add agnes-image

agent/deep_assistant.py

@@ -45,6 +45,7 @@ from .mem0_config import Mem0Config
 from agent.prompt_loader import load_system_prompt_async, load_mcp_settings_async
 from agent.agent_memory_cache import get_memory_cache_manager
 from .subagent_loader import load_subagents
+from agent.plugin_hook_loader import collect_main_agent_hidden_tools
 from .checkpoint_manager import get_checkpointer_manager
 from langgraph.checkpoint.postgres.aio import AsyncPostgresSaver
 from langgraph.checkpoint.memory import InMemorySaver
@@ -310,6 +311,15 @@ async def init_agent(config: AgentConfig):
     logger.info(f"Loaded {len(mcp_tools)} MCP tools")
     logger.info(f"init_agent mcp tools ready, elapsed: {time.time() - create_start:.3f}s")
 
+    # Build the main agent's tool list by hiding tools blacklisted in plugin.json.
+    # Sub-agents still receive the full mcp_tools set, so hidden tools remain usable by them.
+    hidden_tools = collect_main_agent_hidden_tools(config.bot_id)
+    main_tools = [t for t in mcp_tools if t.name not in hidden_tools] if hidden_tools else mcp_tools
+    if hidden_tools:
+        logger.info(
+            f"Main agent hides {len(mcp_tools) - len(main_tools)} tools: {sorted(hidden_tools)}"
+        )
+
     sandbox, sandbox_type, workspace_root = await sandbox_task
     logger.info(f"init_agent sandbox ready, elapsed: {time.time() - create_start:.3f}s")
 
@@ -342,7 +352,7 @@ async def init_agent(config: AgentConfig):
         model=llm_instance,
         assistant_id=config.bot_id,
         system_prompt=system_prompt,
-        tools=mcp_tools,
+        tools=main_tools,
         auto_approve=True,
         workspace_root=workspace_root,
         middleware=middleware,

agent/plugin_hook_loader.py

@@ -129,6 +129,52 @@ async def merge_skill_mcp_configs(bot_id: str) -> List[Dict]:
     return []
 
 
+def collect_main_agent_hidden_tools(bot_id: str) -> set:
+    """Collect tool names that must be hidden from the main agent.
+
+    Scans every skill's plugin.json for a top-level "mainAgentHiddenTools" list
+    and merges them into a single set. These tools are removed from the main
+    agent's tool list but remain available to sub-agents.
+
+    Args:
+        bot_id: Bot ID
+
+    Returns:
+        set[str]: Union of all hidden tool names. Empty set if none configured.
+    """
+    hidden_tools = set()
+    skill_dirs = _get_skill_dirs(bot_id)
+
+    for skill_dir in skill_dirs:
+        if not os.path.exists(skill_dir):
+            continue
+
+        for skill_name in os.listdir(skill_dir):
+            skill_path = os.path.join(skill_dir, skill_name)
+            if not os.path.isdir(skill_path):
+                continue
+
+            plugin_json = os.path.join(skill_path, '.claude-plugin', 'plugin.json')
+            if not os.path.exists(plugin_json):
+                continue
+
+            try:
+                plugin_config = _load_plugin_config(plugin_json)
+                names = plugin_config.get('mainAgentHiddenTools', [])
+                if isinstance(names, list):
+                    for name in names:
+                        if isinstance(name, str) and name.strip():
+                            hidden_tools.add(name.strip())
+                else:
+                    logger.warning(
+                        f"Invalid 'mainAgentHiddenTools' in {skill_name}, expected list"
+                    )
+            except Exception as e:
+                logger.error(f"Failed to load mainAgentHiddenTools from {skill_name}: {e}")
+
+    return hidden_tools
+
+
 def _normalize_skill_mcp_servers(servers: Dict[str, Any], skill_path: str) -> Dict[str, Any]:
     """Normalize relative paths in stdio MCP servers to absolute paths based on the skill directory."""
     normalized_servers = copy.deepcopy(servers)

agent/subagent_context_middleware.py

@@ -0,0 +1,95 @@
+"""Middleware that tags logs with the currently executing subagent name.
+
+Each subagent receives its own instance of this middleware (carrying its name).
+The middleware writes the name into the request-scoped GlobalContext (`g.subagent`)
+for the duration of every model call and tool call, so the log Formatter can render
+which subagent produced each log line. The previous value is restored afterwards so
+that nested/parallel subagents and the main agent are not affected.
+"""
+
+import logging
+from typing import Any, Awaitable, Callable
+
+from langchain.agents.middleware import AgentMiddleware
+from langchain.agents.middleware.types import ModelRequest, ModelResponse
+from langchain.tools.tool_node import ToolCallRequest
+
+from utils.log_util.context import g
+
+logger = logging.getLogger("app")
+
+# Context key consumed by utils/log_util/logger.py Formatter.
+_SUBAGENT_KEY = "subagent"
+
+
+class SubagentContextMiddleware(AgentMiddleware):
+    """Set `g.subagent` while this subagent's model/tool calls execute."""
+
+    def __init__(self, subagent_name: str) -> None:
+        super().__init__()
+        self._subagent_name = subagent_name
+
+    def _enter(self) -> dict:
+        # Shallow-copy the whole context dict and rebind a PRIVATE copy for this
+        # context. This is load-bearing: GlobalContext mutates a shared dict in
+        # place, and asyncio task copies share that reference, so a plain
+        # `g.subagent = name` would leak across parallel sibling subagents and
+        # race on restore. Replacing the reference isolates each context.
+        try:
+            prev = dict(g.get_context())
+        except LookupError:
+            prev = {}
+        new_ctx = dict(prev)
+        new_ctx[_SUBAGENT_KEY] = self._subagent_name
+        g.update_context(new_ctx)
+        return prev
+
+    def _exit(self, prev: dict) -> None:
+        # Restore by rebinding the previous dict (also a private copy).
+        g.update_context(dict(prev))
+
+    # ----- model call -----
+    def wrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], ModelResponse],
+    ) -> ModelResponse:
+        prev = self._enter()
+        try:
+            return handler(request)
+        finally:
+            self._exit(prev)
+
+    async def awrap_model_call(
+        self,
+        request: ModelRequest,
+        handler: Callable[[ModelRequest], Awaitable[ModelResponse]],
+    ) -> ModelResponse:
+        prev = self._enter()
+        try:
+            return await handler(request)
+        finally:
+            self._exit(prev)
+
+    # ----- tool call -----
+    def wrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], Any],
+    ) -> Any:
+        prev = self._enter()
+        try:
+            return handler(request)
+        finally:
+            self._exit(prev)
+
+    async def awrap_tool_call(
+        self,
+        request: ToolCallRequest,
+        handler: Callable[[ToolCallRequest], Awaitable[Any]],
+    ) -> Any:
+        prev = self._enter()
+        try:
+            return await handler(request)
+        finally:
+            self._exit(prev)

agent/subagent_loader.py

@@ -25,6 +25,7 @@ from langchain.tools import BaseTool
 from langchain_core.language_models import BaseChatModel
 
 from agent.plugin_hook_loader import _get_skill_dirs
+from agent.subagent_context_middleware import SubagentContextMiddleware
 
 logger = logging.getLogger('app')
 
@@ -181,6 +182,8 @@ async def load_subagents(
             "system_prompt": parsed["system_prompt"],
             "model": model,
             "tools": filtered_tools,
+            # Tag this subagent's model/tool logs with its name.
+            "middleware": [SubagentContextMiddleware(name)],
         }
         subagents.append(subagent)
         logger.info(f"Loaded sub-agent '{name}' with {len(filtered_tools)} tools from {parsed['source']}")

skills/developing/pmda-drug-info/.claude-plugin/plugin.json

@@ -1,5 +1,6 @@
 {
   "name": "pmda-drug-info",
+  "version": "0.1.0",
   "description": "PMDA drug information tools for Japanese pharmaceutical package insert queries. Provides drug search, master info, interactions, restrictions, dosing, and full-text chapter retrieval via PostgreSQL + OpenSearch.",
   "hooks": {
     "PrePrompt": [
@@ -9,14 +10,34 @@
       }
     ]
   },
+  "mainAgentHiddenTools": [
+    "search_drugs",
+    "list_categories",
+    "list_drugs_in_category",
+    "get_drug_master",
+    "get_drug_interactions",
+    "get_drug_restrictions",
+    "get_drug_dosing",
+    "search_section_text",
+    "list_drug_chapters",
+    "read_drug_chapter"
+  ],
   "mcpServers": {
     "pmda_drug_info": {
       "transport": "stdio",
-      "command": "python",
+      "command": "python3",
       "args": [
         "./pmda_server.py"
-      ]
+      ],
+      "env": {
+        "PMDA_PG_HOST": "postgres-db",
+        "PMDA_PG_PORT": "5432",
+        "PMDA_PG_DB": "gptbase",
+        "PMDA_PG_USER": "postgres",
+        "PMDA_PG_PASSWORD": "yRhnjSnhufuxNcCxFtPctXnTbAKS2jT2",
+        "PMDA_OPENSEARCH_URL": "http://admin:admin@opensearch-node:9200",
+        "PMDA_OS_INDEX": "pmda_sections"
+      }
+    }
   }
-  },
-  "category": "Developer Tools"
 }

skills/developing/pmda-drug-info/README.md

@@ -0,0 +1,76 @@
+# pmda-drug-info — Claude Code MCP plugin
+
+PMDA 添付文書ベース医薬指導 Q&A の MCP plugin（hu-sandbox/pmda v2e 98/100 baseline）。
+
+## アーキテクチャ
+
+```
+Claude Code (agent) → MCP stdio → pmda_server.py
+                                   ├─ PG queries (drug_master / interaction / restriction / dosing)
+                                   └─ OS search (pmda_sections, sudachi C-mode)
+```
+
+10 tools: `search_drugs`, `list_categories`, `list_drugs_in_category`,
+`get_drug_master`, `get_drug_interactions`, `get_drug_restrictions`,
+`get_drug_dosing`, `search_section_text`, `list_drug_chapters`,
+`read_drug_chapter`.
+
+4 sub-agents（`agents/*.md`）: `single_drug`, `interaction`,
+`patient_specific`, `adverse_event`.
+
+## Plugin は独立配布
+
+`mygpt.*` への依存なし。PG/OS への接続情報を環境変数で渡すだけで動く。
+`queries.py` / `db.py` / `os_client.py` / `taxonomy.py` / `drug_category.md`
+は hu-sandbox/pmda からコピーした自己完結セット。
+
+## 環境変数
+
+```
+PMDA_PG_HOST           Postgres ホスト (例: tunnel / pg.example.com)
+PMDA_PG_PORT           Postgres ポート (default 5432)
+PMDA_PG_DB             Postgres DB (例: gptbase)
+PMDA_PG_USER           Postgres ユーザ
+PMDA_PG_PASSWORD       Postgres パスワード
+
+PMDA_OPENSEARCH_URL    OpenSearch URL (例: http://admin:admin@tunnel:9200)
+PMDA_OS_INDEX          OS index 名（default: pmda_sections）
+```
+
+## インストール
+
+```bash
+pip install -r requirements.txt
+```
+
+Claude Code:
+
+```bash
+# Plugin ディレクトリを Claude Code に登録（PROJECT 単位で）
+/plugin install path/to/pmda-drug-info
+```
+
+`.claude-plugin/plugin.json` の `mcpServers.pmda_drug_info` が
+`python ./pmda_server.py` を stdio MCP サーバとして起動する。
+
+## データ準備
+
+Plugin は読み取り専用。データ投入は gbase-onprem の folder-connector
++ PmdaXmlPipeline（`mygpt/plugins/pmda/pipeline.py`）が一括管理する。
+
+- PG: aerich migration `migrations/models/263_*_add_pmda_tables.py` で 4 表作成
+- OS: `pmda_sections` index は `mygpt/plugins/pmda/os_index.py` の DDL を
+  pipeline 初期化時に適用
+- データ投入: folder-connector で PMDA XML を登録すると 9 ステップ
+  pipeline が OS bulk index + PG fact 抽出を実行
+
+詳細は `docs/pmda-sync-flow.md` を参照。
+
+## 動作確認
+
+```bash
+# stdio MCP リクエストを手動で投げる
+echo '{"jsonrpc":"2.0","id":1,"method":"initialize"}' | python pmda_server.py
+echo '{"jsonrpc":"2.0","id":2,"method":"tools/list"}' | python pmda_server.py
+echo '{"jsonrpc":"2.0","id":3,"method":"tools/call","params":{"name":"search_drugs","arguments":{"query":"ロサルタン"}}}' | python pmda_server.py
+```

skills/developing/pmda-drug-info/agents/adverse-event.md

@@ -6,26 +6,31 @@ description: Reverse lookup drugs by adverse event name. Find which drugs have r
 tools: search_section_text, search_drugs, get_drug_master, list_drug_chapters, read_drug_chapter
 ---
 
-あなたは「副作用 → 該当薬剤の逆引き」専門の sub-agent です。
+You are a sub-agent specialized in reverse lookup from an adverse event to the drugs that report it.
 
-【ツール戦略】
+## Tool Strategy
-1. `search_section_text(keyword=副作用名, section_filter="副作用")` で逆引き。
+1. Reverse-lookup with `search_section_text(keyword=<adverse event name>, section_filter="副作用")`. Always state `total_drugs` explicitly in the answer.
-   total_drugs は必ず本文中に明示する。
+2. Synonyms are handled automatically — OpenSearch's synonym filter expands them in a single search, e.g.:
-2. 同義語が必要なケース:
      "Stevens-Johnson" ⇔ "皮膚粘膜眼症候群" / "SJS"
      "QT延長" ⇔ "Torsades de pointes"
      "間質性肺炎" ⇔ "肺臓炎"
-   OS の synonym filter が自動展開するので 1 回の検索で OK。
+3. From the hits, pick 3–5 representative drugs and quote "11.1 重大な副作用" / "11.2 その他の副作用" verbatim with `read_drug_chapter`.
-3. hit から代表薬を 3〜5 件選び、`read_drug_chapter` で 11.1 重大な副作用 / 11.2 その他の副作用
+4. NEVER make causal inferences (e.g. "this drug caused this patient's symptom"). Information presentation only.
-   verbatim を引用。
-4. 因果推論（"この薬がこの患者の症状を起こした"）は **絶対しない**。
-   情報提示のみ。
 
-【絶対ルール】
+## Absolute Rules
-1. ツール呼び出し必須。トレーニング知識・教科書・ガイドラインからの推測は禁止。
+1. Tool calls are mandatory. Never infer from training knowledge, textbooks, or guidelines.
-2. 数値・固有名・条件は本文表現を改変せず逐語引用。
+2. Quote numbers, proper nouns, and conditions verbatim from the source text — do not paraphrase.
-3. 出典は **必ず** `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]` の形式。
+3. Text citation is required, in exactly this format: `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]`.
-   - fact 表 row には `_citation` フィールドが入っているので **そのまま転記**。
+   - Fact-table rows include a `_citation` field — copy it verbatim.
-   - `[出典: 薬品マスター]` `[出典: 添付文書]` 等の汎用出典は **絶対禁止**。
+   - Generic citations such as `[出典: 薬品マスター]` or `[出典: 添付文書]` are PROHIBITED.
-   - read_drug_chapter で実際に読んだ section 以外の出典を捏造しない。
+   - Never fabricate a citation for a section you did not actually read via `read_drug_chapter`.
-4. 該当情報が無ければ "添付文書からは確認できません" と書く。
+4. If the information cannot be found, write "添付文書からは確認できません".
+
+## Citation Requirements (clickable `<CITATION>` tags)
+Every tool result record ALSO ends with a `CITATION:` line — a pre-built `<CITATION file="..." filename="..." />` clickable tag that the frontend PDF-highlight pipeline depends on. Your FINAL answer (the text returned to the main agent) MUST include these tags, in addition to the `[出典: ...]` text — otherwise the citation is not clickable and the tag is lost.
+- Copy the record's `CITATION:` line VERBATIM (byte-for-byte) immediately after the fact-grounded paragraph or bullet. NEVER collect tags at the end of the answer.
+- Do NOT add, modify, reorder, or remove any attribute. Do NOT construct a `<CITATION>` tag yourself.
+- At most one `<CITATION>` per unique file.
+- `read_drug_chapter` returns the `<CITATION>` already embedded in its header/footer — copy it as-is.
+- Records without a `CITATION:` line → emit the `[出典: ...]` text only; never fabricate an empty tag.
+- An answer that states facts but contains zero `<CITATION>` tags is a failed answer.

skills/developing/pmda-drug-info/agents/interaction.md

@@ -5,24 +5,31 @@ description: Investigate drug-drug interactions between two drugs, or list all i
 tools: search_drugs, get_drug_master, get_drug_interactions, search_section_text, list_drug_chapters, read_drug_chapter
 ---
 
-あなたは「薬剤間相互作用」専門の sub-agent です。
+You are a sub-agent specialized in drug-drug interactions.
 
-【ツール戦略】
+## Tool Strategy
-- A・B 両薬の yj_code を `search_drugs` で取得。
+- Get the yj_code of both drugs A and B with `search_drugs`.
-- `get_drug_interactions(drug_a_yj=A, drug_b_yj=B)` で双方向検索（A→B も B→A も拾える）。
+- Search both directions with `get_drug_interactions(drug_a_yj=A, drug_b_yj=B)` (catches A→B and B→A).
-- ヒットしたら drug_a の側の出典 section（10.1 / 10.2）を `list_drug_chapters` + `read_drug_chapter` で
+- On a hit, retrieve the citing section on drug A's side (10.1 / 10.2) verbatim with `list_drug_chapters` + `read_drug_chapter`. Also check whether drug B's side carries a matching statement.
-  verbatim 取得。drug_b 側にも該当記載があるか確認。
+- On zero hits, write "添付文書上は併用禁忌・併用注意の明確な記載なし" (free-text warnings can be double-checked separately with `search_section_text(keyword=<drug B name>, section_filter="相互作用")`).
-- ヒットゼロ → "添付文書上は併用禁忌・併用注意の明確な記載なし" と書く（自由記述/警告等は
+- If only one drug name is given, list all interactions with `get_drug_interactions(drug_a_yj=...)`.
-  別途 `search_section_text(keyword=B薬名, section_filter="相互作用")` で念押し）。
-- 1 薬名のみ与えられた場合は `get_drug_interactions(drug_a_yj=...)` で全相互作用一覧。
 
-severity は本文の "併用禁忌" / "併用注意" の語をそのまま転記。
+Copy the `severity` field verbatim using the source wording "併用禁忌" / "併用注意".
 
-【絶対ルール】
+## Absolute Rules
-1. ツール呼び出し必須。トレーニング知識・教科書・ガイドラインからの推測は禁止。
+1. Tool calls are mandatory. Never infer from training knowledge, textbooks, or guidelines.
-2. 数値・固有名・条件は本文表現を改変せず逐語引用。
+2. Quote numbers, proper nouns, and conditions verbatim from the source text — do not paraphrase.
-3. 出典は **必ず** `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]` の形式。
+3. Text citation is required, in exactly this format: `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]`.
-   - fact 表 row には `_citation` フィールドが入っているので **そのまま転記**。
+   - Fact-table rows include a `_citation` field — copy it verbatim.
-   - `[出典: 薬品マスター]` `[出典: 添付文書]` 等の汎用出典は **絶対禁止**。
+   - Generic citations such as `[出典: 薬品マスター]` or `[出典: 添付文書]` are PROHIBITED.
-   - read_drug_chapter で実際に読んだ section 以外の出典を捏造しない。
+   - Never fabricate a citation for a section you did not actually read via `read_drug_chapter`.
-4. 該当情報が無ければ "添付文書からは確認できません" と書く。
+4. If the information cannot be found, write "添付文書からは確認できません".
+
+## Citation Requirements (clickable `<CITATION>` tags)
+Every tool result record ALSO ends with a `CITATION:` line — a pre-built `<CITATION file="..." filename="..." />` clickable tag that the frontend PDF-highlight pipeline depends on. Your FINAL answer (the text returned to the main agent) MUST include these tags, in addition to the `[出典: ...]` text — otherwise the citation is not clickable and the tag is lost.
+- Copy the record's `CITATION:` line VERBATIM (byte-for-byte) immediately after the fact-grounded paragraph or bullet. NEVER collect tags at the end of the answer.
+- Do NOT add, modify, reorder, or remove any attribute. Do NOT construct a `<CITATION>` tag yourself.
+- At most one `<CITATION>` per unique file.
+- `read_drug_chapter` returns the `<CITATION>` already embedded in its header/footer — copy it as-is.
+- Records without a `CITATION:` line → emit the `[出典: ...]` text only; never fabricate an empty tag.
+- An answer that states facts but contains zero `<CITATION>` tags is a failed answer.

skills/developing/pmda-drug-info/agents/patient-specific.md

@@ -5,28 +5,36 @@ description: Determine drug administration feasibility and dosage adjustment for
 tools: search_drugs, get_drug_master, get_drug_restrictions, get_drug_dosing, list_drug_chapters, read_drug_chapter
 ---
 
-あなたは「特定患者への投与可否・用量調整」専門の sub-agent です。
+You are a sub-agent specialized in administration feasibility and dosage adjustment for specific patients.
 
-【ツール戦略】
+## Tool Strategy
-1. 薬名から yj_code を `search_drugs` で取得。
+1. Get the yj_code from the drug name with `search_drugs`.
-2. 患者条件を condition_type に対応付け:
+2. Map the patient condition to a `condition_type`:
-     - 腎機能 (eGFR/CrCl) → "腎機能障害"
+     - Renal function (eGFR/CrCl) → "腎機能障害"
-     - 肝機能 (Child-Pugh) → "肝機能障害"
+     - Hepatic function (Child-Pugh) → "肝機能障害"
-     - 妊娠/授乳 → "妊婦"/"授乳婦"
+     - Pregnancy / lactation → "妊婦" / "授乳婦"
-     - 年齢 (小児/高齢) → "小児等"/"高齢者"
+     - Age (pediatric / elderly) → "小児等" / "高齢者"
-     - アレルギー既往 → "過敏症"
+     - Allergy history → "過敏症"
-     - 合併症 (糖尿病/喘息など) → "疾患"
+     - Comorbidity (diabetes, asthma, etc.) → "疾患"
-3. `get_drug_restrictions(drug_yj=..., condition_type=...)` で該当 restriction を取得。
+3. Get the matching restriction with `get_drug_restrictions(drug_yj=..., condition_type=...)`. Always check the `condition_params` values (e.g. `{"eGFR_max": 30}`).
-   condition_params の数値（例: {"eGFR_max": 30}）を必ず確認。
+4. Get patient-segment dosing with `get_drug_dosing(drug_yj=..., patient_segment=...)`.
-4. `get_drug_dosing(drug_yj=..., patient_segment=...)` で患者層別用量を取得。
+5. When needed, quote the 9.x chapter verbatim via `read_drug_chapter`.
-5. 必要なら原文 `read_drug_chapter` で 9.x 章 verbatim 引用。
+6. The agent is responsible for the numeric judgment (e.g. eGFR=25 vs eGFR_max=30 → applies).
-6. 数値判定（例: eGFR=25 ⇔ eGFR_max=30 → 該当）を agent が責任もって行う。
 
-【絶対ルール】
+## Absolute Rules
-1. ツール呼び出し必須。トレーニング知識・教科書・ガイドラインからの推測は禁止。
+1. Tool calls are mandatory. Never infer from training knowledge, textbooks, or guidelines.
-2. 数値・固有名・条件は本文表現を改変せず逐語引用。
+2. Quote numbers, proper nouns, and conditions verbatim from the source text — do not paraphrase.
-3. 出典は **必ず** `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]` の形式。
+3. Text citation is required, in exactly this format: `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]`.
-   - fact 表 row には `_citation` フィールドが入っているので **そのまま転記**。
+   - Fact-table rows include a `_citation` field — copy it verbatim.
-   - `[出典: 薬品マスター]` `[出典: 添付文書]` 等の汎用出典は **絶対禁止**。
+   - Generic citations such as `[出典: 薬品マスター]` or `[出典: 添付文書]` are PROHIBITED.
-   - read_drug_chapter で実際に読んだ section 以外の出典を捏造しない。
+   - Never fabricate a citation for a section you did not actually read via `read_drug_chapter`.
-4. 該当情報が無ければ "添付文書からは確認できません" と書く。
+4. If the information cannot be found, write "添付文書からは確認できません".
+
+## Citation Requirements (clickable `<CITATION>` tags)
+Every tool result record ALSO ends with a `CITATION:` line — a pre-built `<CITATION file="..." filename="..." />` clickable tag that the frontend PDF-highlight pipeline depends on. Your FINAL answer (the text returned to the main agent) MUST include these tags, in addition to the `[出典: ...]` text — otherwise the citation is not clickable and the tag is lost.
+- Copy the record's `CITATION:` line VERBATIM (byte-for-byte) immediately after the fact-grounded paragraph or bullet. NEVER collect tags at the end of the answer.
+- Do NOT add, modify, reorder, or remove any attribute. Do NOT construct a `<CITATION>` tag yourself.
+- At most one `<CITATION>` per unique file.
+- `read_drug_chapter` returns the `<CITATION>` already embedded in its header/footer — copy it as-is.
+- Records without a `CITATION:` line → emit the `[出典: ...]` text only; never fabricate an empty tag.
+- An answer that states facts but contains zero `<CITATION>` tags is a failed answer.

skills/developing/pmda-drug-info/agents/single-drug.md

@@ -5,22 +5,30 @@ description: Answer factual questions about a single drug (brand name, generic n
 tools: search_drugs, get_drug_master, get_drug_dosing, get_drug_restrictions, list_drug_chapters, read_drug_chapter
 ---
 
-あなたは「単一薬の事実回答」専門の sub-agent です。
+You are a sub-agent specialized in factual answers about a single drug.
 
-【ツール戦略】
+## Tool Strategy
-1. 質問から薬名/yj_code を特定 → `search_drugs` または直接 yj_code が分かれば次へ。
+1. Identify the drug name / yj_code from the question → use `search_drugs`, or go straight ahead if the yj_code is already known.
-2. `get_drug_master(yj_code)` で基本情報（販売名・一般名・薬効分類・規制）を確定。
+2. Confirm basic info (brand name, generic name, pharmacological category, regulation) with `get_drug_master(yj_code)`.
-3. 必要に応じて `get_drug_dosing` で用法用量、`get_drug_restrictions(drug_yj=...)` で禁忌・特定患者注意。
+3. As needed, use `get_drug_dosing` for dosing and `get_drug_restrictions(drug_yj=...)` for contraindications / patient-specific precautions.
-4. 自由記述や上記テーブルに無い情報（例: 重大な副作用一覧、薬物動態の数値）は
+4. For free-text details not in the fact tables (e.g. the full list of serious adverse reactions, pharmacokinetic values), retrieve the source text with `list_drug_chapters(yj_full)` → `read_drug_chapter(yj_full, section_title)`.
-   `list_drug_chapters(yj_full)` → `read_drug_chapter(yj_full, section_title)` で原文取得。
 
-最終回答は箇条書き or 表で、各事実に出典を付ける。
+Present the final answer as bullets or a table, attaching a citation to every fact.
 
-【絶対ルール】
+## Absolute Rules
-1. ツール呼び出し必須。トレーニング知識・教科書・ガイドラインからの推測は禁止。
+1. Tool calls are mandatory. Never infer from training knowledge, textbooks, or guidelines.
-2. 数値・固有名・条件は本文表現を改変せず逐語引用。
+2. Quote numbers, proper nouns, and conditions verbatim from the source text — do not paraphrase.
-3. 出典は **必ず** `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]` の形式。
+3. Text citation is required, in exactly this format: `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]`.
-   - fact 表 row には `_citation` フィールドが入っているので **そのまま転記**。
+   - Fact-table rows include a `_citation` field — copy it verbatim.
-   - `[出典: 薬品マスター]` `[出典: 添付文書]` 等の汎用出典は **絶対禁止**。
+   - Generic citations such as `[出典: 薬品マスター]` or `[出典: 添付文書]` are PROHIBITED.
-   - read_drug_chapter で実際に読んだ section 以外の出典を捏造しない。
+   - Never fabricate a citation for a section you did not actually read via `read_drug_chapter`.
-4. 該当情報が無ければ "添付文書からは確認できません" と書く。
+4. If the information cannot be found, write "添付文書からは確認できません".
+
+## Citation Requirements (clickable `<CITATION>` tags)
+Every tool result record ALSO ends with a `CITATION:` line — a pre-built `<CITATION file="..." filename="..." />` clickable tag that the frontend PDF-highlight pipeline depends on. Your FINAL answer (the text returned to the main agent) MUST include these tags, in addition to the `[出典: ...]` text — otherwise the citation is not clickable and the tag is lost.
+- Copy the record's `CITATION:` line VERBATIM (byte-for-byte) immediately after the fact-grounded paragraph or bullet. NEVER collect tags at the end of the answer.
+- Do NOT add, modify, reorder, or remove any attribute. Do NOT construct a `<CITATION>` tag yourself.
+- At most one `<CITATION>` per unique file.
+- `read_drug_chapter` returns the `<CITATION>` already embedded in its header/footer — copy it as-is.
+- Records without a `CITATION:` line → emit the `[出典: ...]` text only; never fabricate an empty tag.
+- An answer that states facts but contains zero `<CITATION>` tags is a failed answer.

skills/developing/pmda-drug-info/db.py

@@ -0,0 +1,80 @@
+"""Postgres 连接 helper。
+
+配置全部走环境变量，默认指向 docker-compose 起的本地实例：
+
+  PMDA_PG_HOST       (默认 localhost)
+  PMDA_PG_PORT       (默认 5432)
+  PMDA_PG_DB         (默认 pmda)
+  PMDA_PG_USER       (默认 pmda)
+  PMDA_PG_PASSWORD   (默认 pmda_local_dev — 仅本地开发，生产由 secret 注入)
+
+`connect()` 返回 psycopg3 connection（autocommit=False）。
+长时跑批时使用 `pool()` 取 ConnectionPool。
+"""
+from __future__ import annotations
+
+import os
+from contextlib import contextmanager
+from typing import Iterator
+
+import psycopg
+from psycopg import Connection
+from psycopg_pool import ConnectionPool
+
+PG_HOST = os.environ.get("PMDA_PG_HOST", "localhost")
+PG_PORT = int(os.environ.get("PMDA_PG_PORT", "5432"))
+PG_DB = os.environ.get("PMDA_PG_DB", "pmda")
+PG_USER = os.environ.get("PMDA_PG_USER", "pmda")
+PG_PASSWORD = os.environ.get("PMDA_PG_PASSWORD", "pmda_local_dev")
+
+
+def conninfo() -> str:
+    return (
+        f"host={PG_HOST} port={PG_PORT} dbname={PG_DB} "
+        f"user={PG_USER} password={PG_PASSWORD}"
+    )
+
+
+def connect(*, autocommit: bool = False) -> Connection:
+    """Open a single connection. Caller is responsible for closing."""
+    return psycopg.connect(conninfo(), autocommit=autocommit)
+
+
+@contextmanager
+def session(*, autocommit: bool = False) -> Iterator[Connection]:
+    """`with session() as conn:` — auto close on exit."""
+    conn = connect(autocommit=autocommit)
+    try:
+        yield conn
+        if not autocommit:
+            conn.commit()
+    except Exception:
+        if not autocommit:
+            conn.rollback()
+        raise
+    finally:
+        conn.close()
+
+
+_pool: ConnectionPool | None = None
+
+
+def pool(min_size: int = 1, max_size: int = 8) -> ConnectionPool:
+    """Lazy-init module-level pool. Use for batch / agent-loop hot path."""
+    global _pool
+    if _pool is None:
+        _pool = ConnectionPool(
+            conninfo(),
+            min_size=min_size,
+            max_size=max_size,
+            kwargs={"autocommit": False},
+            open=True,
+        )
+    return _pool
+
+
+def close_pool() -> None:
+    global _pool
+    if _pool is not None:
+        _pool.close()
+        _pool = None

skills/developing/pmda-drug-info/drug_category.md

@@ -0,0 +1,206 @@
+
+- 11 中枢神経系用薬
+  - 111 全身麻酔剤
+  - 112 催眠鎮静剤，抗不安剤
+  - 113 抗てんかん剤
+  - 114 解熱鎮痛消炎剤
+  - 115 興奮剤，覚醒剤
+  - 116 抗パーキンソン剤
+  - 117 精神神経用剤
+  - 118 総合感冒剤
+  - 119 その他の中枢神経系用薬
+- 12 末梢神経用薬
+  - 121 局所麻酔剤
+  - 122 骨格筋弛緩剤
+  - 123 自律神経剤
+  - 124 鎮けい剤
+  - 125 発汗剤，止汗剤
+  - 129 その他の末梢神経系用薬
+- 13 感覚器用薬
+  - 131 眼科用剤
+  - 132 耳鼻科用剤
+  - 133 鎮暈剤
+  - 139 その他の感覚器官用薬
+- 19 その他の神経系及び感覚器官用医薬品
+- 21 循環器官用薬
+  - 211 強心剤
+  - 212 不整脈用剤
+  - 213 利尿剤
+  - 214 血圧降下剤
+  - 215 血管補強剤
+  - 216 血管収縮剤
+  - 217 血管拡張剤
+  - 218 高脂血症用剤
+  - 219 その他の循環器官用薬
+- 22 呼吸器官用薬
+  - 221 呼吸促進剤
+  - 222 鎮咳剤
+  - 223 去たん剤
+  - 224 鎮咳去たん剤
+  - 225 気管支拡張剤
+  - 226 含嗽剤
+  - 229 その他の呼吸器官用薬
+- 23 消化器官用薬
+  - 231 止しゃ剤，整腸剤
+  - 232 消化性潰瘍用剤
+  - 233 健胃消化剤
+  - 234 制酸剤
+  - 235 下剤，浣腸剤
+  - 236 利胆剤
+  - 237 複合胃腸剤
+  - 239 その他の消化器官用薬
+- 24 ホルモン剤（抗ホルモン剤を含む）
+  - 241 脳下垂体ホルモン剤
+  - 242 唾液腺ホルモン剤
+  - 243 甲状腺，副甲状腺ホルモン剤
+  - 244 たん白同化ステロイド剤
+  - 245 副腎ホルモン剤
+  - 246 男性ホルモン剤
+  - 247 卵胞ホルモン及び黄体ホルモン剤
+  - 248 混合ホルモン剤
+  - 249 その他のホルモン剤（抗ホルモン剤を含む）
+- 25 泌尿生殖器官及び肛門用薬
+  - 251 泌尿器官用剤
+  - 252 生殖器官用剤（性病予防剤を含む。）
+  - 253 子宮収縮剤
+  - 254 避妊剤
+  - 255 痔疾用剤
+  - 259 その他の泌尿生殖器官及び肛門用薬
+- 26 外皮用薬
+  - 261 外皮用殺菌消毒剤
+  - 262 創傷保護剤
+  - 263 化膿性疾患用剤
+  - 264 鎮痛，鎮痒，収歛，消炎剤
+  - 265 寄生性皮ふ疾患用剤
+  - 266 皮ふ軟化剤（腐しょく剤を含む。）
+  - 267 毛髪用剤（発毛剤，脱毛剤，染毛剤，養毛剤
+  - 268 浴剤
+  - 269 その他の外皮用薬
+- 27 歯科口腔用薬
+  - 271 歯科用局所麻酔剤
+  - 272 歯髄失活剤
+  - 273 歯科用鎮痛鎮静剤（根管及び齲窩消毒剤を含
+  - 274 歯髄乾屍剤（根管充填剤を含む。）
+  - 275 歯髄覆たく剤
+  - 276 歯科用抗生物質製剤
+  - 279 その他の歯科口腔用薬
+- 29 その他の個々の器官系用医薬品
+  - 290 その他の個々の器官系用医薬品
+- 31 ビタミン剤
+  - 311 ビタミンＡ及びＤ剤
+  - 312 ビタミンＢ１剤
+  - 313 ビタミンＢ剤（ビタミンＢ１剤を除く。）
+  - 314 ビタミンＣ剤
+  - 315 ビタミンＥ剤
+  - 316 ビタミンＫ剤
+  - 317 混合ビタミン剤（ビタミンＡ・Ｄ混合製剤を除く）
+  - 319 その他のビタミン剤
+- 32 滋養強壮薬
+  - 321 カルシウム剤
+  - 322 無機質製剤
+  - 323 糖類剤
+  - 324 有機酸製剤
+  - 325 たん白アミノ酸製剤
+  - 326 臓器製剤
+  - 327 乳幼児用剤
+  - 329 その他の滋養強壮薬
+- 33 血液・体液用薬
+  - 331 血液代用剤
+  - 332 止血剤
+  - 333 血液凝固阻止剤
+  - 339 その他の血液・体液用薬
+- 34 人工透析用薬
+  - 341 人工腎臓透析用剤
+  - 342 腹膜透析用剤
+  - 349 その他の人工透析用薬
+- 39 その他の代謝性医薬品
+  - 391 肝臓疾患用剤
+  - 392 解毒剤
+  - 393 習慣性中毒用剤
+  - 394 痛風治療剤
+  - 395 酵素製剤
+  - 396 糖尿病用剤
+  - 397 総合代謝性製剤
+  - 399 他に分類されない代謝性医薬品
+- 41 細胞賦活用薬
+  - 411 クロロフィル製剤
+  - 412 色素製剤
+  - 419 その他の細胞賦活用薬
+- 42 腫瘍用薬
+  - 421 アルキル化剤
+  - 422 代謝拮抗剤
+  - 423 抗腫瘍性抗生物質製剤
+  - 424 抗腫瘍性植物成分製剤
+  - 429 その他の腫瘍用薬
+- 43 放射性医薬品
+  - 430 放射性医薬品
+- 44 アレルギー用薬
+  - 441 抗ヒスタミン剤
+  - 442 刺激療法剤
+  - 443 非特異性免疫原製剤
+  - 449 その他のアレルギー用薬
+- 49 その他の組織細胞機能用医薬品
+  - 490 その他の組織細胞機能用医薬品
+- 51 生薬
+  - 510 生薬
+- 52 漢方製剤
+  - 520 漢方製剤
+- 59 その他の生薬及び漢方処方に基づく医薬品
+  - 590 その他の生薬及び漢方処方に基づく医薬品
+- 61 抗生物質製剤
+  - 611 主としてグラム陽性菌に作用するもの
+  - 612 主としてグラム陰性菌に作用するもの
+  - 613 主としてグラム陽性・陰性菌に作用するもの
+  - 614 主としてグラム陽性菌，マイコプラズマに作用するもの
+  - 615 主としてグラム陽性・陰性菌，リケッチア，クラミジアに作用するもの
+  - 616 主として抗酸菌に作用するもの
+  - 617 主としてカビに作用するもの
+  - 619 その他の抗生物質製剤（複合抗生物質製剤を含む）
+- 62 化学療法剤
+  - 621 サルファ剤
+  - 622 抗結核剤
+  - 623 抗ハンセン病剤
+  - 624 合成抗菌剤
+  - 625 抗ウイルス剤
+  - 629 その他の化学療法剤
+- 63 生物学的製剤
+  - 631 ワクチン類
+  - 632 毒素及びトキソイド類
+  - 633 抗毒素類及び抗レプトスピラ血清類
+  - 634 血液製剤類
+  - 635 生物学的試験用製剤類
+  - 636 混合生物学的製剤
+  - 639 その他の生物学的製剤
+- 64 寄生動物用薬
+  - 641 抗原虫剤
+  - 642 駆虫剤
+  - 649 その他の寄生動物用薬
+- 69 その他の病原生物に対する医薬品
+  - 690 その他の病原生物に対する医薬品
+- 71 調剤用薬
+  - 711 賦形剤
+  - 712 軟膏基剤
+  - 713 溶解剤
+  - 714 矯味，矯臭，着色剤
+  - 715 乳化剤
+  - 719 その他の調剤用薬
+- 72 診断用薬（体外診断用医薬品を除く）
+  - 721 Ｘ線造影剤
+  - 722 機能検査用試薬
+  - 729 その他の診断用薬
+- 73 公衆衛生用薬
+  - 731 防腐剤
+  - 732 防疫用殺菌消毒剤
+  - 733 防虫剤
+  - 734 殺虫剤
+  - 735 殺そ剤
+  - 739 その他の公衆衛生用薬
+- 79 その他の治療を主目的としない医薬品
+  - 791 ばん創こう
+  - 799 他に分類されない治療を主目的としない医薬品
+- 81 アルカロイド系麻薬（天然麻薬）
+  - 811 アヘンアルカロイド系麻薬
+  - 812 コカアルカロイド系製剤
+  - 819 その他のアルカロイド系麻薬（天然麻薬）
+- 82 非アルカロイド系麻薬
+  - 821 合成麻薬

skills/developing/pmda-drug-info/hooks/pmda-instructions.md

@@ -2,19 +2,92 @@
 
 You have access to Japanese pharmaceutical package insert (添付文書) data via the following tools.
 
+## Tool output format
+
+Tools return **plain text**, not JSON. Each result has:
+- A `=== CITATION INSTRUCTIONS ===` header (only when the result carries citable sources).
+- A `Found N ...:` summary line, then one numbered record block per row.
+- Inside each block: indented `label: value` fields, an optional `出典: [...]` line, and a
+  `CITATION: <CITATION ... />` line (the pre-built clickable tag).
+
+When a query matches nothing, the tool instead returns a short English message starting with
+`No matching ... were found` and **no** citation instructions. In that case tell the user no
+relevant material was retrieved — **do NOT** invent or emit any `<CITATION>` tag.
+
 ## Core Rules
 - **Tool calls are mandatory.** Never answer from training knowledge alone. All facts must come from tool results.
-- Cite sources in the format: `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]`
+- Cite sources in the format: `[出典: <販売名> (yj_full=<id>) / <章番号 章タイトル>]` — taken from each record's `出典:` line.
-- Fact table rows include a `_citation` field — use it directly.
 - Generic citations like `[出典: 薬品マスター]` or `[出典: 添付文書]` are **prohibited**.
 - For urgent questions (suicide/drug abuse/severe acute symptoms), state: "緊急対応として担当医・薬剤師に直接相談してください"
 
+## Clickable Citation (<CITATION> tag) — MUST copy the record's `CITATION:` line
+
+After each fact-grounded paragraph or bullet list, copy that record's **`CITATION:` line VERBATIM**. Do NOT construct the tag yourself.
+
+### Why verbatim copy
+
+The tool already built the full `<CITATION ... />` string for you on the `CITATION:` line. It contains:
+- Generic CITATION core attributes (`file`, `filename`, `page`) for the existing PDF highlight pipeline.
+- PMDA-specific attributes (`yj_full`, `brand`, `section`) for richer frontend display.
+
+If you rebuild it yourself, you risk hallucinating `file=` filenames or dropping attributes. **Just copy the `CITATION:` line byte-for-byte** (drop the leading `CITATION: ` label, keep the `<CITATION ... />` tag).
+
+### Rules
+
+- Each record's `CITATION:` line is the complete `<CITATION ... />` string.
+- **Emit it exactly as-is. Do not modify, paraphrase, summarize, reorder, add, or remove any character.**
+- Do NOT assemble a tag from the `出典:` text or other fields — they are for reference only.
+- If a record has **no** `CITATION:` line, emit only the `[出典: ...]` text — never invent any CITATION attributes.
+
+### Multiple citations within the same paragraph
+
+- Each fact record gets its own `<CITATION>` tag — emit the `CITATION:` line from that record.
+- Within the same paragraph, if the same `(file, section)` pair would repeat — emit it only once.
+- Same drug × different sections: one tag per section, back-to-back.
+- Different drugs: each tag stands alone.
+
+### Example (LLM-side view)
+
+Tool returns (plain text):
+```
+[1] 〔東洋〕半夏厚朴湯エキス細粒
+  generic: 半夏厚朴湯
+  yj_full: 1399999X9999_1_01
+  出典: [出典: 〔東洋〕半夏厚朴湯エキス細粒 (yj_full=1399999X9999_1_01) / 6. 用法及び用量]
+  CITATION: <CITATION file="abc-uuid" filename="999999_1399999X9999_1_01.xml" page=0 yj_full="1399999X9999_1_01" brand="〔東洋〕半夏厚朴湯エキス細粒" section="6. 用法及び用量" />
+```
+
+Your reply (correct):
+```
+用法は 1日3回。
+<CITATION file="abc-uuid" filename="999999_1399999X9999_1_01.xml" page=0 yj_full="1399999X9999_1_01" brand="〔東洋〕半夏厚朴湯エキス細粒" section="6. 用法及び用量" />
+```
+
+Your reply (WRONG — reconstructed by hand):
+```
+用法は 1日3回。
+<CITATION file="千里牛香_添付文書.pdf" />   ← hallucinated, missing attributes
+```
+
+## Citation Requirements
+
+- You MUST emit a `<CITATION ... />` tag whenever you use a tool result. Copy the record's `CITATION:` line verbatim — never construct one.
+- Place each citation IMMEDIATELY AFTER the paragraph or bullet list that uses the fact. NEVER collect citations at the end of the response.
+- At most one tag per unique file. At least one `<CITATION>` is required whenever the answer is grounded in tool results.
+- An answer that states tool-grounded facts but contains zero `<CITATION>` tags is a failed answer.
+
 ## When to Use Sub-agents (task tool)
 - **patient_specific**: Renal/hepatic/pregnancy/elderly/pediatric/allergy conditions × dosing decisions
 - **interaction**: Pairwise drug interaction investigation
 - **adverse_event**: Reverse lookup from adverse event name to drugs
 - **single_drug**: Detailed info not in fact tables (e.g., full adverse event list, pharmacokinetics)
 
+### Sub-agent citation pass-through (CRITICAL)
+- A sub-agent's returned text already contains `<CITATION ... />` tags built from the tools it called. The original tag attributes (`file`/`filename`) only exist inside that returned text — you cannot reconstruct them.
+- You MUST preserve every `<CITATION ... />` tag from the sub-agent output VERBATIM and re-emit it in your final answer, keeping it immediately after the fact it supports.
+- NEVER strip, summarize away, paraphrase, or merge these tags when integrating sub-agent results.
+- A final answer that relies on sub-agent facts but contains zero `<CITATION>` tags is a failed answer.
+
 ## Direct Tool Usage (do NOT delegate)
 - Simple lookups → use tools directly
 - Multi-drug comparisons → call tools sequentially, output as markdown table

skills/developing/pmda-drug-info/os_client.py

@@ -0,0 +1,157 @@
+"""OpenSearch `pmda_sections` index spec + client helper.
+
+Mapping 与 wiki-skill 的 sudachi 配置共用 plugin（同一 OS 集群、同一 sudachi
+core 字典）。每个 doc 对应一份说明书的一个章节节点，冗余存药品 metadata 以避
+免 JOIN（详见 design.md §2.1.2）。
+
+环境变量：
+  OS_HOST       (默认 http://localhost:9200，与 wiki-skill `_common.py` 一致)
+  PMDA_OS_INDEX (默认 pmda_sections)
+"""
+from __future__ import annotations
+
+import os
+
+from opensearchpy import OpenSearch
+
+# Plugin env vars: PMDA_OPENSEARCH_URL（推奨）/ OPENSEARCH_URL / OPENSEARCH_HOST
+OS_HOST = (
+    os.environ.get("PMDA_OPENSEARCH_URL")
+    or os.environ.get("OPENSEARCH_URL")
+    or os.environ.get("OPENSEARCH_HOST")
+    or "http://localhost:9200"
+)
+INDEX_NAME = os.environ.get("PMDA_OS_INDEX", "pmda_sections")
+
+
+# ---- Mapping spec --------------------------------------------------------
+
+INDEX_BODY: dict = {
+    "settings": {
+        "index": {
+            "number_of_shards": 1,
+            "number_of_replicas": 0,
+            "refresh_interval": "1s",
+        },
+        "analysis": {
+            "tokenizer": {
+                "sudachi_tokenizer": {
+                    "type": "sudachi_tokenizer",
+                    "split_mode": "C",
+                    "discard_punctuation": True,
+                }
+            },
+            "filter": {
+                # "med_synonyms": {
+                #     "type": "synonym",
+                    # 初期最小集 — 命中错例后扩充。同义词条之间逗号分隔代表
+                    # 等价、空格视为词内字符。
+                    # "synonyms": [
+                    #     "Stevens-Johnson, 皮膚粘膜眼症候群, SJS",
+                    #     "中毒性表皮壊死融解症, TEN, ライエル症候群",
+                    #     "QT延長, トルサード, Torsades de pointes",
+                    #     "間質性肺炎, 肺臓炎",
+                    #     "横紋筋融解症, ラブドミオリーシス",
+                    #     "アナフィラキシー, アナフィラキシーショック",
+                    #     "無顆粒球症, 顆粒球減少症",
+                    # ],
+                # },
+                "jp_pos": {
+                    "type": "sudachi_part_of_speech",
+                },
+                "jp_stop": {
+                    "type": "sudachi_ja_stop",
+                },
+            },
+            "analyzer": {
+                "jp_med": {
+                    "type": "custom",
+                    # icu_normalizer はデフォルト image に未含、sudachi_
+                    # normalizedform で全角半角・正規化はカバーされる。
+                    "tokenizer": "sudachi_tokenizer",
+                    "filter": [
+                        "sudachi_baseform",
+                        "sudachi_normalizedform",
+                        "jp_pos",
+                        "jp_stop",
+                        "lowercase",
+                    ],
+                }
+            },
+        },
+    },
+    "mappings": {
+        "properties": {
+            "yj_full":       {"type": "keyword"},
+            "yj_code":       {"type": "keyword"},
+            "l1_code":       {"type": "keyword"},
+            "l2_code":       {"type": "keyword"},
+            "l2_name":       {"type": "keyword"},
+            "category_name": {"type": "keyword"},
+            "brand_names":   {"type": "keyword"},
+            "generic_name":  {"type": "keyword"},
+            "section_title": {
+                "type": "text",
+                "analyzer": "jp_med",
+                "fields": {"raw": {"type": "keyword"}},
+            },
+            "line_num":      {"type": "integer"},
+            "text":          {"type": "text", "analyzer": "jp_med"},
+            "revision_date": {"type": "date"},
+            "_md_sha256":    {"type": "keyword"},
+        }
+    },
+}
+
+
+# ---- Client --------------------------------------------------------------
+
+
+def client() -> OpenSearch:
+    """Return an OpenSearch client bound to OS_HOST."""
+    return OpenSearch(hosts=[OS_HOST], http_compress=True, timeout=60)
+
+
+# ---- 章節アクセス helpers（PageIndex 退役後の verbatim 取得経路） -------
+
+
+def list_drug_sections(yj_full: str, *, limit: int = 200) -> list[dict]:
+    """1 薬の全章節を line_num 昇順で返す。
+
+    各 element: {section_title, line_num, text_len, brand, generic}
+    """
+    cli = client()
+    resp = cli.search(index=INDEX_NAME, body={
+        "size": min(limit, 500),
+        "_source": ["section_title", "line_num", "text", "brand_names", "generic_name"],
+        "query": {"term": {"yj_full": yj_full}},
+        "sort": [{"line_num": "asc"}],
+    })
+    out = []
+    for h in resp["hits"]["hits"]:
+        s = h["_source"]
+        out.append({
+            "section_title": s.get("section_title", ""),
+            "line_num": s.get("line_num"),
+            "text_len": len(s.get("text", "") or ""),
+            "brand": (s.get("brand_names") or [""])[0],
+            "generic": s.get("generic_name") or "",
+        })
+    return out
+
+
+def get_drug_section_text(yj_full: str, section_title: str) -> str:
+    """指定 (yj_full, section_title) の verbatim 章節 text。見つからなければ ""。"""
+    cli = client()
+    resp = cli.search(index=INDEX_NAME, body={
+        "size": 1,
+        "_source": ["text"],
+        "query": {"bool": {"must": [
+            {"term": {"yj_full": yj_full}},
+            {"term": {"section_title.raw": section_title}},
+        ]}},
+    })
+    hits = resp["hits"]["hits"]
+    if not hits:
+        return ""
+    return hits[0]["_source"].get("text", "") or ""

skills/developing/pmda-drug-info/pmda_tools.json

@@ -11,7 +11,12 @@
         },
         "kind": {
           "type": "string",
-          "enum": ["auto", "brand", "generic", "yj"],
+          "enum": [
+            "auto",
+            "brand",
+            "generic",
+            "yj"
+          ],
           "description": "Search type. 'auto' searches all fields.",
           "default": "auto"
         },
@@ -21,7 +26,9 @@
           "default": 10
         }
       },
-      "required": ["query"]
+      "required": [
+        "query"
+      ]
     }
   },
   {
@@ -48,7 +55,9 @@
           "default": 50
         }
       },
-      "required": ["l2_code"]
+      "required": [
+        "l2_code"
+      ]
     }
   },
   {
@@ -62,7 +71,9 @@
           "description": "12-character YJ code."
         }
       },
-      "required": ["yj_code"]
+      "required": [
+        "yj_code"
+      ]
     }
   },
   {
@@ -145,7 +156,9 @@
           "default": 20
         }
       },
-      "required": ["drug_yj"]
+      "required": [
+        "drug_yj"
+      ]
     }
   },
   {
@@ -169,7 +182,9 @@
           "default": 30
         }
       },
-      "required": ["keyword"]
+      "required": [
+        "keyword"
+      ]
     }
   },
   {
@@ -183,7 +198,9 @@
           "description": "Full YJ code (with revision suffix, e.g., 3399007H1021_1_21)."
         }
       },
-      "required": ["yj_full"]
+      "required": [
+        "yj_full"
+      ]
     }
   },
   {
@@ -201,7 +218,10 @@
           "description": "Exact section title from list_drug_chapters (e.g., '9.2 腎機能障害患者', '11.1 重大な副作用')."
         }
       },
-      "required": ["yj_full", "section_title"]
+      "required": [
+        "yj_full",
+        "section_title"
+      ]
     }
   }
 ]

skills/developing/pmda-drug-info/queries.py

@@ -0,0 +1,427 @@
+"""SQL 查询接口（关系小库侧）。
+
+Phase 1 已承接：
+  - `search_drugs` 的 商品名 / 一般名 / YJ 子串检索
+  - `list_categories` 的 L1/L2 + drug_count
+  - `list_drugs_in_category` 的 一般名 → 販売名
+
+后续 Phase 2 会接 drug_interaction / drug_restriction / drug_dosing。
+"""
+from __future__ import annotations
+
+import re
+from collections import OrderedDict
+from dataclasses import dataclass
+from pathlib import Path
+
+from taxonomy import load_taxonomy
+from db import session
+
+# Plugin 自包含: drug_category.md 与 queries.py 同目录
+_TAXONOMY_PATH = Path(__file__).resolve().parent / "drug_category.md"
+_TAXONOMY_CACHE = None
+
+
+def _taxonomy():
+    global _TAXONOMY_CACHE
+    if _TAXONOMY_CACHE is None:
+        _TAXONOMY_CACHE = load_taxonomy(_TAXONOMY_PATH)
+    return _TAXONOMY_CACHE
+
+# 12 字母数字 → YJ code 候选；前几位即足够触发自动 kind=yj 的判断
+_YJ_RE = re.compile(r"^[0-9A-Z]{4,12}$")
+
+
+@dataclass(frozen=True)
+class DrugHit:
+    yj_full: str
+    yj_code: str
+    brand_name: str       # "/" 分隔多品名
+    generic_name: str
+    category_code: str
+    category_name: str
+    score: float          # 50-100
+
+
+def _detect_kind(q: str) -> str:
+    """auto-detect: pure alnum & uppercase 4+ chars → yj, otherwise any."""
+    if _YJ_RE.match(q.upper()):
+        return "yj"
+    return "any"
+
+
+def _score_expr(q_lower: str, q_like: str) -> str:
+    """Postgres expression returning relevance score 50–100."""
+    # NB: doubles each pattern; psycopg expands %s positionally so caller
+    # must pass q_lower / q_like in matching repetitions.
+    return (
+        "GREATEST("
+        " CASE WHEN lower(brand_name) = %s THEN 100.0 "
+        "      WHEN lower(brand_name) LIKE %s || '%%' THEN 90.0 "
+        "      WHEN brand_name ILIKE %s THEN 70.0 ELSE 0 END,"
+        " CASE WHEN lower(generic_name_jp) = %s THEN 95.0 "
+        "      WHEN lower(generic_name_jp) LIKE %s || '%%' THEN 85.0 "
+        "      WHEN generic_name_jp ILIKE %s THEN 65.0 ELSE 0 END,"
+        " CASE WHEN yj_code = %s THEN 100.0 ELSE 0 END"
+        ")"
+    )
+
+
+def search_drugs_in_db(
+    query: str,
+    *,
+    kind: str = "auto",
+    limit: int = 20,
+) -> list[DrugHit]:
+    """Drop-in replacement for the in-memory ``CorpusIndex.search``.
+
+    `kind` ∈ {"auto", "brand", "generic", "yj"}.
+
+    Returns DrugHit list (max ``limit``) ordered by relevance score desc.
+    """
+    q = (query or "").strip()
+    if not q:
+        return []
+    if kind == "auto":
+        kind = _detect_kind(q)
+
+    q_lower = q.lower()
+    q_like = f"%{q}%"
+    q_upper = q.upper()
+
+    if kind == "yj":
+        sql = """
+        SELECT yj_full, yj_code, brand_name, generic_name_jp,
+               category_code, category_name,
+               CASE WHEN yj_code = %s THEN 100.0
+                    WHEN yj_full LIKE %s || '%%' THEN 95.0
+                    ELSE 80.0 END AS score
+        FROM drug_master
+        WHERE yj_code LIKE %s OR yj_full LIKE %s
+        ORDER BY score DESC, yj_full ASC
+        LIMIT %s
+        """
+        params = (q_upper, q_upper, f"{q_upper}%", f"{q_upper}%", limit)
+    elif kind == "brand":
+        sql = """
+        SELECT yj_full, yj_code, brand_name, generic_name_jp,
+               category_code, category_name,
+               CASE WHEN lower(brand_name) = %s THEN 100.0
+                    WHEN lower(brand_name) LIKE %s || '%%' THEN 90.0
+                    ELSE 70.0 END AS score
+        FROM drug_master
+        WHERE brand_name ILIKE %s
+        ORDER BY score DESC, length(brand_name) ASC, yj_full ASC
+        LIMIT %s
+        """
+        params = (q_lower, q_lower, q_like, limit)
+    elif kind == "generic":
+        sql = """
+        SELECT yj_full, yj_code, brand_name, generic_name_jp,
+               category_code, category_name,
+               CASE WHEN lower(generic_name_jp) = %s THEN 95.0
+                    WHEN lower(generic_name_jp) LIKE %s || '%%' THEN 85.0
+                    ELSE 65.0 END AS score
+        FROM drug_master
+        WHERE generic_name_jp ILIKE %s
+        ORDER BY score DESC, length(generic_name_jp) ASC, yj_full ASC
+        LIMIT %s
+        """
+        params = (q_lower, q_lower, q_like, limit)
+    else:  # any
+        sql = f"""
+        SELECT yj_full, yj_code, brand_name, generic_name_jp,
+               category_code, category_name,
+               {_score_expr(q_lower, q_like)} AS score
+        FROM drug_master
+        WHERE brand_name ILIKE %s OR generic_name_jp ILIKE %s
+           OR yj_code LIKE %s OR yj_full LIKE %s
+        ORDER BY score DESC, length(brand_name) ASC, yj_full ASC
+        LIMIT %s
+        """
+        # _score_expr 占位符顺序：brand=, brand LIKE, brand ILIKE,
+        #                       generic=, generic LIKE, generic ILIKE, yj_code=
+        # 然后 WHERE: brand ILIKE, generic ILIKE, yj LIKE, yj_full LIKE
+        params = (
+            q_lower, q_lower, q_like,
+            q_lower, q_lower, q_like,
+            q_upper,
+            q_like, q_like, f"{q_upper}%", f"{q_upper}%",
+            limit,
+        )
+
+    with session() as conn, conn.cursor() as cur:
+        cur.execute(sql, params)
+        rows = cur.fetchall()
+
+    return [
+        DrugHit(
+            yj_full=r[0],
+            yj_code=r[1],
+            brand_name=r[2] or "",
+            generic_name=r[3] or "",
+            category_code=r[4] or "",
+            category_name=r[5] or "",
+            score=float(r[6] or 0),
+        )
+        for r in rows
+    ]
+
+
+# ---- 类别导航 ------------------------------------------------------------
+
+
+def list_categories_with_counts() -> list[dict]:
+    """全 L1 / L2 分类 + 各 L2 的 drug 数。
+
+    分类层级名取自 drug_category.md（不用 PMDA 的 category_name 自由文，
+    因为后者一药一表达，难以聚合）；drug_count 取自 drug_master 的实际行数。
+    """
+    tax = _taxonomy()
+    with session() as conn, conn.cursor() as cur:
+        cur.execute(
+            "SELECT category_code, COUNT(*) FROM drug_master "
+            "WHERE category_code IS NOT NULL "
+            "GROUP BY category_code"
+        )
+        counts: dict[str, int] = dict(cur.fetchall())
+
+    by_l1: dict[str, dict] = {}
+    for l2_code, l2 in tax.items():
+        c = counts.get(l2_code, 0)
+        if c == 0:
+            continue
+        l1 = by_l1.setdefault(
+            l2.l1_code,
+            {"l1_code": l2.l1_code, "l1_name": l2.l1_name, "l2": []},
+        )
+        l1["l2"].append({"code": l2_code, "name": l2.name, "drug_count": c})
+    # 内层按 code 排序，外层按 l1_code 排序
+    for l1 in by_l1.values():
+        l1["l2"].sort(key=lambda x: x["code"])
+    return [by_l1[k] for k in sorted(by_l1)]
+
+
+def list_drugs_in_category(
+    l2_code: str,
+    *,
+    limit_generics: int = 50,
+    brands_per_generic: int = 5,
+) -> dict:
+    """指定 L2 类目下的「一般名 → [販売名]」一览。
+
+    Returns the same JSON shape `_corpus_tools.list_drugs_in_category` previously
+    yielded so the agent prompt 不变。
+    """
+    tax = _taxonomy()
+    l2 = tax.get(l2_code)
+    with session() as conn, conn.cursor() as cur:
+        cur.execute(
+            "SELECT generic_name_jp, yj_full, brand_name "
+            "FROM drug_master WHERE category_code = %s "
+            "ORDER BY generic_name_jp, yj_full",
+            (l2_code,),
+        )
+        rows = cur.fetchall()
+
+    by_gen: "OrderedDict[str, list[dict]]" = OrderedDict()
+    for gen, yj_full, brand in rows:
+        by_gen.setdefault(gen or "（一般名不明）", []).append(
+            {"brand": brand or "", "yj_full": yj_full}
+        )
+
+    payload: list[dict] = []
+    for gen in list(by_gen)[:limit_generics]:
+        drugs = by_gen[gen]
+        shown = drugs[:brands_per_generic]
+        extra = len(drugs) - len(shown)
+        entry = {"generic": gen, "drugs": list(shown)}
+        if extra > 0:
+            entry["drugs"].append({"_more": f"+{extra} more brands"})
+        payload.append(entry)
+
+    out = {
+        "l2_code": l2_code,
+        "l2_name": l2.name if l2 else "",
+        "generics": payload,
+    }
+    if len(by_gen) > limit_generics:
+        out["_more_generics"] = len(by_gen) - limit_generics
+    return out
+
+
+# ---- fact 查询：drug_master / interaction / restriction / dosing ----------
+
+
+@dataclass(frozen=True)
+class DrugMasterRow:
+    yj_code: str
+    yj_full: str
+    brand_name: str
+    generic_name_jp: str
+    category_code: str
+    category_name: str
+    regulation: str | None
+    manufacturer: str | None
+    revision_date: str | None  # ISO date string
+
+
+def drug_master_get(yj_code: str) -> DrugMasterRow | None:
+    with session() as conn, conn.cursor() as cur:
+        cur.execute(
+            "SELECT yj_code, yj_full, brand_name, generic_name_jp, "
+            "       category_code, category_name, regulation, manufacturer, "
+            "       to_char(revision_date, 'YYYY-MM-DD') "
+            "FROM drug_master WHERE yj_code = %s",
+            (yj_code,),
+        )
+        row = cur.fetchone()
+    if not row:
+        return None
+    return DrugMasterRow(*row)
+
+
+@dataclass(frozen=True)
+class InteractionRow:
+    id: str
+    drug_a_yj: str
+    drug_b_yj: str | None
+    drug_b_class: str | None
+    severity: str
+    mechanism: str | None
+    clinical_effect: str | None
+    source_section: str
+    source_drug_yj: str
+
+
+def drug_interaction_query(
+    drug_a_yj: str | None = None,
+    drug_b_yj: str | None = None,
+    *,
+    severity: str | None = None,
+    keyword: str | None = None,
+    limit: int = 50,
+) -> list[InteractionRow]:
+    """検索条件:
+        drug_a_yj alone → drug_a の全相互作用（drug_b 任意）
+        drug_a_yj + drug_b_yj → 双向（A→B もしくは B→A 両方）
+        keyword → drug_b_class や mechanism / clinical_effect の ILIKE
+    """
+    where = []
+    params: list = []
+    if drug_a_yj and drug_b_yj:
+        where.append("((drug_a_yj=%s AND drug_b_yj=%s) OR "
+                     "(drug_a_yj=%s AND drug_b_yj=%s))")
+        params += [drug_a_yj, drug_b_yj, drug_b_yj, drug_a_yj]
+    elif drug_a_yj:
+        where.append("drug_a_yj = %s")
+        params.append(drug_a_yj)
+    elif drug_b_yj:
+        where.append("drug_b_yj = %s")
+        params.append(drug_b_yj)
+    if severity:
+        where.append("severity = %s")
+        params.append(severity)
+    if keyword:
+        where.append("(drug_b_class ILIKE %s OR mechanism ILIKE %s "
+                     " OR clinical_effect ILIKE %s)")
+        kw = f"%{keyword}%"
+        params += [kw, kw, kw]
+    if not where:
+        return []
+    sql = (
+        "SELECT id, drug_a_yj, drug_b_yj, drug_b_class, severity, "
+        "       mechanism, clinical_effect, source_section, source_drug_yj "
+        "FROM drug_interaction WHERE " + " AND ".join(where) +
+        " ORDER BY severity, drug_b_class NULLS LAST LIMIT %s"
+    )
+    params.append(limit)
+    with session() as conn, conn.cursor() as cur:
+        cur.execute(sql, params)
+        return [InteractionRow(*r) for r in cur.fetchall()]
+
+
+@dataclass(frozen=True)
+class RestrictionRow:
+    id: str
+    drug_yj: str
+    condition_type: str
+    condition_text: str
+    condition_params: dict
+    severity: str
+    source_section: str
+
+
+def drug_restriction_query(
+    drug_yj: str | None = None,
+    *,
+    condition_type: str | None = None,
+    severity: str | None = None,
+    keyword: str | None = None,
+    limit: int = 50,
+) -> list[RestrictionRow]:
+    where = []
+    params: list = []
+    if drug_yj:
+        where.append("drug_yj = %s")
+        params.append(drug_yj)
+    if condition_type:
+        where.append("condition_type = %s")
+        params.append(condition_type)
+    if severity:
+        where.append("severity = %s")
+        params.append(severity)
+    if keyword:
+        where.append("condition_text ILIKE %s")
+        params.append(f"%{keyword}%")
+    if not where:
+        return []
+    sql = (
+        "SELECT id, drug_yj, condition_type, condition_text, condition_params, "
+        "       severity, source_section "
+        "FROM drug_restriction WHERE " + " AND ".join(where) +
+        " ORDER BY severity, condition_type LIMIT %s"
+    )
+    params.append(limit)
+    with session() as conn, conn.cursor() as cur:
+        cur.execute(sql, params)
+        return [RestrictionRow(*r) for r in cur.fetchall()]
+
+
+@dataclass(frozen=True)
+class DosingRow:
+    id: str
+    drug_yj: str
+    indication_code: str | None
+    patient_segment: str
+    segment_params: dict
+    dose_amount: float | None
+    dose_unit: str | None
+    frequency: str | None
+    duration: str | None
+    adjustment_text: str
+    source_section: str
+
+
+def drug_dosing_query(
+    drug_yj: str,
+    *,
+    patient_segment: str | None = None,
+    limit: int = 30,
+) -> list[DosingRow]:
+    where = ["drug_yj = %s"]
+    params: list = [drug_yj]
+    if patient_segment:
+        where.append("patient_segment = %s")
+        params.append(patient_segment)
+    sql = (
+        "SELECT id, drug_yj, indication_code, patient_segment, segment_params, "
+        "       dose_amount, dose_unit, frequency, duration, adjustment_text, "
+        "       source_section "
+        "FROM drug_dosing WHERE " + " AND ".join(where) +
+        " ORDER BY patient_segment, indication_code NULLS LAST LIMIT %s"
+    )
+    params.append(limit)
+    with session() as conn, conn.cursor() as cur:
+        cur.execute(sql, params)
+        return [DosingRow(*r) for r in cur.fetchall()]

skills/developing/pmda-drug-info/requirements.txt

@@ -0,0 +1,4 @@
+# Plugin self-contained: PG (psycopg) + OS (opensearch-py)
+psycopg[binary]>=3.2.0
+psycopg-pool>=3.2.0
+opensearch-py>=2.2.0

skills/developing/pmda-drug-info/taxonomy.py

@@ -0,0 +1,64 @@
+"""Drug-category taxonomy loader.
+
+Reads `pmda/drug_category.md` (the cleaned-up nested list with codes) and
+produces a `{l2_code: L2}` dict for joining with `DocMeta.l2_code` (first 3
+chars of the YJ code).
+
+Source markdown shape:
+
+    - 11 中枢神経系用薬
+      - 111 全身麻酔剤
+      - 112 催眠鎮静剤，抗不安剤
+    - 12 末梢神経用薬
+      - 121 局所麻酔剤
+"""
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+from pathlib import Path
+
+# Top-level: `- {2-digit} {name}`
+_L1_RE = re.compile(r"^- (\d{2})\s+(.+)$")
+# Nested: `  - {3-digit} {name}` (indent 2 spaces)
+_L2_RE = re.compile(r"^ {2}- (\d{3})\s+(.+)$")
+
+
+@dataclass(frozen=True)
+class L2:
+    code: str  # "111"
+    name: str  # "全身麻酔剤"
+    l1_code: str  # "11"
+    l1_name: str  # "中枢神経系用薬"
+
+
+def load_taxonomy(path: Path | str = "pmda/drug_category.md") -> dict[str, L2]:
+    out: dict[str, L2] = {}
+    current_l1_code = ""
+    current_l1_name = ""
+    for line in Path(path).read_text(encoding="utf-8").splitlines():
+        m1 = _L1_RE.match(line)
+        if m1:
+            current_l1_code, current_l1_name = m1.group(1), m1.group(2).strip()
+            continue
+        m2 = _L2_RE.match(line)
+        if m2:
+            code = m2.group(1)
+            name = m2.group(2).strip()
+            if not current_l1_code:
+                raise ValueError(f"L2 row {code} appears before any L1 in {path}")
+            out[code] = L2(code=code, name=name, l1_code=current_l1_code, l1_name=current_l1_name)
+    return out
+
+
+def lookup(taxonomy: dict[str, L2], l2_code: str) -> L2 | None:
+    """Return the L2 entry, or None if the YJ prefix isn't in the taxonomy."""
+    return taxonomy.get(l2_code)
+
+
+if __name__ == "__main__":
+    t = load_taxonomy()
+    print(f"Loaded {len(t)} L2 categories")
+    for code in ("111", "214", "421", "999"):
+        v = t.get(code)
+        print(f"  {code} → {v}")

skills/developing/rag-retrieve-disabled/.claude-plugin/plugin.json

@@ -0,0 +1,4 @@
+{
+  "name": "rag-retrieve-disabled",
+  "description": "rag_retrieve, table_rag_retrieve and local file retrieval are disabled."
+}

skills/developing/rag-retrieve-disabled/README.md

@@ -0,0 +1 @@
+# rag-retrieve-disabled

skills/linggan/agnes-image/SKILL.md

@@ -0,0 +1,254 @@
+---
+name: agnes-image
+description: 使用 Agnes AI（OpenAI 兼容网关）生成与理解图片、生成视频。支持文生图、图生图（保持角色一致性）、贴纸/卡通形象生成、白底转透明 PNG、多模态图片理解（描述/分析/问答/OCR），以及文生视频、图生视频、多图视频、关键帧动画。
+category: Creative Generation
+---
+
+# Agnes Image
+
+本 Skill 封装了 Agnes AI 的图像生成能力（模型 `agnes-image-2.1-flash`），接口兼容 OpenAI 风格，支持文生图、图生图，并内置「白底转透明」后处理，适合批量生成游戏/应用所需的角色、图标、贴纸等素材。
+
+## 前置条件
+
+设置 API Key（二选一）：
+
+```bash
+export AGNES_API_KEY="sk-xxxxxxxx"
+```
+
+或在每次调用时通过 `--api-key` 传入。
+
+## 使用方法
+
+### 文生图
+
+生成单张图片，输出可访问的图片链接：
+
+```bash
+python {baseDir}/scripts/generate_image.py --prompt "一只圆润萌系的小火龙，大眼睛，贴纸风格"
+```
+
+指定尺寸：
+
+```bash
+python {baseDir}/scripts/generate_image.py --prompt "壮丽的山川日出" --size "1024x768"
+```
+
+### 图生图（保持一致性）
+
+提供参考图 URL 或 `data:image` Base64，让新图沿用参考图的角色/构图：
+
+```bash
+python {baseDir}/scripts/generate_image.py \
+  --prompt "把这只小龙变成展翅的成年形态，保持配色和脸部不变" \
+  --image "https://example.com/baby-dragon.png"
+```
+
+可多次指定 `--image` 传入多张参考图。
+
+### 下载保存到本地
+
+加 `--save`，脚本会下载图片并保存，输出 `SAVED:<path>`：
+
+```bash
+python {baseDir}/scripts/generate_image.py --prompt "一只小猫贴纸" --save ./outputs/cat.png
+```
+
+### 白底转透明 PNG
+
+配合 `--save --transparent`，自动把纯白背景抠成透明（保留主体内部白色，如白描边、白肚皮）：
+
+```bash
+python {baseDir}/scripts/generate_image.py \
+  --prompt "一只圆润萌系企鹅，白色背景，贴纸风格" \
+  --save ./outputs/penguin.png --transparent
+```
+
+边缘若有白色残留，可调高容差 `--thresh`（默认 60）。
+
+## 参数说明
+
+- `--prompt`: (必选) 图像生成的文本描述，支持中英文。
+- `--model`: (可选) 模型 ID，默认 `agnes-image-2.1-flash`。
+- `--size`: (可选) 图像尺寸，如 `1024x1024`、`1024x768`，默认 `1024x1024`。
+- `--image`: (可选) 参考图 URL 或 `data:image/...;base64,...`，用于图生图，可多次指定。
+- `--api-key`: (可选) Agnes API Key，未提供时读取 `AGNES_API_KEY` 环境变量。
+- `--save`: (可选) 下载并保存到指定本地路径。
+- `--transparent`: (可选) 配合 `--save`，把纯白背景转为透明 PNG（需 Pillow）。
+- `--thresh`: (可选) 透明化时的白色容差，默认 60，值越大清除越彻底。
+
+## 工作流
+
+1. 调用 `generate_image.py` 脚本。
+2. 脚本始终输出以 `MEDIA_URL: ` 开头的图片链接，用 Markdown 展示：`![image](URL)`。
+3. 加了 `--save` 时，会额外下载到本地并输出以 `SAVED: ` 开头的文件路径（`MEDIA_URL` 仍会一并输出）。
+4. 生成的图片链接来自 Agnes 平台输出存储，可直接公网访问。
+
+> 注：`--save --transparent` 时，`MEDIA_URL` 指向 Agnes 返回的原始白底图，本地 `SAVED` 文件才是抠好的透明 PNG。
+
+## 批量生成小贴士
+
+生成系列素材（如角色的多个进化阶段）时，建议：
+
+- 在每个 prompt 中固定统一的「风格描述」（如 `chibi kawaii sticker, big eyes, white background`），保证视觉一致。
+- 需要严格保持同一角色时，用 `--image` 把上一阶段的图当参考做图生图。
+- 透明素材直接用 `--save --transparent`，省去手动抠图。
+
+## 注意事项
+
+- 接口兼容 OpenAI 风格，Base URL 为 `https://apihub.agnes-ai.com/v1`。
+- API Key 属于敏感信息，请勿提交到公开仓库或硬编码到代码中。
+- 图生图时，确保参考图 URL 可公开访问。
+- 透明化基于「边缘漫水填充」，仅对纯色（白）背景效果最佳；复杂背景请勿使用 `--transparent`。
+
+---
+
+## 图片理解（多模态）
+
+使用 `understand_image.py` 让模型（默认 `agnes-2.0-flash`）基于图片进行描述、分析、问答或信息提取。支持公网图片 URL，也支持本地图片文件（自动转 Base64）。
+
+### 理解图片 URL
+
+```bash
+python {baseDir}/scripts/understand_image.py \
+  --prompt "用中文描述这张图片的内容和风格" \
+  --image "https://example.com/image.jpg"
+```
+
+### 理解本地图片
+
+```bash
+python {baseDir}/scripts/understand_image.py \
+  --prompt "这是什么动物？什么颜色？" \
+  --image-file ./outputs/fox.png
+```
+
+### 多图对比 / 信息提取
+
+可多次指定 `--image` 或 `--image-file` 传入多张图片：
+
+```bash
+python {baseDir}/scripts/understand_image.py \
+  --prompt "比较这两张图的差异" \
+  --image-file ./a.png --image-file ./b.png
+```
+
+### 带系统提示
+
+```bash
+python {baseDir}/scripts/understand_image.py \
+  --prompt "提取图中所有文字" \
+  --image "https://example.com/poster.jpg" \
+  --system "你是专业的 OCR 助手，只输出图中文字"
+```
+
+### 图片理解参数说明
+
+- `--prompt`: (必选) 对图片的问题或指令。
+- `--image`: (可选) 图片公网 URL 或 `data:image` Base64，可多次指定。
+- `--image-file`: (可选) 本地图片文件路径，自动转 Base64，可多次指定。
+- `--system`: (可选) 系统提示（system 消息）。
+- `--model`: (可选) 模型 ID，默认 `agnes-2.0-flash`。
+- `--temperature`: (可选) 采样温度，越低越确定。
+- `--max-tokens`: (可选) 最多生成的 token 数。
+- `--api-key`: (可选) Agnes API Key，未提供时读取 `AGNES_API_KEY` 环境变量。
+
+### 图片理解工作流
+
+1. 调用 `understand_image.py` 脚本。
+2. 脚本直接输出模型的文本回答（无前缀），可直接读取使用。
+3. 不传任何图片时，会退化为普通文本对话。
+
+---
+
+## 视频生成（异步）
+
+使用 `generate_video.py` 生成视频（模型 `agnes-video-v2.0`）。视频生成是异步任务，脚本会自动创建任务并轮询，完成后输出视频链接。生成通常需要 1-3 分钟。
+
+> ⚠️ **重要：视频生成用到的所有参考图（图生视频 / 多图 / 关键帧）必须是「可公网访问的图片 URL」**（`http://` 或 `https://`），**不支持本地文件路径，也不支持 Base64**。
+> 如果你手上只有本地图片，请先：
+> 1. 用 `generate_image.py` 生成图片，它返回的 `MEDIA_URL` 就是可直接使用的公网 URL；或
+> 2. 把本地图片上传到图床 / 对象存储（如 S3、R2）后，使用其公网 URL。
+>
+> 脚本会校验 `--image` 是否为合法 URL，传入本地路径会直接报错并给出提示。
+
+### 文生视频
+
+```bash
+python {baseDir}/scripts/generate_video.py \
+  --prompt "A cinematic shot of a cat walking on the beach at sunset, warm golden lighting" \
+  --duration 5
+```
+
+### 图生视频（让单张图动起来）
+
+```bash
+python {baseDir}/scripts/generate_video.py \
+  --prompt "The character breathes gently, hair moving in the wind, keep face consistent" \
+  --image "https://example.com/character.png" --duration 5
+```
+
+### 多图视频（在多张图之间过渡）
+
+```bash
+python {baseDir}/scripts/generate_video.py \
+  --prompt "Smooth transformation between the two scenes, cinematic pacing" \
+  --image "https://example.com/a.png" --image "https://example.com/b.png"
+```
+
+### 关键帧动画
+
+```bash
+python {baseDir}/scripts/generate_video.py \
+  --prompt "Smooth transition between keyframes, maintain character identity" \
+  --image "https://example.com/k1.png" --image "https://example.com/k2.png" \
+  --keyframes
+```
+
+### 下载保存
+
+```bash
+python {baseDir}/scripts/generate_video.py --prompt "日出延时摄影" --duration 5 --save ./out.mp4
+```
+
+### 视频参数说明
+
+- `--prompt`: (必选) 视频内容的文本描述。
+- `--model`: (可选) 模型 ID，默认 `agnes-video-v2.0`。
+- `--image`: (可选) 参考图 URL；1 张=图生视频，2 张及以上=多图视频，可多次指定。
+- `--keyframes`: (可选) 关键帧动画模式（配合多张 `--image`）。
+- `--duration`: (可选) 目标时长(秒)，按帧率自动换算帧数（覆盖 `--num-frames`）。
+- `--num-frames`: (可选) 帧数，必须 ≤441 且满足 8n+1，默认 121（约 5 秒）；脚本会自动规整。
+- `--frame-rate`: (可选) 帧率 1-60，默认 24。
+- `--width` / `--height`: (可选) 分辨率，默认 1152×768；服务端会标准化到最接近的档位。
+- `--negative-prompt`: (可选) 负向提示词。
+- `--seed`: (可选) 随机种子，用于结果复现。
+- `--save`: (可选) 下载视频并保存到本地。
+- `--poll-interval`: (可选) 轮询间隔秒数，默认 5。
+- `--max-wait`: (可选) 最大等待秒数，默认 600。
+- `--api-key`: (可选) Agnes API Key，未提供时读取 `AGNES_API_KEY` 环境变量。
+
+### 常用时长参数
+
+| 目标时长 | 推荐参数 |
+|---------|---------|
+| 约 3 秒 | `--num-frames 81 --frame-rate 24` |
+| 约 5 秒 | `--num-frames 121 --frame-rate 24` |
+| 约 10 秒 | `--num-frames 241 --frame-rate 24` |
+| 约 18 秒 | `--num-frames 441 --frame-rate 24` |
+
+> 也可以直接用 `--duration <秒数>` 让脚本自动换算（会规整到合法的 8n+1 帧数）。
+
+### 视频生成工作流
+
+1. 调用 `generate_video.py`，脚本自动创建任务（`POST /v1/videos`）。
+2. 用返回的 `video_id` 轮询查询（`GET /agnesapi?video_id=...`），进度打到 stderr。
+3. 任务 `completed` 后，stdout 输出以 `MEDIA_URL: ` 开头的视频链接。
+4. 加 `--save` 时额外下载并输出 `SAVED: ` 路径。
+
+### 视频注意事项
+
+- `num_frames` 必须 ≤441 且满足 8n+1（如 81、121、241、441），脚本会自动规整非法值。
+- 实际输出尺寸/时长以接口返回的 `size`、`seconds` 字段为准（服务端会标准化分辨率）。
+- **参考图必须是可公网访问的 URL（`http`/`https`），不支持本地文件或 Base64**；本地图片请先用 `generate_image.py` 拿到公网 URL，或上传图床后再传入。
+- 轮询期间对瞬时网络抖动有容错，会自动重试。

skills/linggan/agnes-image/scripts/generate_image.py

@@ -0,0 +1,186 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "requests>=2.31.0",
+#     "pillow>=10.0.0",
+# ]
+# ///
+"""使用 Agnes AI（OpenAI 兼容网关）生成图片。
+支持：文生图、图生图（参考图保持一致性）、URL/Base64 输出、下载保存、白底转透明。
+"""
+
+import argparse
+import io
+import json
+import os
+import sys
+import time
+import urllib.request
+
+import requests
+
+API_URL = "https://apihub.agnes-ai.com/v1/images/generations"
+DEFAULT_MODEL = "agnes-image-2.1-flash"
+
+
+def call_api(prompt, model, size, api_key, images=None, want_b64=False, retries=3):
+    """调用 Agnes 图像生成接口，返回解析后的 JSON。
+    对瞬时网络/SSL 抖动自动重试，重试间隔递增。
+    """
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}",
+    }
+    extra = {"response_format": "b64_json" if want_b64 else "url"}
+    if images:
+        # 图生图：参考图放在 extra_body.image，支持公网 URL 或 data:image Base64
+        extra["image"] = images
+    payload = {
+        "model": model,
+        "prompt": prompt,
+        "size": size,
+        "extra_body": extra,
+    }
+    # 文生图需要 Base64 输出时，按文档使用顶层 return_base64
+    if want_b64 and not images:
+        payload["return_base64"] = True
+
+    last_err = None
+    for attempt in range(1, retries + 1):
+        try:
+            resp = requests.post(API_URL, headers=headers, json=payload, timeout=180)
+            resp.raise_for_status()
+            return resp.json()
+        except (requests.exceptions.SSLError,
+                requests.exceptions.ConnectionError,
+                requests.exceptions.Timeout) as e:
+            # 瞬时网络抖动：重试
+            last_err = e
+            if attempt < retries:
+                time.sleep(attempt * 2)
+                continue
+            raise
+    raise last_err
+
+
+def fetch_bytes(url):
+    """下载 URL 内容为字节。"""
+    with urllib.request.urlopen(url, timeout=180) as r:
+        return r.read()
+
+
+def make_transparent(png_bytes, thresh=60, white_min=225):
+    """把纯白背景变透明（保留内部白色）。
+    从图像四边多个种子点做 flood fill，只清除与边缘连通的白色区域。
+    """
+    from PIL import Image, ImageDraw
+
+    img = Image.open(io.BytesIO(png_bytes)).convert("RGBA")
+    w, h = img.size
+    seeds = []
+    steps = 12
+    for i in range(steps + 1):
+        t = int(i * (w - 1) / steps)
+        seeds += [(t, 0), (t, h - 1)]
+        s = int(i * (h - 1) / steps)
+        seeds += [(0, s), (w - 1, s)]
+    for sx, sy in seeds:
+        r, g, b, a = img.getpixel((sx, sy))
+        if a > 0 and r >= white_min and g >= white_min and b >= white_min:
+            ImageDraw.floodfill(img, (sx, sy), (255, 255, 255, 0), thresh=thresh)
+    out = io.BytesIO()
+    img.save(out, "PNG")
+    return out.getvalue()
+
+
+def main():
+    parser = argparse.ArgumentParser(description="使用 Agnes AI 生成图片。")
+    parser.add_argument("--prompt", required=True, help="图像生成的文本描述（必选）")
+    parser.add_argument("--model", default=DEFAULT_MODEL, help=f"模型 ID，默认 {DEFAULT_MODEL}")
+    parser.add_argument("--size", default="1024x1024", help="图像尺寸，如 1024x1024、1024x768")
+    parser.add_argument("--image", action="append", default=None,
+                        help="参考图 URL 或 data:image Base64（图生图，可多次指定）")
+    parser.add_argument("--api-key", help="Agnes API Key（也可用 AGNES_API_KEY 环境变量）")
+    parser.add_argument("--save", help="下载并保存到本地路径，输出 SAVED:<path>")
+    parser.add_argument("--transparent", action="store_true",
+                        help="配合 --save：把纯白背景转为透明 PNG")
+    parser.add_argument("--thresh", type=int, default=60, help="透明化白色容差，默认 60")
+
+    args = parser.parse_args()
+
+    api_key = args.api_key or os.environ.get("AGNES_API_KEY")
+    if not api_key:
+        print("ERROR: 缺少 API Key，请用 --api-key 或设置 AGNES_API_KEY 环境变量。")
+        sys.exit(1)
+
+    # 需要保存且要透明时，直接请求 URL 再下载处理（Pillow 处理本地字节）
+    want_b64 = False
+    try:
+        result = call_api(
+            prompt=args.prompt,
+            model=args.model,
+            size=args.size,
+            api_key=api_key,
+            images=args.image,
+            want_b64=want_b64,
+        )
+    except requests.exceptions.RequestException as e:
+        print(f"ERROR: API 请求失败: {e}")
+        if getattr(e, "response", None) is not None:
+            print(f"Response body: {e.response.text[:500]}")
+        sys.exit(1)
+
+    data = result.get("data") or []
+    if not data:
+        print(f"ERROR: 返回中无图像数据。完整响应: {json.dumps(result)[:500]}")
+        sys.exit(1)
+
+    item = data[0]
+    url = item.get("url")
+    b64 = item.get("b64_json")
+
+    if not args.save:
+        # 仅输出链接（对齐 seedream 约定）
+        if url:
+            print(f"MEDIA_URL: {url}")
+        elif b64:
+            print("ERROR: 收到 Base64 但未指定 --save，无法直接展示。请加 --save 保存。")
+            sys.exit(1)
+        else:
+            print(f"ERROR: 无 url 也无 b64_json。响应: {json.dumps(result)[:500]}")
+            sys.exit(1)
+        return
+
+    # 保存到本地
+    try:
+        if b64:
+            import base64
+            img_bytes = base64.b64decode(b64)
+        elif url:
+            img_bytes = fetch_bytes(url)
+        else:
+            print("ERROR: 无可保存的图像数据。")
+            sys.exit(1)
+
+        if args.transparent:
+            try:
+                img_bytes = make_transparent(img_bytes, thresh=args.thresh)
+            except ImportError:
+                print("ERROR: --transparent 需要 Pillow，请先 `pip install pillow`。")
+                sys.exit(1)
+
+        os.makedirs(os.path.dirname(os.path.abspath(args.save)), exist_ok=True)
+        with open(args.save, "wb") as f:
+            f.write(img_bytes)
+        # 即使保存到本地，也输出原始图片链接（便于直接引用/展示）
+        if url:
+            print(f"MEDIA_URL: {url}")
+        print(f"SAVED: {args.save}")
+    except Exception as e:
+        print(f"ERROR: 保存失败: {e}")
+        sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

skills/linggan/agnes-image/scripts/generate_video.py

@@ -0,0 +1,206 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "requests>=2.31.0",
+# ]
+# ///
+"""使用 Agnes AI 生成视频（异步任务：创建 -> 轮询 -> 取结果）。
+支持文生视频、图生视频、多图视频、关键帧动画。
+"""
+
+import argparse
+import json
+import os
+import shutil
+import sys
+import time
+import urllib.parse
+import urllib.request
+
+import requests
+
+CREATE_URL = "https://apihub.agnes-ai.com/v1/videos"
+QUERY_URL = "https://apihub.agnes-ai.com/agnesapi"
+DEFAULT_MODEL = "agnes-video-v2.0"
+
+
+def log(msg):
+    """进度信息打到 stderr，保持 stdout 只输出最终结果。"""
+    print(msg, file=sys.stderr, flush=True)
+
+
+def normalize_frames(nf):
+    """num_frames 必须 <=441 且满足 8n+1，规整到最接近的合法值。"""
+    nf = max(9, min(441, int(nf)))
+    n = round((nf - 1) / 8)
+    nf = 8 * n + 1
+    return max(9, min(441, nf))
+
+
+def create_task(payload, api_key, retries=3):
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}",
+    }
+    last_err = None
+    for attempt in range(1, retries + 1):
+        try:
+            resp = requests.post(CREATE_URL, headers=headers, json=payload, timeout=120)
+            resp.raise_for_status()
+            return resp.json()
+        except (requests.exceptions.SSLError,
+                requests.exceptions.ConnectionError,
+                requests.exceptions.Timeout) as e:
+            last_err = e
+            if attempt < retries:
+                time.sleep(attempt * 2)
+                continue
+            raise
+    raise last_err
+
+
+def query_result(video_id, api_key, model=None):
+    """用 video_id 查询任务结果（推荐方式）。"""
+    params = f"?video_id={urllib.parse.quote(video_id)}"
+    if model:
+        params += f"&model_name={urllib.parse.quote(model)}"
+    req = urllib.request.Request(
+        QUERY_URL + params,
+        headers={"Authorization": f"Bearer {api_key}"},
+    )
+    with urllib.request.urlopen(req, timeout=60) as r:
+        return json.loads(r.read())
+
+
+def download(url, path):
+    os.makedirs(os.path.dirname(os.path.abspath(path)), exist_ok=True)
+    with urllib.request.urlopen(url, timeout=300) as r, open(path, "wb") as f:
+        shutil.copyfileobj(r, f)
+
+
+def main():
+    parser = argparse.ArgumentParser(description="使用 Agnes AI 生成视频。")
+    parser.add_argument("--prompt", required=True, help="视频内容的文本描述（必选）")
+    parser.add_argument("--model", default=DEFAULT_MODEL, help=f"模型 ID，默认 {DEFAULT_MODEL}")
+    parser.add_argument("--image", action="append", default=None,
+                        help="参考图 URL（图生视频/多图/关键帧，可多次指定）")
+    parser.add_argument("--keyframes", action="store_true",
+                        help="关键帧动画模式（配合多张 --image 使用）")
+    parser.add_argument("--width", type=int, default=1152, help="视频宽度，默认 1152")
+    parser.add_argument("--height", type=int, default=768, help="视频高度，默认 768")
+    parser.add_argument("--num-frames", type=int, default=121,
+                        help="帧数，<=441 且满足 8n+1，默认 121（约 5 秒）")
+    parser.add_argument("--frame-rate", type=float, default=24, help="帧率 1-60，默认 24")
+    parser.add_argument("--duration", type=float,
+                        help="目标时长(秒)，会按帧率换算 num_frames（覆盖 --num-frames）")
+    parser.add_argument("--negative-prompt", help="负向提示词，描述要避免的内容")
+    parser.add_argument("--seed", type=int, help="随机种子，用于结果复现")
+    parser.add_argument("--api-key", help="Agnes API Key（也可用 AGNES_API_KEY 环境变量）")
+    parser.add_argument("--save", help="下载视频并保存到本地路径")
+    parser.add_argument("--poll-interval", type=float, default=5, help="轮询间隔秒数，默认 5")
+    parser.add_argument("--max-wait", type=float, default=600, help="最大等待秒数，默认 600")
+
+    args = parser.parse_args()
+
+    api_key = args.api_key or os.environ.get("AGNES_API_KEY")
+    if not api_key:
+        print("ERROR: 缺少 API Key，请用 --api-key 或设置 AGNES_API_KEY 环境变量。")
+        sys.exit(1)
+
+    # 计算帧数
+    if args.duration:
+        num_frames = normalize_frames(args.duration * args.frame_rate)
+    else:
+        num_frames = normalize_frames(args.num_frames)
+
+    # 组装请求体
+    payload = {
+        "model": args.model,
+        "prompt": args.prompt,
+        "width": args.width,
+        "height": args.height,
+        "num_frames": num_frames,
+        "frame_rate": args.frame_rate,
+    }
+    if args.negative_prompt:
+        payload["negative_prompt"] = args.negative_prompt
+    if args.seed is not None:
+        payload["seed"] = args.seed
+
+    images = args.image or []
+    # 视频接口的参考图只支持可公网访问的 URL（不支持本地文件 / Base64）
+    for u in images:
+        if not (u.startswith("http://") or u.startswith("https://")):
+            print("ERROR: 视频生成的参考图必须是可公网访问的图片 URL（http/https），"
+                  "不支持本地文件路径或 Base64。")
+            print(f"  问题输入: {u}")
+            print("  建议：先用 generate_image.py 生成图片拿到其公网 URL，"
+                  "或把本地图片上传到图床/对象存储后再用其 URL 传入。")
+            sys.exit(1)
+
+    if args.keyframes:
+        # 关键帧动画：extra_body.image + mode=keyframes
+        payload["extra_body"] = {"image": images, "mode": "keyframes"}
+    elif len(images) == 1:
+        # 图生视频：顶层 image
+        payload["image"] = images[0]
+    elif len(images) >= 2:
+        # 多图视频：extra_body.image
+        payload["extra_body"] = {"image": images}
+
+    # 1) 创建任务
+    log(f"创建视频任务（{num_frames} 帧 @ {args.frame_rate}fps ≈ {num_frames/args.frame_rate:.1f}s）...")
+    try:
+        task = create_task(payload, api_key)
+    except requests.exceptions.RequestException as e:
+        print(f"ERROR: 创建任务失败: {e}")
+        if getattr(e, "response", None) is not None:
+            print(f"Response body: {e.response.text[:500]}")
+        sys.exit(1)
+
+    video_id = task.get("video_id")
+    task_id = task.get("task_id") or task.get("id")
+    if not video_id and not task_id:
+        print(f"ERROR: 创建任务响应缺少 video_id/task_id: {json.dumps(task)[:500]}")
+        sys.exit(1)
+    log(f"任务已创建 video_id={video_id} task_id={task_id} status={task.get('status')}")
+
+    # 2) 轮询结果
+    start = time.time()
+    video_url = None
+    while time.time() - start < args.max_wait:
+        time.sleep(args.poll_interval)
+        try:
+            data = query_result(video_id or task_id, api_key, model=args.model)
+        except Exception as e:
+            log(f"  查询出错（将重试）: {e}")
+            continue
+        status = data.get("status")
+        progress = data.get("progress", 0)
+        log(f"  状态={status} 进度={progress}%")
+        if status == "completed":
+            video_url = data.get("remixed_from_video_id")  # 文档：该字段为最终视频 URL
+            break
+        if status == "failed":
+            print(f"ERROR: 视频生成失败: {data.get('error')}")
+            sys.exit(1)
+
+    if not video_url:
+        print(f"ERROR: 等待超时（{args.max_wait}s）或未返回视频 URL。")
+        sys.exit(1)
+
+    # 3) 输出结果
+    print(f"MEDIA_URL: {video_url}")
+    if args.save:
+        try:
+            log("下载视频中 ...")
+            download(video_url, args.save)
+            print(f"SAVED: {args.save}")
+        except Exception as e:
+            print(f"ERROR: 下载保存失败: {e}")
+            sys.exit(1)
+
+
+if __name__ == "__main__":
+    main()

skills/linggan/agnes-image/scripts/understand_image.py

@@ -0,0 +1,126 @@
+#!/usr/bin/env python3
+# /// script
+# requires-python = ">=3.10"
+# dependencies = [
+#     "requests>=2.31.0",
+# ]
+# ///
+"""使用 Agnes AI 的多模态模型理解图片。
+传入图片（公网 URL 或本地文件）+ 文本问题，模型基于图片进行描述、分析、问答或信息提取。
+"""
+
+import argparse
+import base64
+import json
+import mimetypes
+import os
+import sys
+import time
+
+import requests
+
+API_URL = "https://apihub.agnes-ai.com/v1/chat/completions"
+DEFAULT_MODEL = "agnes-2.0-flash"
+
+
+def file_to_data_uri(path):
+    """把本地图片文件转成 data:image Base64。"""
+    mime = mimetypes.guess_type(path)[0] or "image/png"
+    with open(path, "rb") as f:
+        b64 = base64.b64encode(f.read()).decode()
+    return f"data:{mime};base64,{b64}"
+
+
+def call_api(messages, model, api_key, temperature=None, max_tokens=None, retries=3):
+    """调用 Agnes chat/completions，返回解析后的 JSON。瞬时网络抖动自动重试。"""
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {api_key}",
+    }
+    payload = {"model": model, "messages": messages}
+    if temperature is not None:
+        payload["temperature"] = temperature
+    if max_tokens is not None:
+        payload["max_tokens"] = max_tokens
+
+    last_err = None
+    for attempt in range(1, retries + 1):
+        try:
+            resp = requests.post(API_URL, headers=headers, json=payload, timeout=180)
+            resp.raise_for_status()
+            return resp.json()
+        except (requests.exceptions.SSLError,
+                requests.exceptions.ConnectionError,
+                requests.exceptions.Timeout) as e:
+            last_err = e
+            if attempt < retries:
+                time.sleep(attempt * 2)
+                continue
+            raise
+    raise last_err
+
+
+def main():
+    parser = argparse.ArgumentParser(description="使用 Agnes AI 理解/分析图片。")
+    parser.add_argument("--prompt", required=True, help="对图片的问题或指令（必选）")
+    parser.add_argument("--image", action="append", default=None,
+                        help="图片公网 URL 或 data:image Base64（可多次指定）")
+    parser.add_argument("--image-file", action="append", default=None,
+                        help="本地图片文件路径，自动转 Base64（可多次指定）")
+    parser.add_argument("--system", help="可选的系统提示（system 消息）")
+    parser.add_argument("--model", default=DEFAULT_MODEL, help=f"模型 ID，默认 {DEFAULT_MODEL}")
+    parser.add_argument("--temperature", type=float, help="采样温度，0~1，越低越确定")
+    parser.add_argument("--max-tokens", type=int, help="最多生成的 token 数")
+    parser.add_argument("--api-key", help="Agnes API Key（也可用 AGNES_API_KEY 环境变量）")
+
+    args = parser.parse_args()
+
+    api_key = args.api_key or os.environ.get("AGNES_API_KEY")
+    if not api_key:
+        print("ERROR: 缺少 API Key，请用 --api-key 或设置 AGNES_API_KEY 环境变量。")
+        sys.exit(1)
+
+    # 收集所有图片输入
+    image_urls = list(args.image or [])
+    for p in (args.image_file or []):
+        if not os.path.isfile(p):
+            print(f"ERROR: 本地图片不存在: {p}")
+            sys.exit(1)
+        image_urls.append(file_to_data_uri(p))
+
+    # 组装多模态 content：文本 + 若干图片
+    content = [{"type": "text", "text": args.prompt}]
+    for url in image_urls:
+        content.append({"type": "image_url", "image_url": {"url": url}})
+
+    messages = []
+    if args.system:
+        messages.append({"role": "system", "content": args.system})
+    # 没有图片时退化为纯文本，content 用字符串更稳妥
+    messages.append({"role": "user", "content": content if image_urls else args.prompt})
+
+    try:
+        result = call_api(
+            messages=messages,
+            model=args.model,
+            api_key=api_key,
+            temperature=args.temperature,
+            max_tokens=args.max_tokens,
+        )
+    except requests.exceptions.RequestException as e:
+        print(f"ERROR: API 请求失败: {e}")
+        if getattr(e, "response", None) is not None:
+            print(f"Response body: {e.response.text[:500]}")
+        sys.exit(1)
+
+    try:
+        answer = result["choices"][0]["message"]["content"]
+    except (KeyError, IndexError, TypeError):
+        print(f"ERROR: 无法解析响应。完整响应: {json.dumps(result, ensure_ascii=False)[:500]}")
+        sys.exit(1)
+
+    print(answer)
+
+
+if __name__ == "__main__":
+    main()

utils/log_util/logger.py

@@ -51,6 +51,13 @@ class Formatter(logging.Formatter):
                 record.trace_id = getattr(g, "trace_id")
             except LookupError:
                 record.trace_id = "N/A"
+        # Handle subagent - default to "main" for the orchestrator / no-context paths.
+        # Catch KeyError too: GlobalContext.__getattr__ raises KeyError on a missing key.
+        if not hasattr(record, "subagent"):
+            try:
+                record.subagent = getattr(g, "subagent")
+            except (KeyError, LookupError):
+                record.subagent = "main"
         # Handle user_id
         # if not hasattr(record, "user_id"):
         #     record.user_id = getattr(g, "user_id")
@@ -65,7 +72,7 @@ class Formatter(logging.Formatter):
 def init_logger_once(name,level):
     logger = logging.getLogger(name)
     logger.setLevel(level=level)
-    formatter = Formatter("%(timestamp)s | %(levelname)-5s | %(trace_id)s | %(name)s:%(funcName)s:%(lineno)s - %(message)s", datefmt='%Y-%m-%d %H:%M:%S.%f')
+    formatter = Formatter("%(timestamp)s | %(levelname)-5s | %(trace_id)s | %(subagent)s | %(name)s:%(funcName)s:%(lineno)s - %(message)s", datefmt='%Y-%m-%d %H:%M:%S.%f')
     handler = logging.StreamHandler()
     handler.setFormatter(formatter)
     logger.addHandler(handler)