github-trending-wan-skill

GitHub 每日热门开源信息图 Skill 你是一个"GitHub 热门开源情报编辑 + 信息图策划师"。目标：抓取 GitHub Trending 当前榜单的 Top 5 开源项目，把摘要字段先翻译成中文，再生成简洁的中文简报与可直接给 Wan 2.7 使用的海报 Prompt。支持 10 种视觉风格，采用 3 步引导式工作流。 --- 专家思维框架在每个步骤开始前，先问自己： - 用户真正要什么？用户说"帮我看看今天热门项目"可能只要简报，不要图片。不要预设用户需要生图。 - 数据是否可信？抓取结果是实时快照，不代表某个固定时间点。如果抓取时间与用户指定日期有偏差，必须主动说明。 - 信息密度对吗？每次生成 prompt 后，想象这些文字渲染到一张 3:4 海报上——如果你自己都觉得读不完，就是太密了。 --- NEVER - NEVER 在用户未明确确认前直接执行 Wan 生图 Why : Wan 2.7 调用消耗 API 额度且不可撤销，用户可能只需要内容或 Prompt 而非最终图片 - NEVER 把"当前 Trending 榜单"说成"指定历史日期的真实榜单" Why : 抓取脚本只能获取当前实时榜单，无法回溯历史数据；错误声称会误导用户对数据时效性的判断 - NEVER 将 README 长段落原样塞进最终 prompt；必须使用脚本产出的摘要结果…

\n match = re.match(direct_pattern, input_str)\n if match:\n width = int(match.group(1))\n height = int(match.group(2))\n \n if not (768*768 \u003c= width * height \u003c= 2048*2048):\n raise ValueError(f\"Total pixel count must be within the range [768*768, 2048*2048], i.e., [{768*768}, {2048*2048}]. Current: {width}*{height}: {width*height}\")\n \n aspect_ratio = width / height\n if not (1/8 \u003c= aspect_ratio \u003c= 8):\n raise ValueError(f\"Aspect ratio must be within the range [1:8, 8:1]. Current: {aspect_ratio:.2f}\")\n \n return f\"{width}*{height}\"\n \n # Mode 2: K + aspect ratio (e.g., \"2K 3:4\", \"1K 16:9\")\n k_ratio_pattern = r'^([124])k\\s*(\\d+):(\\d+)

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

\n match = re.match(k_ratio_pattern, input_str)\n if match:\n k_value = int(match.group(1))\n ratio_w = int(match.group(2))\n ratio_h = int(match.group(3))\n \n \n total_pixels = min(max((k_value * 1024) ** 2, 1024*1024), 2048*2048)\n base_unit = (total_pixels / (ratio_w * ratio_h)) ** 0.5\n width = int(round(base_unit * ratio_w))\n height = int(round(base_unit * ratio_h))\n\n # Ensure it is even (required by some models)\n width = width if width % 2 == 0 else width - 1\n height = height if height % 2 == 0 else height - 1\n\n if not (768*768 \u003c= width * height \u003c= 2048*2048):\n raise ValueError(f\"Total pixel count must be within the range [768*768, 2048*2048], i.e., [{768*768}, {2048*2048}]. Calculated resolution is out of range: {width}*{height}: {width*height}\")\n \n aspect_ratio = width / height\n if not (1/8 \u003c= aspect_ratio \u003c= 8):\n raise ValueError(f\"Aspect ratio must be within the range [1:8, 8:1]. Current: {aspect_ratio:.2f}\")\n \n return f\"{width}*{height}\"\n \n # Mode 3: K value only (e.g., \"1K\", \"2K\"), defaulting to a 1:1 aspect ratio\n k_only_pattern = r'^([12])k

Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.

\n match = re.match(k_only_pattern, input_str)\n if match:\n k_value = int(match.group(1))\n size = k_value * 1024\n return f\"{size}*{size}\"\n\n\n raise ValueError(\n f\"Unsupported input format: '{input_str}'\\n\"\n f\"Supported input formats: \\n\"\n f\" - K + aspect ratio: '2K 3:4', '1K 16:9', '2K 1:1'\\n\"\n f\" - K value only: '1K', '2K' (defaulting to a 1:1 aspect ratio)\\n\"\n f\" - Directly specify resolution size: '1280*1280', '2048*2048'\"\n )\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description='Parses resolution strings, supporting K value format or specific pixel formats',\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog='''\nExample:\n python parse_resolution.py '2K 3:4' # output: 1536*2048\n python parse_resolution.py '1K 16:9' # output: 1696*960 \n python parse_resolution.py '2048*2048' # output: 2048*2048\n python parse_resolution.py '1K' # output: 1024*1024\n '''.strip()\n )\n \n parser.add_argument(\n 'input',\n help='Resolution input string, format like: \"2K 3:4\", \"2048*2048\", \"1K\"'\n )\n \n args = parser.parse_args()\n input_str = args.input\n \n try:\n result = parse_resolution(input_str)\n print(result)\n except ValueError as e:\n print(f\"Failed: {e}\", file=sys.stderr)\n sys.exit(1)\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":5199,"content_sha256":"1ce0784038588960015b76598cd4ae0a9391d7056f59d5338f290009dd4bbe7a"},{"filename":"scripts/run_wan_generation.py","content":"#!/usr/bin/env python3\n\"\"\"Run Wan 2.7 generation with a prepared prompt file.\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport re\nimport subprocess\nimport sys\nfrom datetime import datetime, timezone\nfrom pathlib import Path\n\nSKILL_ROOT = Path(__file__).resolve().parent.parent\nWAN_SKILL_DIR = Path(os.environ[\"WAN_SKILL_DIR\"]) if os.getenv(\"WAN_SKILL_DIR\") else (\n SKILL_ROOT.parent / \"Wan-skills-main\" / \"skills\" / \"wan2.7-image-skill\"\n)\nWAN_SCRIPTS_DIR = WAN_SKILL_DIR / \"scripts\"\n\nWAN_SCRIPT = WAN_SCRIPTS_DIR / \"image-generation-editing.py\"\nCHECK_SCRIPT = WAN_SCRIPTS_DIR / \"check_wan_task_status.py\"\nPARSE_RESOLUTION_SCRIPT = WAN_SCRIPTS_DIR / \"parse_resolution.py\"\nDEFAULT_PROMPT_PATH = Path(\"output/wan_prompt.txt\")\nOUTPUT_DIR = SKILL_ROOT / \"output\"\nRESULT_FILE = \"wan_result.json\"\n\nURL_PATTERN = re.compile(r\"result No\\.\\s*\\d+:\\s+(https?://\\S+)\")\nTASK_ID_PATTERN = re.compile(r\"(?:Dashscope\\s+)?TASK_ID:\\s+(\\S+)\", re.IGNORECASE)\n\n\ndef ensure_script(path: Path, label: str) -> None:\n if not path.is_file():\n raise FileNotFoundError(f\"缺少{label}脚本: {path}\")\n\n\ndef read_prompt(path: Path) -> str:\n if not path.is_file():\n raise FileNotFoundError(f\"Prompt 文件不存在: {path}\")\n content = path.read_text(encoding=\"utf-8\")\n if not content.strip():\n raise ValueError(f\"Prompt 文件为空: {path}\")\n return content.strip()\n\n\ndef resolve_size(size: str, ratio: str | None) -> str:\n normalized_size = size.strip()\n if not ratio:\n return normalized_size\n if \"*\" in normalized_size or \"x\" in normalized_size.lower():\n return normalized_size\n ensure_script(PARSE_RESOLUTION_SCRIPT, \"分辨率解析\")\n command = [sys.executable, str(PARSE_RESOLUTION_SCRIPT), f\"{normalized_size} {ratio}\"]\n result = subprocess.run(command, capture_output=True, text=True, check=False)\n if result.returncode != 0:\n detail = (result.stderr or result.stdout).strip() or \"未知错误\"\n raise ValueError(f\"分辨率解析失败: {detail}\")\n resolved_size = result.stdout.strip()\n if not resolved_size:\n raise ValueError(\"分辨率解析失败: 未返回有效 size\")\n return resolved_size\n\n\ndef ensure_env() -> None:\n if not os.getenv(\"DASHSCOPE_API_KEY\"):\n raise EnvironmentError(\"缺少 DASHSCOPE_API_KEY，当前只支持先生成内容和 Prompt。\")\n\n\ndef build_command(prompt: str, size: str, sequential: bool, number: int) -> list[str]:\n ensure_script(WAN_SCRIPT, \"Wan 生成\")\n command = [\n sys.executable,\n str(WAN_SCRIPT),\n \"--user_requirement\",\n prompt,\n \"--n\",\n str(number),\n \"--size\",\n size,\n ]\n if sequential:\n command.append(\"--enable_sequential\")\n return command\n\n\ndef run_subprocess(command: list[str], action: str) -> tuple[int, str]:\n result = subprocess.run(command, capture_output=True, text=True, check=False)\n stdout = result.stdout or \"\"\n if stdout:\n print(stdout, end=\"\" if stdout.endswith(\"\\n\") else \"\\n\")\n if result.returncode != 0:\n detail = (result.stderr or stdout).strip() or f\"{action}失败\"\n print(detail, file=sys.stderr)\n return result.returncode, stdout\n\n\ndef parse_generation_output(stdout: str) -> dict:\n urls = URL_PATTERN.findall(stdout)\n task_id_match = TASK_ID_PATTERN.search(stdout)\n task_id = task_id_match.group(1) if task_id_match else \"\"\n if urls:\n status = \"SUCCEEDED\"\n elif task_id:\n status = \"PENDING\"\n else:\n status = \"UNKNOWN\"\n return {\n \"task_id\": task_id,\n \"image_urls\": urls,\n \"generated_at\": datetime.now(timezone.utc).isoformat(),\n \"status\": status,\n }\n\n\ndef save_result(result_data: dict) -> Path:\n OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n result_path = OUTPUT_DIR / RESULT_FILE\n with open(result_path, \"w\", encoding=\"utf-8\") as handle:\n json.dump(result_data, handle, ensure_ascii=False, indent=2)\n print(f\"已保存结果: {result_path}\")\n return result_path\n\n\ndef run_generation(prompt_path: Path, size: str, ratio: str | None, sequential: bool, number: int) -> int:\n ensure_env()\n prompt = read_prompt(prompt_path)\n resolved_size = resolve_size(size, ratio)\n command = build_command(prompt, resolved_size, sequential, number)\n print(f\"开始调用 Wan 2.7 生图: prompt={prompt_path} size={resolved_size} n={number} sequential={str(sequential).lower()}\")\n code, stdout = run_subprocess(command, \"Wan 生图\")\n result_data = parse_generation_output(stdout)\n result_data[\"prompt_path\"] = str(prompt_path)\n result_data[\"size\"] = resolved_size\n result_data[\"number\"] = number\n save_result(result_data)\n return code\n\n\ndef check_task(task_id: str) -> int:\n ensure_env()\n ensure_script(CHECK_SCRIPT, \"Wan 任务查询\")\n command = [sys.executable, str(CHECK_SCRIPT), \"--task_id\", task_id]\n print(f\"开始查询 Wan 任务: task_id={task_id}\")\n code, stdout = run_subprocess(command, \"Wan 任务查询\")\n result_data = parse_generation_output(stdout)\n result_data[\"task_id\"] = task_id\n save_result(result_data)\n return code\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser(description=\"Run Wan 2.7 generation with prepared prompt\")\n parser.add_argument(\"--prompt\", default=str(DEFAULT_PROMPT_PATH), help=\"Prompt file path\")\n parser.add_argument(\"--size\", default=\"2K\", help=\"Requested size, e.g. 2K or 1774*2364\")\n parser.add_argument(\"--ratio\", default=\"3:4\", help=\"Aspect ratio used when size is K-based\")\n parser.add_argument(\"--sequential\", action=\"store_true\", help=\"Enable sequential multi-image generation\")\n parser.add_argument(\"--n\", type=int, default=1, help=\"Number of images to request\")\n parser.add_argument(\"--task-id\", default=\"\", help=\"Existing task id to query instead of generating\")\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n try:\n if args.task_id:\n code = check_task(args.task_id)\n else:\n code = run_generation(\n prompt_path=Path(args.prompt),\n size=args.size,\n ratio=args.ratio,\n sequential=args.sequential,\n number=args.n,\n )\n except (EnvironmentError, FileNotFoundError, ValueError) as exc:\n print(f\"执行失败: {exc}\", file=sys.stderr)\n raise SystemExit(1) from exc\n\n raise SystemExit(code)\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":6557,"content_sha256":"3680aaa3916a2cb7132ec6df635d6e59b5aa62e41ed0dc6469e9fac3c9684c47"},{"filename":"scripts/translate_daily_top.py","content":"#!/usr/bin/env python3\n\"\"\"Translate trending summary fields into Simplified Chinese via DashScope compatible chat API.\"\"\"\n\nfrom __future__ import annotations\n\nimport argparse\nimport json\nimport os\nimport random\nimport re\nimport sys\nfrom copy import deepcopy\nfrom datetime import datetime, timezone\nfrom typing import Any\nfrom urllib.error import HTTPError, URLError\nfrom urllib.request import Request, urlopen\n\nDEFAULT_INPUT = \"output/daily_top10.json\"\nDEFAULT_OUTPUT = \"output/daily_top5_zh.json\"\nDEFAULT_BASE_URL = \"https://dashscope.aliyuncs.com/compatible-mode/v1\"\nMODEL_CANDIDATES = [\n \"kimi-k2.5\",\n \"qwen3.5-flash-2026-02-23\",\n \"qwen3.5-397b-a17b\",\n \"MiniMax-M2.5\",\n \"qwen3.5-35b-a3b\",\n \"qwen3.5-plus\",\n \"qwen3.5-plus-2026-02-15\",\n \"glm-5\",\n \"qwen3-max-2026-01-23\",\n \"qwen3.5-27b\",\n \"tongyi-xiaomi-analysis-flash\",\n \"qwen3.6-plus-2026-04-02\",\n]\nENGLISH_RE = re.compile(r\"[A-Za-z]\")\nCODE_BLOCK_RE = re.compile(r\"^```(?:json)?\\s*|\\s*```$\", re.MULTILINE)\n\n\ndef read_json(path: str) -> dict[str, Any]:\n with open(path, \"r\", encoding=\"utf-8\") as handle:\n return json.load(handle)\n\n\ndef save_json(path: str, data: dict[str, Any]) -> None:\n parent = os.path.dirname(path)\n if parent:\n os.makedirs(parent, exist_ok=True)\n with open(path, \"w\", encoding=\"utf-8\") as handle:\n json.dump(data, handle, ensure_ascii=False, indent=2)\n\n\ndef ensure_env() -> str:\n api_key = os.getenv(\"DASHSCOPE_API_KEY\", \"\").strip()\n if not api_key:\n raise EnvironmentError(\"缺少 DASHSCOPE_API_KEY，无法执行中文翻译。\")\n return api_key\n\n\ndef normalize_base_url(base_url: str | None) -> str:\n candidate = (base_url or os.getenv(\"DASHSCOPE_COMPATIBLE_BASE_URL\") or \"\").strip()\n if not candidate:\n candidate = os.getenv(\"DASHSCOPE_BASE_URL\", \"\").strip()\n if not candidate:\n return DEFAULT_BASE_URL\n\n candidate = candidate.rstrip(\"/\")\n if candidate.endswith(\"/compatible-mode/v1\"):\n return candidate\n if candidate.endswith(\"/api/v1\"):\n return f\"{candidate[:-7]}/compatible-mode/v1\"\n if candidate.endswith(\"/v1\"):\n return candidate\n return f\"{candidate}/compatible-mode/v1\"\n\n\ndef contains_english(value: str) -> bool:\n return bool(value and ENGLISH_RE.search(value))\n\n\ndef should_translate_list(values: list[str]) -> bool:\n return any(contains_english(value) for value in values)\n\n\ndef extract_response_text(payload: dict[str, Any]) -> str:\n choices = payload.get(\"choices\") or []\n if not choices:\n raise ValueError(\"翻译接口未返回 choices\")\n message = choices[0].get(\"message\") or {}\n content = message.get(\"content\", \"\")\n if isinstance(content, list):\n chunks: list[str] = []\n for item in content:\n if isinstance(item, dict) and item.get(\"type\") == \"text\":\n chunks.append(item.get(\"text\", \"\"))\n content = \"\".join(chunks)\n if not isinstance(content, str) or not content.strip():\n raise ValueError(\"翻译接口返回空内容\")\n return content.strip()\n\n\ndef parse_json_text(text: str) -> dict[str, Any]:\n cleaned = CODE_BLOCK_RE.sub(\"\", text).strip()\n return json.loads(cleaned)\n\n\ndef post_json(url: str, headers: dict[str, str], payload: dict[str, Any]) -> dict[str, Any]:\n body = json.dumps(payload, ensure_ascii=False).encode(\"utf-8\")\n request = Request(url, data=body, headers=headers, method=\"POST\")\n with urlopen(request, timeout=120) as response:\n return json.loads(response.read().decode(\"utf-8\", errors=\"replace\"))\n\n\ndef translate_item(item: dict[str, Any], api_key: str, base_url: str, model: str) -> tuple[dict[str, Any], int]:\n fields = {\n \"description\": item.get(\"description\", \"\"),\n \"summary_intro\": item.get(\"summary_intro\", \"\"),\n \"tech_stack\": item.get(\"tech_stack\") or [],\n \"target_users\": item.get(\"target_users\", \"\"),\n }\n if not (\n contains_english(fields[\"description\"])\n or contains_english(fields[\"summary_intro\"])\n or contains_english(fields[\"target_users\"])\n or should_translate_list(fields[\"tech_stack\"])\n ):\n return item, 0\n\n endpoint = f\"{base_url}/chat/completions\"\n system_prompt = (\n \"你是技术内容翻译助手。请把输入字段翻译成简体中文，要求忠实、简洁、不扩写。\"\n \"保留仓库名、编程语言名、框架名、产品名、协议名和常见技术术语；\"\n \"如果技术栈条目本身就是专有名词，可直接保留；返回 JSON，不要加解释。\"\n )\n user_prompt = json.dumps(\n {\n \"task\": \"translate_fields_to_zh_cn\",\n \"schema\": {\n \"description\": \"string\",\n \"summary_intro\": \"string\",\n \"tech_stack\": [\"string\"],\n \"target_users\": \"string\",\n },\n \"input\": fields,\n },\n ensure_ascii=False,\n )\n payload = {\n \"model\": model,\n \"messages\": [\n {\"role\": \"system\", \"content\": system_prompt},\n {\"role\": \"user\", \"content\": user_prompt},\n ],\n \"temperature\": 0.2,\n }\n headers = {\n \"Content-Type\": \"application/json\",\n \"Authorization\": f\"Bearer {api_key}\",\n }\n\n response = post_json(endpoint, headers, payload)\n translated = parse_json_text(extract_response_text(response))\n result = dict(item)\n result[\"description\"] = translated.get(\"description\", fields[\"description\"]).strip() or fields[\"description\"]\n result[\"summary_intro\"] = translated.get(\"summary_intro\", fields[\"summary_intro\"]).strip() or fields[\"summary_intro\"]\n tech_stack = translated.get(\"tech_stack\", fields[\"tech_stack\"])\n if isinstance(tech_stack, list):\n result[\"tech_stack\"] = [str(value).strip() for value in tech_stack if str(value).strip()]\n result[\"target_users\"] = translated.get(\"target_users\", fields[\"target_users\"]).strip() or fields[\"target_users\"]\n changed_count = sum(\n 1\n for key in (\"description\", \"summary_intro\", \"target_users\")\n if result.get(key) != item.get(key)\n )\n if result.get(\"tech_stack\") != item.get(\"tech_stack\"):\n changed_count += 1\n return result, changed_count\n\n\ndef translate_payload(data: dict[str, Any], api_key: str, base_url: str, model: str) -> tuple[dict[str, Any], int]:\n translated = deepcopy(data)\n items = translated.get(\"items\") or []\n total_changes = 0\n translated_items: list[dict[str, Any]] = []\n for item in items:\n translated_item, changed_count = translate_item(item, api_key, base_url, model)\n translated_items.append(translated_item)\n total_changes += changed_count\n translated[\"items\"] = translated_items\n translated[\"translation_model\"] = model\n translated[\"translated_at\"] = datetime.now(timezone.utc).isoformat()\n translated[\"translation_target_language\"] = \"zh-CN\"\n translated[\"translation_changed_fields\"] = total_changes\n return translated, total_changes\n\n\ndef parse_args() -> argparse.Namespace:\n parser = argparse.ArgumentParser(description=\"Translate daily trending summary fields into Simplified Chinese\")\n parser.add_argument(\"--input\", default=DEFAULT_INPUT, help=\"Input JSON path\")\n parser.add_argument(\"--output\", default=DEFAULT_OUTPUT, help=\"Output JSON path\")\n parser.add_argument(\"--model\", default=\"\", help=\"Optional explicit model; default picks one random candidate\")\n parser.add_argument(\"--base-url\", default=\"\", help=\"Optional compatible-mode base URL\")\n return parser.parse_args()\n\n\ndef main() -> None:\n args = parse_args()\n api_key = ensure_env()\n model = args.model.strip() or random.choice(MODEL_CANDIDATES)\n base_url = normalize_base_url(args.base_url)\n try:\n data = read_json(args.input)\n translated, changed_count = translate_payload(data, api_key, base_url, model)\n save_json(args.output, translated)\n except (FileNotFoundError, json.JSONDecodeError, HTTPError, URLError, TimeoutError, ValueError) as exc:\n print(f\"翻译失败: {exc}\", file=sys.stderr)\n raise SystemExit(1) from exc\n\n print(f\"已保存: {args.output}\")\n print(f\"翻译模型: {model}\")\n print(f\"字段变更数: {changed_count}\")\n\n\nif __name__ == \"__main__\":\n main()\n","content_type":"text/x-python; charset=utf-8","language":"python","size":8315,"content_sha256":"dc39f2b3f3f3b726965df27f72aabc0d9ec2fdf58717d9b2a4f27e6f2348bbf1"}],"content_json":{"type":"doc","content":[{"type":"heading","attrs":{"level":1},"content":[{"text":"GitHub 每日热门开源信息图 Skill","type":"text"}]},{"type":"paragraph","content":[{"text":"你是一个\"GitHub 热门开源情报编辑 + 信息图策划师\"。","type":"text"}]},{"type":"paragraph","content":[{"text":"目标：抓取 GitHub Trending 当前榜单的 ","type":"text"},{"text":"Top 5","type":"text","marks":[{"type":"strong"}]},{"text":" 开源项目，把摘要字段先翻译成中文，再生成简洁的中文简报与可直接给 ","type":"text"},{"text":"Wan 2.7","type":"text","marks":[{"type":"strong"}]},{"text":" 使用的海报 Prompt。支持 ","type":"text"},{"text":"10 种视觉风格","type":"text","marks":[{"type":"strong"}]},{"text":"，采用 ","type":"text"},{"text":"3 步引导式工作流","type":"text","marks":[{"type":"strong"}]},{"text":"。","type":"text"}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"专家思维框架","type":"text"}]},{"type":"paragraph","content":[{"text":"在每个步骤开始前，先问自己：","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"用户真正要什么？","type":"text","marks":[{"type":"strong"}]},{"text":" 用户说\"帮我看看今天热门项目\"可能只要简报，不要图片。不要预设用户需要生图。","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"数据是否可信？","type":"text","marks":[{"type":"strong"}]},{"text":" 抓取结果是实时快照，不代表某个固定时间点。如果抓取时间与用户指定日期有偏差，必须主动说明。","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"信息密度对吗？","type":"text","marks":[{"type":"strong"}]},{"text":" 每次生成 prompt 后，想象这些文字渲染到一张 3:4 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展示给用户确认","type":"text"}]},{"type":"paragraph","content":[{"text":"向用户返回：","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"daily_brief.md","type":"text","marks":[{"type":"code_inline"}]},{"text":" 的核心内容摘要（Top 5 项目卡片 + 热点方向 + 结论）","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"当前使用的日期 / 读者 / 风格 / 分辨率","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"当前随机选用的翻译模型","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"说明：回复\"确认生图\"后进入步骤 3","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"⚠️ 如果用户只要内容或 Prompt，到这里停止，不进入步骤 3。","type":"text","marks":[{"type":"strong"}]}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":3},"content":[{"text":"步骤 3：确认后生图","type":"text"}]},{"type":"paragraph","content":[{"text":"前置条件","type":"text","marks":[{"type":"strong"}]},{"text":"：步骤 2 全部完成且自查清单通过。若步骤 2 中任一脚本失败且未恢复，不得进入此步骤。","type":"text"}]},{"type":"paragraph","content":[{"text":"只有用户明确回复\"确认生图\"后，才执行：","type":"text","marks":[{"type":"strong"}]}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"python3 \"\u003cSKILL_ROOT>/scripts/run_wan_generation.py\" \\\n --prompt \"\u003cSKILL_ROOT>/output/wan_prompt.txt\" \\\n --size \"\u003cSIZE>\" \\\n --ratio \"\u003cRATIO>\" \\\n --n 1","type":"text"}]},{"type":"paragraph","content":[{"text":"执行约定：","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"默认海报模式为 ","type":"text"},{"text":"文生单图","type":"text","marks":[{"type":"strong"}]},{"text":"，即 ","type":"text"},{"text":"--n 1","type":"text","marks":[{"type":"code_inline"}]},{"text":" 且不启用 ","type":"text"},{"text":"--sequential","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"run_wan_generation.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" 会读取现成的 ","type":"text"},{"text":"wan_prompt.txt","type":"text","marks":[{"type":"code_inline"}]},{"text":"，并调用参考 Wan 2.7 脚本","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"若缺少 ","type":"text"},{"text":"DASHSCOPE_API_KEY","type":"text","marks":[{"type":"code_inline"}]},{"text":"，无法完成中文翻译，应停止并明确告知用户","type":"text"}]}]}]},{"type":"paragraph","content":[{"text":"如果返回 ","type":"text"},{"text":"task_id","type":"text","marks":[{"type":"code_inline"}]},{"text":"，继续查状态：","type":"text"}]},{"type":"code_block","attrs":{"wrap":false,"language":"bash"},"content":[{"text":"python3 \"\u003cSKILL_ROOT>/scripts/run_wan_generation.py\" \\\n --task-id \"\u003ctask_id>\"","type":"text"}]},{"type":"paragraph","content":[{"text":"查询约定：","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"若状态为 ","type":"text"},{"text":"RUNNING","type":"text","marks":[{"type":"code_inline"}]},{"text":"，把 ","type":"text"},{"text":"task_id","type":"text","marks":[{"type":"code_inline"}]},{"text":" 返回给用户，提示稍后继续查询","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"若状态为 ","type":"text"},{"text":"SUCCEEDED","type":"text","marks":[{"type":"code_inline"}]},{"text":"，返回最终图片 URL","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"若状态为 ","type":"text"},{"text":"FAILED","type":"text","marks":[{"type":"code_inline"}]},{"text":"，返回失败信息，不擅自降级用户需求","type":"text"}]}]}]},{"type":"hr","attrs":{"markup":"---"}},{"type":"heading","attrs":{"level":2},"content":[{"text":"输出物","type":"text"}]},{"type":"paragraph","content":[{"text":"固定输出目录：","type":"text"}]},{"type":"bullet_list","content":[{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"\u003cSKILL_ROOT>/output/daily_top10.json","type":"text","marks":[{"type":"code_inline"}]},{"text":" — 抓取原始数据","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"\u003cSKILL_ROOT>/output/daily_top5_zh.json","type":"text","marks":[{"type":"code_inline"}]},{"text":" — 翻译后数据","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"\u003cSKILL_ROOT>/output/daily_brief.md","type":"text","marks":[{"type":"code_inline"}]},{"text":" — 中文简报","type":"text"}]}]},{"type":"list_item","content":[{"type":"paragraph","content":[{"text":"\u003cSKILL_ROOT>/output/wan_prompt.txt","type":"text","marks":[{"type":"code_inline"}]},{"text":" — Wan 2.7 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返回非零退出码或输出为空","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"告知用户\"GitHub Trending 暂时无法访问\"，建议稍后重试，不要编造数据","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"翻译超时","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"translate_daily_top.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" 超过 60 秒无响应","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"终止进程，换一个候选模型重试一次；若仍失败，使用英文原文继续并告知用户","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"API Key 无效","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"DASHSCOPE_API_KEY","type":"text","marks":[{"type":"code_inline"}]},{"text":" 未设置或返回 401/403","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"立即停止，提示用户检查环境变量，不要尝试跳过翻译步骤直接生图","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"空榜单","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"抓取结果 items 数组为空","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"告知用户\"当前 Trending 榜单为空（可能处于刷新期）\"，不要用缓存数据或虚构项目填充","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"翻译模型不可用","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"随机选取的模型返回错误","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"从候选列表中排除该模型，随机选取下一个重试，最多重试 2 次","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"Wan 生图失败","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"run_wan_generation.py","type":"text","marks":[{"type":"code_inline"}]},{"text":" 返回 FAILED 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参数透传","type":"text"}]}]}]},{"type":"tr","content":[{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"scripts/run_wan_generation.py","type":"text","marks":[{"type":"code_inline"}]}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"生图失败需排查参数或输出解析问题时","type":"text"}]}]},{"type":"td","attrs":{"colspan":1,"rowspan":1,"colwidth":null,"alignment":""},"content":[{"type":"paragraph","content":[{"text":"正常生图流程中直接调用 CLI 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Important: agents should read /llm.txt, /llms.txt, or /.well-known/skills.json to discover the public Skillopedia API.