feat: builder 简历生成 + 轻度优化 + 简历导入交付副本

自内部仓库剥离深度优化与 RAG 知识库后的交付版本：
- Builder 对话式简历生成（FSM + 意图路由 LLM 兜底增强）
- 条目级轻度优化：事实覆盖门禁 + STAR/bullet 修复链，功能/简介/成果与技术栈同级保护
- 简历导入：DOCX/PDF 解析、结构归一、手机号脱敏
- PostgreSQL 运行时 + Alembic 迁移链

Co-Authored-By: Claude <noreply@anthropic.com>

backend/app/skill_suggester.py

@@ -0,0 +1,106 @@
+"""Injectable, grounded skill suggestions for the enrichment skills card."""
+
+from __future__ import annotations
+
+from typing import Any, Protocol
+
+from pydantic import Field
+
+from .enrichment_modules import skill_suggestions
+from .llm_services import OpenAICompatibleStructuredClient, StrictSchema
+from .settings import Settings
+
+
+class SkillSuggester(Protocol):
+    """Suggest skills from the user's target role and already-entered resume facts."""
+
+    def suggest(self, profile: dict[str, Any]) -> list[str]: ...
+
+
+class RuleBasedSkillSuggester:
+    def suggest(self, profile: dict[str, Any]) -> list[str]:
+        return skill_suggestions(profile.get("target_position"), profile)
+
+
+class SkillSuggestionOutput(StrictSchema):
+    skills: list[str] = Field(max_length=8)
+
+
+class OpenAISkillSuggester:
+    def __init__(
+        self, completion: OpenAICompatibleStructuredClient, fallback: SkillSuggester
+    ) -> None:
+        self.completion = completion
+        self.fallback = fallback
+
+    def suggest(self, profile: dict[str, Any]) -> list[str]:
+        fallback = self.fallback.suggest(profile)
+        try:
+            output = self.completion.complete(
+                schema=SkillSuggestionOutput,
+                schema_name="skill_suggestions",
+                system_prompt=(
+                    "你是中文求职简历助手。根据目标岗位和用户已经填写的经历事实推荐技能标签。"
+                    "只输出适合技能卡的简短技能名称，不要写句子、等级、熟练度或虚构项目成果。"
+                    "可以补充目标岗位常见但用户尚未填写的技能，作为待学习/待确认建议；"
+                    "不要把公司、学校、课程或奖项名称当作技能。"
+                ),
+                payload={
+                    "target_position": profile.get("target_position"),
+                    "facts": _skill_facts(profile),
+                    "existing_skills": (profile.get("tags") or {}).get("skills") or [],
+                },
+            )
+        except Exception:
+            return fallback
+        return _merge_suggestions(output.skills, fallback, profile)
+
+
+def build_skill_suggester(
+    settings: Settings, client: Any | None = None
+) -> SkillSuggester:
+    rules = RuleBasedSkillSuggester()
+    if not settings.use_openai:
+        return rules
+    return OpenAISkillSuggester(OpenAICompatibleStructuredClient(settings, client), rules)
+
+
+def _skill_facts(profile: dict[str, Any]) -> list[dict[str, Any]]:
+    facts: list[dict[str, Any]] = []
+    entries: list[Any] = [profile.get("anchor")]
+    entries.extend(profile.get("experiences") or [])
+    for records in (profile.get("records") or {}).values():
+        entries.extend(records or [])
+    for entry in entries:
+        if not isinstance(entry, dict):
+            continue
+        description = str(entry.get("description") or "").strip()
+        highlights = [str(item).strip() for item in entry.get("highlights") or [] if str(item).strip()]
+        if description or highlights:
+            facts.append(
+                {
+                    "record_type": entry.get("record_type"),
+                    "description": description or None,
+                    "highlights": highlights,
+                }
+            )
+    return facts
+
+
+def _merge_suggestions(
+    proposed: list[str], fallback: list[str], profile: dict[str, Any]
+) -> list[str]:
+    existing = {
+        str(skill).strip().casefold()
+        for skill in ((profile.get("tags") or {}).get("skills") or [])
+        if str(skill).strip()
+    }
+    result: list[str] = []
+    seen: set[str] = set()
+    for skill in [*proposed, *fallback]:
+        normalized = str(skill).strip()
+        key = normalized.casefold()
+        if normalized and len(normalized) <= 32 and key not in existing and key not in seen:
+            result.append(normalized)
+            seen.add(key)
+    return result[:8]