    project_id=project["id"],
+                    knowledge_base_id=str(requested_payload.get("knowledge_base_id") or requested_payload.get("knowledgeBaseId") or ""),
+                    assistant_id=(assistant or {}).get("id", ""),
+                    content_source_id=(existing_source or {}).get("id", ""),
+                    platform=inferred_platform,
+                    handle=str(requested_payload.get("handle") or ""),
+                    source_url=source_url,
+                    title=str(requested_payload.get("title") or requested_payload.get("name") or ""),
+                    analysis_model_profile_id=str(requested_payload.get("analysis_model_profile_id") or requested_payload.get("analysisModelProfileId") or ""),
+                    language=str(requested_payload.get("language") or "auto"),
+                    max_items=max(1, min(int(requested_payload.get("max_items") or requested_payload.get("maxItems") or 5), 20)),
+                    skip_existing=bool(requested_payload.get("skip_existing", requested_payload.get("skipExisting", True))),
+                    auto_trigger_analysis=bool(requested_payload.get("auto_trigger_analysis", requested_payload.get("autoTriggerAnalysis", True))),
+                ),
+                account,
+            )
+            return {
+                "title": "OneLiner 已导入主页",
+                "summary": f"已把主页接入当前项目，并触发 {legacy.platform_label(inferred_platform)} 内容源同步。",
+                "payload": {
+                    "job": sync_job,
+                    "platform": inferred_platform,
+                    "source_url": source_url,
+                    "existing_source_id": (existing_source or {}).get("id", ""),
+                },
+            }
+
+        async def _run_analyze_top_videos() -> dict[str, Any]:
+            if not normalized_platform:
+                raise HTTPException(status_code=400, detail="Platform is required for top video analysis")
+            target_account = None
+            requested_account_id = str(requested_payload.get("target_account_id") or requested_payload.get("targetAccountId") or "").strip()
+            if requested_account_id and normalized_platform != "douyin":
+                target_account = legacy.db.fetch_one(
+                    """
+                    SELECT * FROM content_sources
+                    WHERE id = ? AND user_id = ? AND project_id = ? AND platform = ? AND source_kind = 'creator_account'
+                    """,
+                    (requested_account_id, account["id"], project["id"], normalized_platform),
+                )
+            if not target_account:
+                target_account = _latest_platform_account(account, project_id=project["id"], platform=normalized_platform)
+            videos = _linked_platform_videos(
+                account,
+                project_id=project["id"],
+                platform=normalized_platform,
+                account_row=target_account or {},
+                limit=max(2, min(int(requested_payload.get("top_video_count") or requested_payload.get("topVideoCount") or 6), 12)),
+            ) if target_account else []
+            account_payload = legacy.content_source_payload(target_account) if target_account else None
+            if not videos:
+                account_payload, videos = _fallback_platform_videos(
+                    account,
+                    project_id=project["id"],
+                    platform=normalized_platform,
+                    requested_account_id=requested_account_id,
+                    limit=max(2, min(int(requested_payload.get("top_video_count") or requested_payload.get("topVideoCount") or 6), 12)),
+                )
+            if not account_payload:
+                raise HTTPException(status_code=404, detail="No platform account available for top video analysis")
+            min_score = float(requested_payload.get("min_score") or requested_payload.get("minScore") or 0)
+            ranked = [item for item in videos if float((item.get("score") or {}).get("performance_score") or 0) >= min_score]
+            if not ranked:
+                raise HTTPException(status_code=404, detail="No candidate videos available for analysis")
+            profile = legacy.model_profile_for_account(
+                account["id"],
+                str(requested_payload.get("model_profile_id") or requested_payload.get("modelProfileId") or ""),
+            )
+            items: list[dict[str, Any]] = []
+            parse_json_object = getattr(legacy, "parse_json_object", None)
+            for video in ranked:
+                prompt = (
+                    f"请拆解这条{legacy.platform_label(normalized_platform)}作品为什么值得关注，"
+                    "输出 JSON，字段包括 summary、borrow_points、risks、next_actions。"
+                    f"\n\n输入：\n{json.dumps(video, ensure_ascii=False, indent=2)}"
+                )
+                output = await legacy.call_model(
+                    profile,
+                    "你是平台内容拆解助手。尽量输出 JSON，字段包括 summary、borrow_points、risks、next_actions。",
+                    prompt,
+                    temperature=float(requested_payload.get("temperature") or 0.25),
+                )
+                parsed = parse_json_object(output) if callable(parse_json_object) else _parse_json(output, {})
+                summary_text = str(parsed.get("summary") or parsed.get("headline_summary") or output).strip()[:240]
+                items.append(
+                    {
+                        "id": make_id(f"{normalized_platform}_va"),
+                        "video_id": video["id"],
+                        "video_title": video["title"],
+                        "status": "ok",
+                        "summary_text": summary_text,
+                        "parsed_json": parsed,
+                        "performance_score": (video.get("score") or {}).get("performance_score", 0),
+                        "latest_job_id": video.get("latest_job_id", ""),
+                        "created_at": now(),
+                    }
+                )
+            memory = _remember_platform_observation(
+                account,
+                project_id=project["id"],
+                platform=normalized_platform,
+                memory_key=f"top_videos::{target_account['id']}",
+                title=f"{legacy.platform_label(normalized_platform)} 高分作品拆解",
+                summary=f"已拆解 {len(items)} 条高分作品，继续固化有效结构与风险判断。",
+                details={
+                    "account_id": account_payload["id"],
+                    "items": items,
+                },
+                confidence=0.84,
+            )
+            return {
+                "title": "OneLiner 已分析高分作品",
+                "summary": f"已为 {legacy.platform_label(normalized_platform)} 账号拆解 {len(items)} 条高分作品。",
+                "payload": {
+                    "platform": normalized_platform,
+                    "account": account_payload,
+                    "analyzed_count": len(items),
+                    "items": items,
+                    "memory": memory,
+                },
+            }
+
+        async def _run_create_ai_video() -> dict[str, Any]:
+            source_job = _load_owned_job(account, str(requested_payload.get("source_job_id") or requested_payload.get("sourceJobId") or "")) or _latest_derivable_job(
+                account,
+                project_id=project["id"],
+                exclude_line_types={"ai_video"},
+            )
+            if not source_job:
+                raise HTTPException(status_code=404, detail="No completed source job available for AI video")
+            assistant = _resolve_execution_assistant(account, project_id=project["id"], platform=normalized_platform)
+            brief = str(
+                requested_payload.get("brief")
+                or requested_payload.get("video_brief")
+                or requested_payload.get("videoBrief")
+                or latest_user_message
+                or _assistant_brief_from_job(source_job)
+                or ""
+            ).strip()
+            if not brief:
+                brief = f"请基于任务「{source_job.get('title') or source_job['id']}」输出一版适合短视频平台的 AI 视频。"
+            job = await legacy.create_ai_video_job(
+                legacy.AiVideoJobRequest(
+                    project_id=project["id"],
+                    assistant_id=(assistant or {}).get("id", ""),
+                    knowledge_base_id=str(requested_payload.get("knowledge_base_id") or requested_payload.get("knowledgeBaseId") or source_job.get("knowledge_base_id") or ""),
+                    source_job_id=source_job["id"],
+                    title=str(requested_payload.get("title") or f"{source_job.get('title') or '任务'} · AI 视频"),
+                    brief=brief,
+                    style=str(requested_payload.get("style") or "realistic"),
+                    shots=max(1, min(int(requested_payload.get("shots") or 4), 12)),
+                    duration=max(3, min(int(requested_payload.get("duration") or 5), 12)),
+                ),
+                account,
+            )
+            return {
+                "title": "OneLiner 已创建 AI 视频任务",
+                "summary": f"已基于「{source_job.get('title') or source_job['id']}」创建 AI 视频任务。",
+                "payload": {
+                    "job": job,
+                    "source_job": legacy.job_payload(source_job),
+                    "brief": brief,
+                },
+            }
+
+        async def _run_create_real_cut() -> dict[str, Any]:
+            source_job = _load_owned_job(account, str(requested_payload.get("source_job_id") or requested_payload.get("sourceJobId") or "")) or _latest_derivable_job(
+                account,
+                project_id=project["id"],
+                exclude_line_types={"ai_video", "real_cut"},
+            )
+            if not source_job:
+                raise HTTPException(status_code=404, detail="No completed source job available for real cut")
+            job = await legacy.create_real_cut_job(
+                legacy.RealCutJobRequest(
+                    project_id=project["id"],
+                    title=str(requested_payload.get("title") or f"{source_job.get('title') or '任务'} · 实拍剪辑"),
+                    source_job_id=source_job["id"],
+                    objective=str(
+                        requested_payload.get("objective")
+                        or "保留高信息密度片段，输出适合短视频平台的粗剪结果"
+                    ),
+                    target_duration_sec=max(10, min(int(requested_payload.get("target_duration_sec") or requested_payload.get("targetDurationSec") or 60), 300)),
+                    target_aspect_ratio=str(requested_payload.get("target_aspect_ratio") or requested_payload.get("targetAspectRatio") or "9:16"),
+                    review_enabled=bool(requested_payload.get("review_enabled", requested_payload.get("reviewEnabled", False))),
+                ),
+                account,
+            )
+            return {
+                "title": "OneLiner 已创建实拍剪辑任务",
+                "summary": f"已基于「{source_job.get('title') or source_job['id']}」创建实拍剪辑任务。",
+                "payload": {
+                    "job": job,
+                    "source_job": legacy.job_payload(source_job),
+                },
+            }
+
+        async def _run_save_live_recorder_source() -> dict[str, Any]:
+            source_url = str(
+                requested_payload.get("source_url")
+                or requested_payload.get("sourceUrl")
+                or _extract_first_url(latest_user_message)
+                or ""
+            ).strip()
+            if not source_url:
+                raise HTTPException(status_code=400, detail="No live recorder source URL available")
+            recorder_platform = normalize_platform_from_text(
+                requested_payload.get("platform")
+                or requested_payload.get("platform_label")
+                or normalized_platform
+                or legacy.infer_platform_from_url(source_url)
+            ) or _safe_platform(legacy.infer_platform_from_url(source_url), fallback="kuaishou")
+            assistant = _resolve_execution_assistant(account, project_id=project["id"], platform=recorder_platform)
+            saved = legacy.create_live_recorder_source(
+                legacy.LiveRecorderSourceCreateRequest(
+                    project_id=project["id"],
+                    assistant_id=(assistant or {}).get("id", ""),
+                    platform=recorder_platform,
+                    source_url=source_url,
+                    title=str(requested_payload.get("title") or ""),
+                    quality=str(requested_payload.get("quality") or "原画"),
+                    enabled=bool(requested_payload.get("enabled", True)),
+                ),
+                account,
+            )
+            started = None
+            if bool(requested_payload.get("auto_start", requested_payload.get("autoStart", True))):
+                try:
+                    started = legacy.live_recorder_start(account)
+                except Exception as exc:
+                    started = {"ok": False, "message": str(exc)}
+            return {
+                "title": "OneLiner 已保存录制源",
+                "summary": f"已把直播源保存到当前租户的 NAS 录制配置里。",
+                "payload": {
+                    "saved": saved,
+                    "started": started,
+                    "platform": recorder_platform,
+                    "source_url": source_url,
+                },
+            }
+
         executors = {
             "platform-self-check": _run_platform_self_check,
             "storage-status": _run_storage_status,
@@ -1827,6 +2425,11 @@ def register_oneliner_routes(app: Any, legacy: Any) -> None:
             "scan-admin-ops": _run_ops_scan,
             "generate-copy": _run_generate_copy,
             "review-draft": _run_review_draft,
+            "import-homepage": _run_import_homepage,
+            "analyze-top-videos": _run_analyze_top_videos,
+            "create-ai-video": _run_create_ai_video,
+            "create-real-cut": _run_create_real_cut,
+            "save-live-recorder-source": _run_save_live_recorder_source,
         }
         executor = executors.get(action_key)
         if not executor:

web/storyforge-web-v4/assets/app.js

@@ -770,7 +770,17 @@ function renderOneLinerMessagesHtml() {
                     [
                       item.executor_key ? `data-executor-key="${escapeHtml(item.executor_key)}"` : "",
                       item.platform ? `data-platform="${escapeHtml(item.platform)}"` : "",
-                      message.session_id ? `data-session-id="${escapeHtml(message.session_id)}"` : ""
+                      message.session_id ? `data-session-id="${escapeHtml(message.session_id)}"` : "",
+                      ...Object.entries(item.payload || {}).map(([payloadKey, payloadValue]) => {
+                        const attrKey = String(payloadKey || "")
+                          .replace(/([a-z0-9])([A-Z])/g, "$1-$2")
+                          .replace(/_/g, "-")
+                          .toLowerCase();
+                        const serialized = typeof payloadValue === "string"
+                          ? payloadValue
+                          : JSON.stringify(payloadValue);
+                        return `data-${escapeHtml(attrKey)}="${escapeHtml(serialized)}"`;
+                      })
                     ].filter(Boolean).join(" ")
                   )).join("")}
                 </div>
@@ -1120,6 +1130,61 @@ function renderOneLinerExecutionPayloadHtml(payload) {
   if (!payload || typeof payload !== "object") {
     return `<div class="task-item compact"><h4>没有返回执行结果</h4><p>当前执行器没有附带额外数据。</p></div>`;
   }
+  if (payload.job) {
+    const job = payload.job || {};
+    const sourceJob = payload.source_job || {};
+    return `
+      <div class="task-item compact">
+        <h4>${escapeHtml(job.title || "任务已创建")}</h4>
+        <p>${escapeHtml(payload.brief || sourceJob.title || `已创建 ${job.line_type || job.source_type || "任务"}，你可以继续进入生产中心查看进度。`)}</p>
+        <div class="task-meta">
+          <span class="tag blue">${escapeHtml(job.line_type || job.source_type || "analysis")}</span>
+          <span class="tag ${job.status === "completed" ? "green" : "orange"}">${escapeHtml(job.status || "queued")}</span>
+          ${job.id ? `<span class="tag clickable-tag" data-action="open-job-detail" data-job-id="${escapeHtml(job.id)}">看任务详情</span>` : ""}
+          <span class="tag clickable-tag" data-action="goto-production">去生产中心</span>
+        </div>
+      </div>
+    `;
+  }
+  if (payload.saved || payload.started) {
+    const saved = payload.saved || {};
+    const item = saved.item || {};
+    const started = payload.started || {};
+    return `
+      <div class="task-item compact">
+        <h4>${escapeHtml(item.binding_title || payload.source_url || "录制源已保存")}</h4>
+        <p>${escapeHtml(item.source_url || payload.source_url || "直播源已经保存到当前租户的录制配置。")}</p>
+        <div class="task-meta">
+          <span class="tag blue">${escapeHtml(platformLabel(payload.platform || item.platform || "kuaishou"))}</span>
+          <span class="tag">${escapeHtml(item.quality || "原画")}</span>
+          <span class="tag ${started && started.ok === false ? "orange" : "green"}">${escapeHtml(started && started.ok === false ? "启动待重试" : "已同步")}</span>
+          <span class="tag clickable-tag" data-action="open-live-recorder">打开录制控制</span>
+        </div>
+      </div>
+    `;
+  }
+  if (payload.analyzed_count !== undefined && safeArray(payload.items).length) {
+    return `
+      <div class="detail-grid">
+        <div class="mini-card"><small>平台</small><strong>${escapeHtml(platformLabel(payload.platform || payload.account?.platform || "douyin"))}</strong></div>
+        <div class="mini-card"><small>账号</small><strong>${escapeHtml(payload.account?.title || payload.account?.handle || payload.account?.source_url || "当前账号")}</strong></div>
+        <div class="mini-card"><small>拆解作品</small><strong>${escapeHtml(formatNumber(payload.analyzed_count || 0))}</strong></div>
+        <div class="mini-card"><small>已写记忆</small><strong>${escapeHtml(payload.memory?.title ? "是" : "否")}</strong></div>
+      </div>
+      <div class="list" style="margin-top:12px;">
+        ${safeArray(payload.items).slice(0, 4).map((item) => `
+          <div class="task-item compact">
+            <h4>${escapeHtml(item.video_title || "高分作品")}</h4>
+            <p>${escapeHtml(item.summary_text || "已完成拆解。")}</p>
+            <div class="task-meta">
+              <span class="tag blue">得分 ${escapeHtml(formatNumber(item.performance_score || 0))}</span>
+              ${item.latest_job_id ? `<span class="tag clickable-tag" data-action="open-job-detail" data-job-id="${escapeHtml(item.latest_job_id)}">看原分析</span>` : ""}
+            </div>
+          </div>
+        `).join("")}
+      </div>
+    `;
+  }
   if (payload.route_checks) {
     return `
       <div class="detail-grid">
@@ -1207,6 +1272,33 @@ function renderOneLinerExecutionPayloadHtml(payload) {
   `;
 }
 
+function parseOneLinerActionPayloadValue(value) {
+  const text = String(value ?? "").trim();
+  if (!text) return "";
+  if (text === "true") return true;
+  if (text === "false") return false;
+  if (/^-?\d+(\.\d+)?$/.test(text)) return Number(text);
+  if ((text.startsWith("{") && text.endsWith("}")) || (text.startsWith("[") && text.endsWith("]"))) {
+    try {
+      return JSON.parse(text);
+    } catch (error) {
+      return text;
+    }
+  }
+  return text;
+}
+
+function collectOneLinerActionPayload(action) {
+  const reserved = new Set(["action", "executorKey", "platform", "sessionId", "disabledReason"]);
+  const payload = {};
+  Object.entries(action?.dataset || {}).forEach(([key, value]) => {
+    if (reserved.has(key)) return;
+    if (value === undefined || value === null || value === "") return;
+    payload[key] = parseOneLinerActionPayloadValue(value);
+  });
+  return payload;
+}
+
 async function executeOneLinerAction(executorKey, options = {}) {
   if (!backendSupports("/v2/oneliner/actions/execute")) {
     throw new Error("当前后端还没有接入 OneLiner 动作执行器。");
@@ -1219,7 +1311,7 @@ async function executeOneLinerAction(executorKey, options = {}) {
       action_key: executorKey,
       project_id: projectId,
       platform: options.platform || getPreferredPlatform(),
-      session_id: session?.id || "",
+      session_id: options.sessionId || session?.id || "",
       payload: options.payload || {}
     }
   });
@@ -4987,7 +5079,8 @@ document.addEventListener("click", async (event) => {
       try {
         await executeOneLinerAction(action.dataset.executorKey || "", {
           platform: action.dataset.platform || "",
-          payload: {}
+          sessionId: action.dataset.sessionId || "",
+          payload: collectOneLinerActionPayload(action)
         });
       } catch (error) {
         alert("执行 OneLiner 动作失败: " + error.message);