Upload folder using huggingface_hub

app.py

@@ -17,43 +17,49 @@ QUESTIONS = [
         "key": "input",
         "title": "\u7b2c1\u8f6e\uff1a\u8f93\u5165",
         "question": "\u8fd9\u4e2a\u5de5\u4f5c\u7684\u8f93\u5165\u662f\u4ec0\u4e48\uff1f\u4ece\u54ea\u91cc\u6765\uff1f\u4ec0\u4e48\u683c\u5f0f\uff1f",
-        "hint": "\u4f60\u53ef\u4ee5\u6309\u201c\u6765\u6e90 + \u5185\u5bb9 + \u683c\u5f0f\u201d\u56de\u7b54\u3002\u4e0d\u786e\u5b9a\u65f6\uff0c\u5148\u8bf4\u201c\u7528\u6237\u5728\u5bf9\u8bdd\u6846\u8f93\u5165\u7684\u81ea\u7136\u8bed\u8a00\u201d\u4e5f\u53ef\u4ee5\u3002",
-        "sample": "\u793a\u4f8b\uff1a\u8f93\u5165\u662f\u7528\u6237\u5728\u7f51\u9875\u5bf9\u8bdd\u6846\u91cc\u53d1\u6765\u7684\u6587\u6848\u9700\u6c42\uff0c\u683c\u5f0f\u662f\u4e00\u6bb5\u81ea\u7136\u8bed\u8a00\u6587\u672c\u3002",
+        "why": "先确认输入是否稳定、容易识别。输入越稳定，这个工作越适合做成 Agent。",
+        "options": "可选回答：A. 用户在对话框输入文字；B. 用户上传文件；C. 来自表格/表单；D. 来自固定文件夹；E. 还不确定，需要 Agent 追问。",
+        "sample": "参考回答：输入是用户在网页对话框里发来的文案需求，格式是一段自然语言文本。",
     },
     {
         "key": "workflow",
         "title": "\u7b2c2\u8f6e\uff1a\u5904\u7406\u52a8\u4f5c",
         "question": "\u62ff\u5230\u8f93\u5165\u540e\uff0c\u5177\u4f53\u8981\u505a\u54ea\u51e0\u6b65\uff1f\u8bf7\u63cf\u8ff0\u4e3b\u8981\u52a8\u4f5c\uff0c\u6211\u4f1a\u5e2e\u4f60\u62c6\u6210 3-8 \u4e2a\u6b65\u9aa4\u3002",
-        "hint": "\u5148\u5199\u7c97\u7565\u6d41\u7a0b\u5c31\u884c\uff0c\u4e0d\u9700\u8981\u50cf\u7a0b\u5e8f\u4e00\u6837\u7cbe\u786e\u3002\u5e38\u89c1\u6b65\u9aa4\uff1a\u8bc6\u522b\u9700\u6c42\u3001\u8865\u9f50\u4fe1\u606f\u3001\u751f\u6210\u521d\u7a3f\u3001\u68c0\u67e5\u8d28\u91cf\u3001\u5bfc\u51fa\u6587\u4ef6\u3002",
-        "sample": "\u793a\u4f8b\uff1a1. \u8bc6\u522b Agent \u7c7b\u578b\uff1b2. \u8ffd\u95ee\u7f3a\u5931\u4fe1\u606f\uff1b3. \u6574\u7406\u5c97\u4f4d\u5361\uff1b4. \u751f\u6210\u9879\u76ee\u5305\uff1b5. \u652f\u6301\u7528\u6237\u4fee\u6539\u3002",
+        "why": "这里用“先定岗位，再拆流程”的方法，把重复工作变成 Agent 能执行的动作。",
+        "options": "可选动作：识别需求、判断是否适合 Agent 化、追问缺失信息、生成初稿、检查质量、导出文件、支持用户修改。",
+        "sample": "参考回答：1. 识别 Agent 类型；2. 追问缺失信息；3. 整理岗位卡；4. 生成项目包；5. 支持用户修改。",
     },
     {
         "key": "output",
         "title": "\u7b2c3\u8f6e\uff1a\u8f93\u51fa",
         "question": "\u505a\u5b8c\u4e4b\u540e\u8f93\u51fa\u4ec0\u4e48\uff1f\u653e\u54ea\u91cc\uff1f\u4ec0\u4e48\u683c\u5f0f\uff1f",
-        "hint": "\u8bf4\u6e05\u4e09\u4ef6\u4e8b\uff1a\u4ea4\u4ed8\u7269\u662f\u4ec0\u4e48\u3001\u7528\u6237\u600e\u4e48\u62ff\u5230\u3001\u6587\u4ef6\u683c\u5f0f\u662f\u4ec0\u4e48\u3002",
-        "sample": "\u793a\u4f8b\uff1a\u8f93\u51fa\u4e00\u4e2a Agent \u53d8\u8eab\u5305 ZIP\uff0c\u5728\u9875\u9762\u4e0b\u8f7d\u6309\u94ae\u63d0\u4f9b\uff0c\u91cc\u9762\u5305\u542b AGENTS.md\u3001agent-spec.json\u3001docs \u548c skills\u3002",
+        "why": "Agent 的交付物要清晰、可验收，用户拿到后要知道怎么用。",
+        "options": "可选输出：一段回复、Markdown 文件、表格、图片提示词、ZIP 项目包、下载链接、保存到指定文件夹。",
+        "sample": "参考回答：输出一个 Agent 变身包 ZIP，在页面下载按钮提供，里面包含 AGENTS.md、agent-spec.json、docs 和 skills。",
     },
     {
         "key": "success",
         "title": "\u7b2c4\u8f6e\uff1a\u6210\u529f\u6807\u51c6",
         "question": "\u600e\u4e48\u5224\u65ad\u505a\u5bf9\u4e86\uff1f\u600e\u4e48\u5224\u65ad\u505a\u9519\u4e86\uff1f",
-        "hint": "\u53ef\u4ee5\u4ece\u201c\u6587\u4ef6\u662f\u5426\u9f50\u5168\u3001\u5185\u5bb9\u662f\u5426\u53ef\u6267\u884c\u3001\u7528\u6237\u662f\u5426\u80fd\u76f4\u63a5\u7528\u201d\u6765\u5224\u65ad\u3002",
-        "sample": "\u793a\u4f8b\uff1a\u505a\u5bf9\u4e86\u662f ZIP \u80fd\u4e0b\u8f7d\u3001\u6587\u4ef6\u9f50\u5168\u3001AGENTS.md \u53ef\u4ee5\u6307\u5bfc Agent \u5de5\u4f5c\uff1b\u505a\u9519\u4e86\u662f\u7f3a\u5b57\u6bb5\u3001\u6307\u4ee4\u7a7a\u6cdb\u6216\u65e0\u6cd5\u4f7f\u7528\u3002",
+        "why": "这里是在写验收标准。没有验收标准，Agent 很容易看似完成，实际不可用。",
+        "options": "可选标准：文件齐全、格式正确、步骤可执行、输出符合用户需求、能被另一个 Agent 读取、遇到不清楚会追问。",
+        "sample": "参考回答：做对了是 ZIP 能下载、文件齐全、AGENTS.md 可以指导 Agent 工作；做错了是缺字段、指令空泛或无法使用。",
     },
     {
         "key": "fallback",
         "title": "\u7b2c5\u8f6e\uff1a\u4eba\u5de5\u515c\u5e95",
         "question": "\u4ec0\u4e48\u60c5\u51b5\u9700\u8981\u4eba\u5de5\u4ecb\u5165\uff1f\u4f60\u5e0c\u671b\u5728\u54ea\u4e2a\u73af\u8282\u68c0\u67e5\uff1f",
-        "hint": "\u5e38\u89c1\u4eba\u5de5\u4ecb\u5165\uff1a\u9700\u6c42\u77db\u76fe\u3001\u4fe1\u606f\u4e0d\u8db3\u3001\u6d89\u53ca\u8d26\u53f7\u6743\u9650\u3001\u5bf9\u5916\u53d1\u5e03\u3001\u4ed8\u8d39\u3001\u9ad8\u98ce\u9669\u6216\u8fdd\u89c4\u5185\u5bb9\u3002",
-        "sample": "\u793a\u4f8b\uff1a\u7528\u6237\u9700\u6c42\u51b2\u7a81\u3001\u4fe1\u606f\u4e0d\u8db3\u3001\u6d89\u53ca\u5bf9\u5916\u53d1\u5e03\u6216\u8d26\u53f7\u6743\u9650\u65f6\u9700\u8981\u4eba\u5de5\u4ecb\u5165\uff1b\u5728\u6bcf\u8f6e\u5f52\u7eb3\u540e\u548c\u751f\u6210\u524d\u68c0\u67e5\u3002",
+        "why": "好 Agent 不是全自动乱跑，而是在关键风险点停下来让人确认。",
+        "options": "可选介入条件：需求矛盾、信息不足、用户目标不适合 Agent 化、涉及账号权限、对外发布、付费操作、高风险或违规内容。",
+        "sample": "参考回答：用户需求冲突、信息不足、涉及对外发布或账号权限时需要人工介入；在每轮归纳后和生成前检查。",
     },
     {
         "key": "out_of_scope",
         "title": "\u7b2c6\u8f6e\uff1a\u672c\u671f\u4e0d\u505a",
         "question": "\u6709\u4ec0\u4e48\u662f\u8fd9\u4e2a Agent \u73b0\u5728\u660e\u786e\u4e0d\u5e94\u8be5\u505a\u7684\uff1f\u8bf7\u5217 3-5 \u6761\u3002",
-        "hint": "\u8fd9\u91cc\u662f\u8bbe\u8fb9\u754c\uff0c\u9632\u6b62 Agent \u505a\u592a\u591a\u3002\u4f18\u5148\u5199\u9ad8\u98ce\u9669\u3001\u4e0d\u53ef\u9006\u3001\u8d85\u51fa\u672c\u671f\u8303\u56f4\u7684\u4e8b\u3002",
-        "sample": "\u793a\u4f8b\uff1a\u4e0d\u81ea\u52a8\u64cd\u4f5c\u7528\u6237\u8d26\u53f7\uff1b\u4e0d\u7ed5\u8fc7\u5e73\u53f0\u6743\u9650\uff1b\u4e0d\u751f\u6210\u8fdd\u6cd5\u8fdd\u89c4 Agent\uff1b\u4e0d\u5728\u4fe1\u606f\u4e0d\u6e05\u695a\u65f6\u7f16\u9020\u7ec6\u8282\u3002",
+        "why": "小岗位优先。先做一个边界清楚、能跑通闭环的 Agent，再逐步扩展。",
+        "options": "可选边界：不登录账号、不自动发布、不付款、不处理违法违规内容、不承诺结果、不在信息不足时编造、不做万能助手。",
+        "sample": "参考回答：不自动操作用户账号；不绕过平台权限；不生成违法违规 Agent；不在信息不清楚时编造细节。",
     },
 ]
 
@@ -105,7 +111,8 @@ def _format_question(question: dict[str, str]) -> str:
     return (
         f"{question['title']}\n\n"
         f"{question['question']}\n\n"
-        f"提示：{question.get('hint', '')}\n\n"
+        f"为什么问这个：{question.get('why', '')}\n\n"
+        f"不会答可以选：{question.get('options', '')}\n\n"
         f"{question.get('sample', '')}"
     )
 
@@ -115,7 +122,13 @@ def _current_helper_text(state: dict[str, Any]) -> str:
     if state.get("done"):
         return "已生成 Agent 变身包。你可以下载 ZIP，或在对话框里继续提出修改意见。"
     question = QUESTIONS[min(step, len(QUESTIONS) - 1)]
-    return f"{question['title']}\n\n{question.get('hint', '')}\n\n可直接参考：\n{question.get('sample', '')}"
+    return (
+        f"{question['title']}\n\n"
+        f"五维筛选：重复出现 / 输入稳定 / 步骤明确 / 输出可验收 / 人工兜底\n\n"
+        f"为什么问这个：{question.get('why', '')}\n\n"
+        f"不会答可以选：{question.get('options', '')}\n\n"
+        f"{question.get('sample', '')}"
+    )
 
 
 def _progress_text(state: dict[str, Any]) -> str:
@@ -201,16 +214,18 @@ def _llm_next_reply(summary: str, state: dict[str, Any], next_question: dict[str
 Current summary:
 {summary}
 
-Write the next message to the user in natural Chinese.
+Write the next message to the user in natural Chinese, using the OpenClaw-style Agent architect guidance.
 Requirements:
 - First confirm the summary in one concise sentence starting with "归纳确认：".
 - Then ask only this one next question:
 {next_question['title']}: {next_question['question']}
-- Also include this helpful hint and example after the question:
-- Use the Chinese label "提示：" followed by: {next_question.get('hint', '')}
-- Use the Chinese label "示例：" followed by: {next_question.get('sample', '')}
+- After the question, include exactly these three Chinese guidance labels:
+- "为什么问这个：" followed by: {next_question.get('why', '')}
+- "不会答可以选：" followed by: {next_question.get('options', '')}
+- Then include this reference answer exactly once: {next_question.get('sample', '')}
 - Do not ask multiple questions.
 - Do not output English labels such as "Hint" or "Example".
+- Do not use the old labels "提示：" or "示例：".
 """
     else:
         user_prompt = f"""Confirmed information:
@@ -357,7 +372,7 @@ def _save_card(card: str) -> str:
     return str(file_path)
 
 
-def _build_agent_spec(card: str, answers: dict[str, str]) -> dict[str, Any]:
+def _build_agent_spec(card: str, answers: dict[str, str]) -> dict[str, Any]:
     suitability = {
         "repeatable": "unknown",
         "stable_input": "confirmed" if answers.get("input") else "unknown",
@@ -384,11 +399,33 @@ def _build_agent_spec(card: str, answers: dict[str, str]) -> dict[str, Any]:
         "human_fallback": answers.get("fallback", "Ask for human intervention when key information is missing or risk is high."),
         "constraints": _split_items(answers.get("out_of_scope", ""), "Do not invent unconfirmed details."),
         "platforms": ["generic", "codex"],
-        "source_card": card,
-    }
-
-
-def _build_agents_md(card: str, answers: dict[str, str]) -> str:
+        "source_card": card,
+    }
+
+
+def _build_screening_doc(answers: dict[str, str]) -> str:
+    return f"""# 五维筛选
+
+这份 Agent 采用“先判断是否适合 Agent 化，再定岗位、拆流程、写 Profile”的制作方式。
+
+## 筛选结果
+
+- 是否重复出现：由用户需求场景确认，若只是一次性创意任务，建议先做成辅助 Agent。
+- 输入是否稳定、容易识别：{answers.get("input", "待确认")}
+- 处理步骤和判断规则是否明确：{answers.get("workflow", "待确认")}
+- 输出是否清晰、可验收：{answers.get("success", "待确认")}
+- 出问题时是否可以人工兜底：{answers.get("fallback", "待确认")}
+
+## 制作原则
+
+1. 小岗位优先：先做一个边界清楚的岗位，不做万能助手。
+2. 交付可验收：输出必须能被用户检查、下载或复用。
+3. 失败可兜底：遇到缺信息、冲突、高风险动作时停下来问人。
+4. 最小闭环优先：先跑通输入、处理、输出、验收，再扩展自动化。
+"""
+
+
+def _build_agents_md(card: str, answers: dict[str, str]) -> str:
     return f"""# { _agent_name(answers) }
 
 You are this repository's active Agent worker. Follow the role card below as your operating contract.
@@ -525,6 +562,7 @@ def _build_install_prompt(answers: dict[str, str]) -> str:
 
 - AGENTS.md
 - agent-spec.json
+- docs/00-five-dimension-screening.md
 - docs/01-role-card.md
 - docs/02-workflow.md
 - docs/03-profile.md
@@ -568,8 +606,9 @@ def _save_agent_package(card: str, answers: dict[str, str]) -> str:
     (package_dir / "START_HERE.md").write_text(_build_start_here(answers), encoding="utf-8")
     (package_dir / "INSTALL_PROMPT.md").write_text(_build_install_prompt(answers), encoding="utf-8")
     (package_dir / "AGENTS.md").write_text(_build_agents_md(card, answers), encoding="utf-8")
-    (package_dir / "agent-spec.json").write_text(json.dumps(spec, ensure_ascii=False, indent=2), encoding="utf-8")
-    (docs_dir / "01-role-card.md").write_text(card, encoding="utf-8")
+    (package_dir / "agent-spec.json").write_text(json.dumps(spec, ensure_ascii=False, indent=2), encoding="utf-8")
+    (docs_dir / "00-five-dimension-screening.md").write_text(_build_screening_doc(answers), encoding="utf-8")
+    (docs_dir / "01-role-card.md").write_text(card, encoding="utf-8")
     (docs_dir / "02-workflow.md").write_text(_build_workflow_doc(answers), encoding="utf-8")
     (docs_dir / "03-profile.md").write_text(_build_profile_doc(card, answers), encoding="utf-8")
     (docs_dir / "04-test-log.md").write_text(_build_test_log_doc(), encoding="utf-8")
@@ -586,7 +625,7 @@ This package was generated by Agent Architect.
 1. Unzip this package.
 2. Put `AGENTS.md` at the root of your Codex-style project.
 3. Keep `agent-spec.json` as the structured agent contract.
-4. Read `docs/01-role-card.md`, `docs/02-workflow.md`, and `docs/03-profile.md`.
+4. Read `docs/00-five-dimension-screening.md`, `docs/01-role-card.md`, `docs/02-workflow.md`, and `docs/03-profile.md`.
 5. Use `docs/04-test-log.md` to record real tests before long-term use.
 
 This package follows a role-card-first Agent project structure: role card, workflow, profile, test log, usage guide, and showcase notes.
@@ -688,17 +727,28 @@ def respond(message: str, history: list[dict[str, str]], state: dict[str, Any]):
     summary = _summarize(key, message)
     state["summaries"][key] = summary
 
-    if len(message) < 4:
-        history.append(_chat_line("assistant", f"我先归纳为：{summary}\n\n这个回答还比较短，可以再具体一点吗？"))
+    if len(message) < 4:
+        history.append(_chat_line(
+            "assistant",
+            f"归纳确认：{summary}\n\n这个回答还比较短，我怕生成出来会太空。你可以按下面任选一种补充：\n\n不会答可以选：{question.get('options', '')}\n\n{question.get('sample', '')}",
+        ))
         return _ui_result(history, state, state.get("file_path"))
 
     state["step"] += 1
     if state["step"] < len(QUESTIONS):
         next_q = QUESTIONS[state["step"]]
-        try:
-            assistant_reply = _llm_next_reply(summary, state, next_q)
-        except Exception as exc:
-            assistant_reply = f"归纳确认：{summary}\n\n{next_q['title']}\n\n{next_q['question']}\n\n提示：模型暂时不可用，已切换为规则追问。错误：{exc}"
+        try:
+            assistant_reply = _llm_next_reply(summary, state, next_q)
+        except Exception as exc:
+            assistant_reply = (
+                f"归纳确认：{summary}\n\n"
+                f"{next_q['title']}\n\n"
+                f"{next_q['question']}\n\n"
+                f"为什么问这个：{next_q.get('why', '')}\n\n"
+                f"不会答可以选：{next_q.get('options', '')}\n\n"
+                f"{next_q.get('sample', '')}\n\n"
+                f"系统说明：模型暂时不可用，已切换为规则追问。错误：{exc}"
+            )
         history.append(_chat_line("assistant", assistant_reply))
         return _ui_result(history, state, state.get("file_path"))