| content |
[{"type":"thinking","text" [{"type":"thinking","text":"The user wants a detailed explanation of how the cuiqu-export skill is organized. Let me re-read the SKILL.md carefully and also check the template file referenced in the docs, as well as any related scripts or references.\n\nI already have the SKILL.md content. Let me also look at the handbook template and any other related files."},{"type":"thinking","text":"The user wants a detailed explanation of how the cuiqu-export skill is organized. Let me re-read the SKILL.md carefully and also check the template file referenced in the docs, as well as any related scripts or references.\n\nI already have the SKILL.md content. Let me also look at the handbook template and any other related files."},{"type":"tool_use","id":"call_00_ZQ8U3kSqxzQ1yMjWezJq1062","name":"Read","input":{"file_path":"/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/docs/templates/handbook-template.html"}},{"type":"tool_result","tool_use_id":"call_00_ZQ8U3kSqxzQ1yMjWezJq1062","content":"1\t<!DOCTYPE html>\n2\t<html lang=\"zh-CN\">\n3\t<head>\n4\t <meta charset=\"UTF-8\">\n5\t <title>手册视觉模板 · v1.0</title>\n6\t <style>\n7\t * { box-sizing: border-box; }\n8\t body {\n9\t font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'PingFang SC', 'Hiragino Sans GB', 'Microsoft YaHei', sans-serif;\n10\t line-height: 1.75;\n11\t color: #2d2d2d;\n12\t max-width: 960px;\n13\t margin: 0 auto;\n14\t padding: 30px 20px;\n15\t background: #f4f6f9;\n16\t }\n17\t .container {\n18\t background: #fff;\n19\t padding: 50px 60px;\n20\t border-radius: 10px;\n21\t box-shadow: 0 2px 20px rgba(0,0,0,0.06);\n22\t }\n23\t .doc-header {\n24\t text-align: center;\n25\t margin-bottom: 40px;\n26\t padding-bottom: 30px;\n27\t border-bottom: 2px solid #eaecef;\n28\t }\n29\t .doc-header h1 {\n30\t font-size: 26px;\n31\t color: #1a1a2e;\n32\t margin-bottom: 10px;\n33\t line-height: 1.4;\n34\t }\n35\t .doc-header .subtitle {\n36\t color: #6c757d;\n37\t font-size: 15px;\n38\t }\n39\t .meta-bar {\n40\t display: flex;\n41\t justify-content: center;\n42\t gap: 24px;\n43\t margin-top: 14px;\n44\t flex-wrap: wrap;\n45\t }\n46\t .meta-item {\n47\t font-size: 13px;\n48\t color: #9ca3af;\n49\t }\n50\t .meta-item strong {\n51\t color: #6b7280;\n52\t }\n53\t h2 {\n54\t font-size: 20px;\n55\t color: #1a1a2e;\n56\t margin-top: 50px;\n57\t margin-bottom: 18px;\n58\t padding-bottom: 8px;\n59\t border-bottom: 1px solid #eaecef;\n60\t }\n61\t h2::before {\n62\t content: '';\n63\t display: inline-block;\n64\t width: 4px;\n65\t height: 18px;\n66\t backgrou [truncated]","is_error":false},{"type":"tool_raw_result","tool_use_id":"call_00_ZQ8U3kSqxzQ1yMjWezJq1062","tool_name":"Read","raw_content":{"type":"text","text":"1\t<!DOCTYPE html>\n2\t<html lang=\"zh-CN\">\n3\t<head>\n4\t <meta charset=\"UTF-8\">\n5\t <title>手册视觉模板 · v1.0</title>\n6\t <style>\n7\t * { box-sizing: border-box; }\n8\t body {\n9\t font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'PingFang SC', 'Hiragino Sans GB', 'Microsoft YaHei', sans-serif;\n10\t line-height: 1.75;\n11\t color: #2d2d2d;\n12\t max-width: 960px;\n13\t margin: 0 auto;\n14\t padding: 30px 20px;\n15\t background: #f4f6f9;\n16\t }\n17\t .container {\n18\t background: #fff;\n19\t padding: 50px 60px;\n20\t border-radius: 10px;\n21\t box-shadow: 0 2px 20px rgba(0,0,0,0.06);\n22\t }\n23\t .doc-header {\n24\t text-align: center;\n25\t margin-bottom: 40px;\n26\t padding-bottom: 30px;\n27\t border-bottom: 2px solid #eaecef;\n28\t }\n29\t .doc-header h1 {\n30\t font-size: 26px;\n31\t color: #1a1a2e;\n32\t margin-bottom: 10px;\n33\t line-height: 1.4;\n34\t }\n35\t .doc-header .subtitle {\n36\t color: #6c757d;\n37\t font-size: 15px;\n38\t }\n39\t .meta-bar {\n40\t display: flex;\n41\t justify-content: center;\n42\t gap: 24px;\n43\t margin-top: 14px;\n44\t flex-wrap: wrap;\n45\t }\n46\t .meta-item {\n47\t font-size: 13px;\n48\t color: #9ca3af;\n49\t }\n50\t .meta-item strong {\n51\t color: #6b7280;\n52\t }\n53\t h2 {\n54\t font-size: 20px;\n55\t color: #1a1a2e;\n56\t margin-top: 50px;\n57\t margin-bottom: 18px;\n58\t padding-bottom: 8px;\n59\t border-bottom: 1px solid #eaecef;\n60\t }\n61\t h2::before {\n62\t content: '';\n63\t display: inline-block;\n64\t width: 4px;\n65\t height: 18px;\n66\t background: #3b82f6;\n67\t margin-right: 10px;\n68\t vertical-align: middle;\n69\t border-radius: 2px;\n70\t }\n71\t h3 {\n72\t font-size: 17px;\n73\t color: #374151;\n74\t margin-top: 30px;\n75\t margin-bottom: 12px;\n76\t }\n77\t h4 {\n78\t font-size: 15px;\n79\t color: #4b5563;\n80\t margin-top: 22px;\n81\t margin-bottom: 10px;\n82\t font-weight: 600;\n83\t }\n84\t p {\n85\t margin-bottom: 16px;\n86\t text-align: justify;\n87\t }\n88\t table {\n89\t width: 100%;\n90\t border-collapse: collapse;\n91\t margin: 22px 0;\n92\t font-size: 14px;\n93\t }\n94\t th {\n95\t background: #f0f4ff;\n96\t color: #1e40af;\n97\t padding: 11px 14px;\n98\t text-align: left;\n99\t font-weight: 600;\n100\t border: 1px solid #dde3f0;\n101\t }\n102\t\n[truncated]"}},{"type":"tool_use","id":"call_01_QuR2A98hRpLpMAXUk0KX8201","name":"Bash","input":{"command":"find /home/forge/workspace/forgeworkspace/AI-Extraction-Engine/scripts -name \"generate_deliverable*\" -o -name \"generate_dossier*\" | sort","description":"Find export-related scripts"}},{"type":"tool_result","tool_use_id":"call_01_QuR2A98hRpLpMAXUk0KX8201","content":"/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/scripts/generate_deliverable.py\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/scripts/generate_dossier.py","is_error":false},{"type":"tool_raw_result","tool_use_id":"call_01_QuR2A98hRpLpMAXUk0KX8201","tool_name":"Bash","raw_content":{"type":"text","text":"/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/scripts/generate_deliverable.py\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/scripts/generate_dossier.py"}},{"type":"tool_use","id":"call_02_WYIRAdVhP3giNBmqK7P81970","name":"Bash","input":{"command":"find /home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables -type f 2>/dev/null | head -30","description":"Check existing deliverables"}},{"type":"tool_result","tool_use_id":"call_02_WYIRAdVhP3giNBmqK7P81970","content":"/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/summary.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/handbook.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/co-creation-dossier.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/summary.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/handbook.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/co-creation-dossier.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/summary.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/handbook.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/ [truncated]","is_error":false},{"type":"tool_raw_result","tool_use_id":"call_02_WYIRAdVhP3giNBmqK7P81970","tool_name":"Bash","raw_content":{"type":"text","text":"/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/summary.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-19_expert-003/handbook.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/co-creation-dossier.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/summary.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-06-27_expert-001/handbook.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/co-creation-dossier.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/summary.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-16_xiaoguo-001/handbook.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/co-creation-dossier.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/summary.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-05-08_xiayu-001/handbook.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-15_shichao-001/summary.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-15_shichao-001/full.md\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-15_shichao-001/co-creation-dossier.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-15_shichao-001/full.html\n/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/deliverables/2026-07-15_shichao-001/summary.html\n/home/forge/workspace/for\n[truncated]"}},{"type":"thinking","text":"Now let me also look at the dossier script and an existing dossier HTML to understand its structure better."},{"type":"tool_use","id":"call_00_7RJnFlBqjbH8RMutao4F7419","name":"Read","input":{"file_path":"/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/scripts/generate_deliverable.py"}},{"type":"tool_result","tool_use_id":"call_00_7RJnFlBqjbH8RMutao4F7419","content":"1\t# scripts/generate_deliverable.py\n2\t\"\"\"生成客户成果物(spec §5.7)。\n3\t\n4\t把一个 session 的所有产物(meta.json / interview_state.json / wiki/index.json\n5\t过滤后的卡 + 每张卡的完整 JSON-LD)渲染成两套文档:\n6\t\n7\tMarkdown(底稿,适合 Git 版本追踪):\n8\t- summary.md:一页纸成果汇总(≤ 300 字)\n9\t- full.md:完整萃取文档(按 episode 组织,六层次错位 + 原话锚点 +\n10\t 失败边界 + inferred 标红 + 补槽提示)\n11\t\n12\tHTML(客户可读,基于 PRD 视觉风格):\n13\t- summary.html:同上,可视化为卡片墙 + 覆盖度仪表 + 后续 callout\n14\t- full.html:同上,六层次错位渲染为色彩分层 callout,原话为引言卡\n15\t\n16\t约束(spec §5.7):\n17\t- 确定性渲染,无 LLM 调用\n18\t- inferred 字段必须显式 ⚠️ [推断] 前缀(HC-5 透明性延伸)\n19\t- 含 inferred 字段的卡,episode 标题加 🚧 待校核\n20\t- 缺失 layer 直接写\"(访谈未提及)\",不用 \"TBD\" / \"无\"\n21\t- pending-review 卡的章节标题加 🚧 待校核 前缀\n22\t\n23\t仅依赖标准库 + pyyaml(模板加载用)。\n24\t\"\"\"\n25\tfrom __future__ import annotations\n26\timport json\n27\timport os\n28\timport re\n29\timport sys\n30\timport tempfile\n31\tfrom datetime import datetime, timezone\n32\tfrom pathlib import Path\n33\t\n34\timport yaml\n35\t\n36\t# 六层次中文标签(spec §3 词汇表) + 对应的 JSON-LD 字段路径\n37\tLAYER_FIELDS = [\n38\t (\"道(为什么这招有效)\", \"k2j:daoBelief\"),\n39\t (\"法(方法论框架)\", \"k2j:faFramework\"),\n40\t (\"术(具体动作)\", \"k2j:shuTactics\"),\n41\t (\"策(if-then 决策)\", \"k2j:ceStrategy\"),\n42\t (\"器(工具/模板)\", \"k2j:qiTool\"),\n43\t (\"坑(新人最容易踩)\", \"k2j:kengTrap\"),\n44\t]\n45\t\n46\t# 主导层中文(用于 summary.md 一句话洞察)\n47\tDOMINANT_LAYER_LABEL = {\n48\t \"Dao\": \"道\",\n49\t \"Fa\": \"法\",\n50\t \"Shu\": \"术\",\n51\t \"Ce\": \"策\",\n52\t \"Qi\": \"器\",\n53\t \"Keng\": \"坑\",\n54\t}\n55\t\n56\tDEFAULT_OUT_DIR = \"deliverables\"\n57\t\n58\t\n59\t# ---- 加载 ----\n60\t\n61\tdef _read_json(path: Path) -> dict:\n62\t \"\"\"读 JSON 文件;不存在/损坏返回空 dict。\"\"\"\n63\t try:\n64\t return json.loads(Path(path).read_text(encoding=\"utf-8\"))\n65\t except (json.JSONDecodeError, OSError):\n66\t return {}\n67\t\n68\t\n69\tdef load_session_cards(session_id: str, wiki_root: Path = Path(\"wiki\")) -> list[dict]:\n70\t \"\"\"从 wiki/index.json 过滤出该 session 的所有卡,并加载完整 JSON-LD。\n71\t\n72\t 过滤逻辑:index 项的 `expert` 字段 = session_id(spec §5.6: expert 来自\n73\t provenance.k2j:sessionId)。同 episode 但跨 session 的卡不混入。\n74\t \"\"\"\n75\t wiki_root = Path(wik [truncated]","is_error":false},{"type":"tool_raw_result","tool_use_id":"call_00_7RJnFlBqjbH8RMutao4F7419","tool_name":"Read","raw_content":{"type":"text","text":"1\t# scripts/generate_deliverable.py\n2\t\"\"\"生成客户成果物(spec §5.7)。\n3\t\n4\t把一个 session 的所有产物(meta.json / interview_state.json / wiki/index.json\n5\t过滤后的卡 + 每张卡的完整 JSON-LD)渲染成两套文档:\n6\t\n7\tMarkdown(底稿,适合 Git 版本追踪):\n8\t- summary.md:一页纸成果汇总(≤ 300 字)\n9\t- full.md:完整萃取文档(按 episode 组织,六层次错位 + 原话锚点 +\n10\t 失败边界 + inferred 标红 + 补槽提示)\n11\t\n12\tHTML(客户可读,基于 PRD 视觉风格):\n13\t- summary.html:同上,可视化为卡片墙 + 覆盖度仪表 + 后续 callout\n14\t- full.html:同上,六层次错位渲染为色彩分层 callout,原话为引言卡\n15\t\n16\t约束(spec §5.7):\n17\t- 确定性渲染,无 LLM 调用\n18\t- inferred 字段必须显式 ⚠️ [推断] 前缀(HC-5 透明性延伸)\n19\t- 含 inferred 字段的卡,episode 标题加 🚧 待校核\n20\t- 缺失 layer 直接写\"(访谈未提及)\",不用 \"TBD\" / \"无\"\n21\t- pending-review 卡的章节标题加 🚧 待校核 前缀\n22\t\n23\t仅依赖标准库 + pyyaml(模板加载用)。\n24\t\"\"\"\n25\tfrom __future__ import annotations\n26\timport json\n27\timport os\n28\timport re\n29\timport sys\n30\timport tempfile\n31\tfrom datetime import datetime, timezone\n32\tfrom pathlib import Path\n33\t\n34\timport yaml\n35\t\n36\t# 六层次中文标签(spec §3 词汇表) + 对应的 JSON-LD 字段路径\n37\tLAYER_FIELDS = [\n38\t (\"道(为什么这招有效)\", \"k2j:daoBelief\"),\n39\t (\"法(方法论框架)\", \"k2j:faFramework\"),\n40\t (\"术(具体动作)\", \"k2j:shuTactics\"),\n41\t (\"策(if-then 决策)\", \"k2j:ceStrategy\"),\n42\t (\"器(工具/模板)\", \"k2j:qiTool\"),\n43\t (\"坑(新人最容易踩)\", \"k2j:kengTrap\"),\n44\t]\n45\t\n46\t# 主导层中文(用于 summary.md 一句话洞察)\n47\tDOMINANT_LAYER_LABEL = {\n48\t \"Dao\": \"道\",\n49\t \"Fa\": \"法\",\n50\t \"Shu\": \"术\",\n51\t \"Ce\": \"策\",\n52\t \"Qi\": \"器\",\n53\t \"Keng\": \"坑\",\n54\t}\n55\t\n56\tDEFAULT_OUT_DIR = \"deliverables\"\n57\t\n58\t\n59\t# ---- 加载 ----\n60\t\n61\tdef _read_json(path: Path) -> dict:\n62\t \"\"\"读 JSON 文件;不存在/损坏返回空 dict。\"\"\"\n63\t try:\n64\t return json.loads(Path(path).read_text(encoding=\"utf-8\"))\n65\t except (json.JSONDecodeError, OSError):\n66\t return {}\n67\t\n68\t\n69\tdef load_session_cards(session_id: str, wiki_root: Path = Path(\"wiki\")) -> list[dict]:\n70\t \"\"\"从 wiki/index.json 过滤出该 session 的所有卡,并加载完整 JSON-LD。\n71\t\n72\t 过滤逻辑:index 项的 `expert` 字段 = session_id(spec §5.6: expert 来自\n73\t provenance.k2j:sessionId)。同 episode 但跨 session 的卡不混入。\n74\t \"\"\"\n75\t wiki_root = Path(wiki_root)\n76\t index = _read_json(wiki_root / \"index.json\")\n77\t cards = []\n78\t for entry in index.get(\"cards\", []) if isinstance(index.get(\"cards\"), list) else []:\n79\t if entry.get(\"expert\") != session_id:\n80\t continue\n81\t card_path = wiki_root.parent / entry.get(\"path\", \"\") if entry.get(\"path\") else None\n82\t # entry.path 是 \"wiki/rules/X.jsonld\" 形式;wiki_root 是 \"wiki\",\n83\t # 所以 path 相对 wiki_root.parent(仓库根)解析\n84\t rel = entry.get(\"path\", \"\")\n85\t if rel.startswith(\"wiki/\"):\n86\t card_path = wiki_root.parent / rel\n87\t elif rel:\n88\t card_path = wiki_root / rel\n89\t else:\n90\t continue\n91\t card = _read_json(card_path)\n92\t if card:\n93\t # 把 index 的派生字段(dominantLayer / hasDaoSibling / episodeTitle)合进 card,\n94\t # 避免后面再去算\n95\t card[\"_dominantLayer\"] = entry.get(\"dominantLayer\", \"\")\n96\t card[\"_hasDaoSibling\"] = entry.get(\"hasDaoSibling\", Fa\n[truncated]"}},{"type":"tool_use","id":"call_01_JLuzbEaFwcTvvHNMi0ju3105","name":"Read","input":{"file_path":"/home/forge/workspace/forgeworkspace/AI-Extraction-Engine/scripts/generate_dossier.py"}},{"type":"tool_result","tool_use_id":"call_01_JLuzbEaFwcTvvHNMi0ju3105","content":"1\t# scripts/generate_dossier.py\n2\t\"\"\"生成共创档案(spec docs/superpowers/specs/2026-06-20-co-creation-dossier-design.md)。\n3\t\n4\t把一个 session 的萃取结果渲染成给专家本人的多页 HTML 报告:\n5\t- 6 页:封面 / 价值仪表盘 / 判断模型图 / 知识卡精选 / 影响辐射 / 践行者身份\n6\t- 4 条 tagline(共创框架):让专家产生\"AI 是协同伙伴,不是萃取机器\"的认知\n7\t- 纯模板装配,无 LLM,确定性优先\n8\t\n9\tHC 落地:\n10\t- HC-1: businessGoal.objective 空 → 硬失败\n11\t- HC-4: quoteVerbatim 空时降级 businessGoal,不编造\n12\t- HC-5: inferredFields 在卡精选页显式 ⚠️ [推断] badge\n13\t- HC-7: _audit_no_raw_leak 防御性 grep,确保 raw/ 内容不入 dossier\n14\t- HC-8: 判断模型图显式呈现道/法/术/策/坑五层\n15\t\n16\t仅依赖标准库。\n17\t\"\"\"\n18\tfrom __future__ import annotations\n19\timport html\n20\timport json\n21\timport os\n22\timport re\n23\timport sys\n24\timport tempfile\n25\tfrom pathlib import Path\n26\t\n27\tDEFAULT_OUT_DIR = \"deliverables\"\n28\t\n29\t# 固定 tagline(spec §AI 态度宣言)\n30\tTAGLINE_1 = \"让我帮您,发现您的更多可能\" # 封面副标\n31\tTAGLINE_2 = \"您脑子里的判断,值得被更多人用到\" # 页 5 落点\n32\tTAGLINE_3 = \"不是拿走您的经验,是放大它、传远它\" # 页 2 副标\n33\tTAGLINE_4 = \"AI 不是来替代您,是让您的智慧走得更远\" # 页 6 副引导\n34\t\n35\t# 六层次颜色(spec §3 页 3 节点色)\n36\tLAYER_COLORS = {\n37\t \"Dao\": \"#3b82f6\", # 蓝\n38\t \"Fa\": \"#10b981\", # 绿\n39\t \"Shu\": \"#8b5cf6\", # 紫\n40\t \"Ce\": \"#ec4899\", # 粉\n41\t \"Keng\":\"#ef4444\", # 红\n42\t # Qi 故意不渲染(spec §3 页 3:器是执行支撑,非判断本身)\n43\t}\n44\t\n45\t# dossier number 提取正则:K2J_<Archetype>_<YYYY>_<MMDD>_<NNN>\n46\t_KID_PATTERN = re.compile(r\"^K2J_[A-Z]+_\\d{4}_\\d{4}_(\\d+)$\")\n47\t\n48\t\n49\tdef _read_json(path: Path) -> dict:\n50\t \"\"\"读 JSON 文件;不存在/损坏返回空 dict。\"\"\"\n51\t try:\n52\t return json.loads(Path(path).read_text(encoding=\"utf-8\"))\n53\t except (json.JSONDecodeError, OSError):\n54\t return {}\n55\t\n56\t\n57\tdef _compute_dossier_number(cards: list[dict]) -> str:\n58\t \"\"\"从该 session 卡中提取 dossier 编号(spec §4.3)。\n59\t\n60\t 规则:\n61\t 1. 取该 session 卡中 knowledgeId 字典序最大的那张\n62\t 2. 按 `_` 切分取最后一段,解析为 int\n63\t 3. zero-pad 成 3 位字符串\n64\t\n65\t 幂等:同输入同输出。\n66\t 失败:0 张卡 → ValueError;knowledgeId 不符合正则 → ValueError。\n67\t \"\"\"\n68\t if not cards:\n69\t raise ValueError(\"dossier number 计算至少需要 1 张卡\")\n70\t kids = [truncated]","is_error":false},{"type":"tool_raw_result","tool_use_id":"call_01_JLuzbEaFwcTvvHNMi0ju3105","tool_name":"Read","raw_content":{"type":"text","text":"1\t# scripts/generate_dossier.py\n2\t\"\"\"生成共创档案(spec docs/superpowers/specs/2026-06-20-co-creation-dossier-design.md)。\n3\t\n4\t把一个 session 的萃取结果渲染成给专家本人的多页 HTML 报告:\n5\t- 6 页:封面 / 价值仪表盘 / 判断模型图 / 知识卡精选 / 影响辐射 / 践行者身份\n6\t- 4 条 tagline(共创框架):让专家产生\"AI 是协同伙伴,不是萃取机器\"的认知\n7\t- 纯模板装配,无 LLM,确定性优先\n8\t\n9\tHC 落地:\n10\t- HC-1: businessGoal.objective 空 → 硬失败\n11\t- HC-4: quoteVerbatim 空时降级 businessGoal,不编造\n12\t- HC-5: inferredFields 在卡精选页显式 ⚠️ [推断] badge\n13\t- HC-7: _audit_no_raw_leak 防御性 grep,确保 raw/ 内容不入 dossier\n14\t- HC-8: 判断模型图显式呈现道/法/术/策/坑五层\n15\t\n16\t仅依赖标准库。\n17\t\"\"\"\n18\tfrom __future__ import annotations\n19\timport html\n20\timport json\n21\timport os\n22\timport re\n23\timport sys\n24\timport tempfile\n25\tfrom pathlib import Path\n26\t\n27\tDEFAULT_OUT_DIR = \"deliverables\"\n28\t\n29\t# 固定 tagline(spec §AI 态度宣言)\n30\tTAGLINE_1 = \"让我帮您,发现您的更多可能\" # 封面副标\n31\tTAGLINE_2 = \"您脑子里的判断,值得被更多人用到\" # 页 5 落点\n32\tTAGLINE_3 = \"不是拿走您的经验,是放大它、传远它\" # 页 2 副标\n33\tTAGLINE_4 = \"AI 不是来替代您,是让您的智慧走得更远\" # 页 6 副引导\n34\t\n35\t# 六层次颜色(spec §3 页 3 节点色)\n36\tLAYER_COLORS = {\n37\t \"Dao\": \"#3b82f6\", # 蓝\n38\t \"Fa\": \"#10b981\", # 绿\n39\t \"Shu\": \"#8b5cf6\", # 紫\n40\t \"Ce\": \"#ec4899\", # 粉\n41\t \"Keng\":\"#ef4444\", # 红\n42\t # Qi 故意不渲染(spec §3 页 3:器是执行支撑,非判断本身)\n43\t}\n44\t\n45\t# dossier number 提取正则:K2J_<Archetype>_<YYYY>_<MMDD>_<NNN>\n46\t_KID_PATTERN = re.compile(r\"^K2J_[A-Z]+_\\d{4}_\\d{4}_(\\d+)$\")\n47\t\n48\t\n49\tdef _read_json(path: Path) -> dict:\n50\t \"\"\"读 JSON 文件;不存在/损坏返回空 dict。\"\"\"\n51\t try:\n52\t return json.loads(Path(path).read_text(encoding=\"utf-8\"))\n53\t except (json.JSONDecodeError, OSError):\n54\t return {}\n55\t\n56\t\n57\tdef _compute_dossier_number(cards: list[dict]) -> str:\n58\t \"\"\"从该 session 卡中提取 dossier 编号(spec §4.3)。\n59\t\n60\t 规则:\n61\t 1. 取该 session 卡中 knowledgeId 字典序最大的那张\n62\t 2. 按 `_` 切分取最后一段,解析为 int\n63\t 3. zero-pad 成 3 位字符串\n64\t\n65\t 幂等:同输入同输出。\n66\t 失败:0 张卡 → ValueError;knowledgeId 不符合正则 → ValueError。\n67\t \"\"\"\n68\t if not cards:\n69\t raise ValueError(\"dossier number 计算至少需要 1 张卡\")\n70\t kids = [c.get(\"knowledgeId\", \"\") for c in cards]\n71\t if not all(kids):\n72\t raise ValueError(f\"发现空 knowledgeId:{kids}\")\n73\t max_kid = max(kids)\n74\t m = _KID_PATTERN.match(max_kid)\n75\t if not m:\n76\t raise ValueError(f\"knowledgeId 不符合 K2J_<Arch>_<YYYY>_<MMDD>_<NNN> 格式:{max_kid}\")\n77\t return m.group(1).zfill(3)\n78\t\n79\t\n80\tdef _select_inscription(cards: list[dict], business_goal_objective: str) -> tuple[str, str]:\n81\t \"\"\"选 inscription:优先 quoteVerbatim,fallback businessGoal.objective。\n82\t\n83\t 返回 (text, source) where source ∈ {\"verbatim\", \"objective\"}。\n84\t quoteVerbatim 为空串或纯空白 → fallback。\n85\t \"\"\"\n86\t for card in cards:\n87\t quote = card.get(\"provenance\", {}).get(\"k2j:quoteVerbatim\", \"\")\n88\t if quote and quote.strip():\n89\t return quote.strip(), \"verbatim\"\n90\t return (business_goal_objective or \"\").strip(), \"objective\"\n91\t\n92\t\n93\tdef _load_dossier_cards(sid: str, wiki_root: Path) -> list[dict]:\n94\t \"\"\"从 wiki/index.json 过滤 sid \n[truncated]"}},{"type":"thinking","text":"The user asked me to explain in detail how the cuiqu-export skill is organized. I've now read a lot of the code. Let me synthesize everything I've learned to give a comprehensive answer about the skill's organization.\n\nFrom my readings:\n\n1. **cuiqu-export/SKILL.md** - The main skill definition (already read earlier)\n2. **docs/templates/handbook-template.html** - The HTML template for the handbook\n3. **scripts/generate_deliverable.py** - The main Python script that renders deliverables\n4. **scripts/generate_dossier.py** - The Python script that renders the co-creation dossier\n5. **deliverables/** directory shows actual output files\n\nLet me now synthesize everything into a clear explanation for the user."},{"type":"text","text":"好，我已经读完了所有相关代码。下面给你详细拆解 cuiqu-export 的整体组织方式。\n\n---\n\n## cuiqu-export 的整体架构\n\n这个 skill 本身**不写任何推理逻辑**，它的工作是编排两个 Python 脚本去渲染文件。整体分成两条线：\n\n```\ncuiqu-export <session-id>\n │\n ├──→ generate_deliverable.py → 产出前 3 件套（确定性渲染）\n │ │ summary.md / full.md / handbook.html\n │ │ + summary.html / full.html（HTML 版）\n │ └── 读取: meta.json + interview_state.json + wiki/index.json + 各卡片\n │\n └──→ generate_dossier.py → 产出第 4 件套（best-effort）\n co-creation-dossier.html（6 页）\n \n 读取: meta.json + wiki/index.json + 各卡片\n + stage1-slices.json + stage2-dag.json（编译能耗数据）\n```\n\n---\n\n## 一、3 件套：generate_deliverable.py（~3200 行）\n\n### 数据源\n\n| 读取位置 | 用途 |\n|----------|------|\n| `raw/[sid]/meta.json` | session 元数据（专家信息、业务目标、checklist 覆盖） |\n| `raw/[sid]/interview_state.json` | 访谈状态（轮次、金句池） |\n| `raw/[sid]/transcript.jsonl` | **full.html 增强版**才用，用于叙事化包装 |\n| `wiki/index.json` | 按 `expert == sessionId` 过滤出该 session 的卡片 |\n| `wiki/[type]/[id].jsonld` | 每张卡片的完整六层次内容 |\n| `.llmwiki/in-progress/[sid]/stage2-dag.json` | **full.html 增强版**才用，DAG 节点数据 |\n| `templates/[scenario]/[archetype]/` | 可选模板包（keyword_pools + golden_quotes） |\n\n### 输出文件\n\n```\ndeliverables/[sid]/\n├── summary.md —— 一页纸汇总（Markdown，≤300 字）\n├── summary.html —— 同上，HTML 可视化版\n├── full.md —— 完整文档（Markdown）\n├── full.html —— 完整文档（HTML 版，v2 增强为\"案例汇报版\"）\n└── handbook.html —— 新人手册（HTML，含培训素材渲染）\n```\n\n### 4 种渲染模式\n\n**1. summary.html（一页纸汇总）**\n\n分 3 个区：\n- **访谈覆盖度仪表** — 4 个 stat-card（checklist 覆盖 N/7、turn 数、金句数、episode 数）\n- **核心洞察卡片墙** — 每张卡一个 insight-card，显示主导层标签 + 核心原话 + 推断标记\n- **后续指引** — 提示推断卡数量、待 HR 校核数量\n\n**2. full.html（完整文档 → v2 升级为\"案例汇报版\"）**\n\n按 Episode 分组渲染，每个 Episode 包含：\n- **六层次判断模型** — 道/法/术/策/器/坑，每层一个彩色 callout\n- **失败边界** — 适用/不适用/风险三字段表格\n- **专家原话锚点** — 引用卡片 quoteVerbatim\n- **补槽提示** — 缺失 layer + inferred 字段的警告\n\nv2 增强版（从 stage2-dag.json + transcript 额外读取）还包含：\n- **案列摘要** — 1.1 主题概述、1.2 个人简介、1.3 成交结果（7 字段）\n- **成交历程时间线** — 按 transcript 事件顺序抽关键节点\n- **客户认知 4 维** — 客户特征/需求/决策链/关键人\n- **工具话术集锦** — 从 transcript 抽带引号的话术，按场景分组\n\n**3. handbook.html（新人手册）**\n\n基于 `docs/templates/handbook-template.html` 模板渲染，含 7 个章节：\n- 业务背景与目标\n- 五维分析（人/事/财/货/场）\n- 核心判断模型（六层次，每层有流程图 + callout）\n- 关键判断故事（Episode）\n- 原话锚点\n- 适用边界与风险\n- 附录\n\n如果卡片挂了 `trainingMaterial` 节点，handbook 会优先用它渲染教学模块（大白话/三板斧/话术对照等）；否则降级到 sixLayers 文本。\n\n---\n\n## 二、4 件套：generate_dossier.py（~2400 行）\n\n### 数据源\n\n除了 deliverable 的数据源外，额外读：\n- `.llmwiki/in-progress/[sid]/stage1-slices.json` — 切片统计\n- `.llmwiki/in-progress/[sid]/stage2-dag.json` — DAG 节点统计\n\n### 输出\n\n```\ndeliverables/[sid]/co-creation-dossier.html\n```\n\n### 6 页结构\n\n| 页码 | 名称 | 视觉 | 内容 |\n|------|------|------|------|\n| 1 | **封面** | 深色渐变 + 金色细节 | 专家名主视觉 + tagline \"让我帮您,发现您的更多可能\" + inscription 金句压底 |\n| 2 | **价值仪表盘** | 白色 + 彩虹渐变色 | 编译能耗面板（切片→DAG→卡片 流水线）+ 4 资产卡（经验/判断/原话/训练）+ Curiosity Gap 邀请 + score 状态条 |\n| 3 | **判断模型图** | SVG 五色辐射图 | 主导 Episode 的 5 节点 SVG（道/法/术/策/坑 × 五色）+ 其他 episode 列表 |\n| 4 | **知识卡精选** | 卡片 + 六层次分色 | 每个 episode 一张代表卡 + 六层次内容 + JSON-LD 折叠视图 |\n| 5 | **影响辐射** | 双段式 | 上段·利己（4 条 AI 自白：\"AI 没有您的 XXX 就无法共同达成 YYY\"）+ 下段·利他（4 条辐射下游场景） |\n| 6 | **践行者身份** | 深色渐变 + SVG 三角 | tagline 4 + \"AI 不是来替代您\" + 三方协同图（您/AI/他人）+ \"人机协同·践行者\" |\n\n### 关键特色\n\n- **4 条 tagline** 贯穿全文（封面副标、利己利他过渡、落点收束）\n- **Compile Effort 面板** 展示从切片到卡片的编译能耗（真实数据 from stage1/stage2）\n- **Curiosity Gap 邀请** — 4 级 fallback 推导还有哪些没聊透的\n- **Agent Simulator** — 预录 3 个客户咨询场景，展示\"您的分身\"怎么回答问题\n- **HC-7 自审** — `_audit_no_raw_leak()` 防御性检查 HTML 不含 raw/ 路径或 PII\n\n---\n\n## 三、核心设计原则\n\n| 原则 | 含义 | 代码体现 |\n|------|------|----------|\n| **确定性渲染** | 无 LLM 调用，同一输入永远同一输出 | 纯 Python 字符串拼接 + 字典映射 |\n| **inferred 标红** | 推断字段必须显式标记 | `⚠️ [推断]` badge + 🚧 待校核 |\n| **缺失不掩饰** | 空 layer 写\"访谈未提及\"，不编造 | `_layer_value()` 返回 `\"(访谈未提及)\"` |\n| **best-effort dossier** | 共创档案失败不阻塞其他 3 件套 | `generate_dossier.py` 单独一个 try |\n| **单文件可分享** | HTML 内联 CSS，无外部依赖 | `_css()` 函数把全部样式嵌入 `<style>` |\n\n---\n\n## 四、数据流总结\n\n```\nmeta.json + state.json + index.json + 各卡 JSON-LD\n │\n ▼\n generate_deliverable.py ──→ summary.html/md（一页纸）\n │ full.html/md（完整文档）\n │ handbook.html（新人手册）\n │\n generate_dossier.py ──→ co-creation-dossier.html（共创档案）\n \n（可选）templates/[scenario]/[archetype]/ → keyword pools + golden quotes\n ↑ 用于 full.html 增强版的\"案例汇报\"\n 仅当模板包存在时加载\n```\n\n有没有哪个具体部分你还想深入了解？比如某个 HTML 页面的详细渲染逻辑？"}]... |