feat: 将项目从nano重命名为beaver并更新相关配置

- 将所有环境变量前缀从NANO_改为BEAVER_
- 更新README.md文档内容，包括项目介绍、组件说明和快速开始指南
- 修改.gitignore文件，添加auth-portal运行时路径排除规则
- 更新app-instance镜像标签从nano/app-instance改为beaver/app-instance
- 增强技能安全检查器，支持工具前缀白名单功能
- 添加技能草稿重新检查安全性API端点
- 扩展证据选择器，收集工具调用名称用于技能学习
- 改进技能合成器，基于实际调用的工具生成工具提示
- 优化路由超时处理机制，增加重试逻辑
- 更新后端架构文档，添加可视化入口和基础概念说明
- 实现在WebSocket消息中传递工具迭代次数信息

app-instance/backend/beaver/skills/learning/synthesizer.py

@@ -65,19 +65,29 @@ class SkillDraftSynthesizer:
         )
         payload = self._parse_payload(response.content or "")
         if payload:
-            return payload
+            return self._normalize_payload(payload, evidence_packet)
         return self._fallback_payload(candidate, evidence_packet, action)
 
     @staticmethod
     def _build_prompt(candidate: SkillLearningCandidate, evidence_packet: EvidencePacket, action: str) -> str:
+        tool_names = _coerce_string_list(evidence_packet.metadata.get("tool_names"))
+        tool_section = ", ".join(tool_names) if tool_names else "none observed"
+        selected_tool_names = _coerce_string_list(evidence_packet.metadata.get("selected_tool_names"))
+        selected_tool_section = ", ".join(selected_tool_names) if selected_tool_names else "none recorded"
         return (
             f"Action: {action}\n"
             f"Candidate kind: {candidate.kind}\n"
             f"Reason: {candidate.reason}\n"
             f"Related skills: {candidate.related_skill_names}\n"
+            f"Called tool names: {tool_section}\n"
+            f"Run-selected tool names: {selected_tool_section}\n"
             f"Task summaries:\n- " + "\n- ".join(evidence_packet.task_summaries)
             + "\n\nSession excerpts:\n" + "\n\n".join(evidence_packet.session_excerpts)
-            + "\n\nReturn JSON only."
+            + "\n\nReturn JSON only. The frontmatter object must include:"
+            + "\n- description: a concise skill description"
+            + "\n- tools: an explicit JSON array of exact tool names this skill needs. "
+            + "Prefer called tool names when the workflow depends on them; use run-selected tool names only when clearly required. "
+            + "Use [] only when no tool is required."
         )
 
     @staticmethod
@@ -103,6 +113,19 @@ class SkillDraftSynthesizer:
             "change_reason": str(payload.get("change_reason") or ""),
         }
 
+    @staticmethod
+    def _normalize_payload(payload: dict[str, Any], evidence_packet: EvidencePacket) -> dict[str, Any]:
+        frontmatter = dict(payload.get("frontmatter") or {})
+        tool_hints = _coerce_string_list(frontmatter.get("tools"))
+        if not tool_hints:
+            tool_hints = _coerce_string_list(evidence_packet.metadata.get("tool_names"))
+        frontmatter["tools"] = tool_hints
+        return {
+            "frontmatter": frontmatter,
+            "content": str(payload.get("content") or "").strip(),
+            "change_reason": str(payload.get("change_reason") or ""),
+        }
+
     @staticmethod
     def _fallback_payload(candidate: SkillLearningCandidate, evidence_packet: EvidencePacket, action: str) -> dict[str, Any]:
         related = candidate.related_skill_names[0] if candidate.related_skill_names else "generated-skill"
@@ -111,8 +134,25 @@ class SkillDraftSynthesizer:
         return {
             "frontmatter": {
                 "description": candidate.reason or f"Auto-generated {action} draft for {title}.",
-                "tools": [],
+                "tools": _coerce_string_list(evidence_packet.metadata.get("tool_names")),
             },
             "content": f"# {title}\n\n## Evidence\n\n{content}\n",
             "change_reason": candidate.reason or f"Fallback {action} synthesis.",
         }
+
+
+def _coerce_string_list(value: Any) -> list[str]:
+    raw_items: list[Any]
+    if isinstance(value, list):
+        raw_items = value
+    elif isinstance(value, str):
+        raw_items = value.split(",")
+    else:
+        raw_items = []
+
+    result: list[str] = []
+    for item in raw_items:
+        cleaned = str(item).strip()
+        if cleaned and cleaned not in result:
+            result.append(cleaned)
+    return result