generated from kgod/ai-review-template
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>
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Claude
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"""创建后丰富模块规格表（PRD §10.2 优先级队列）。
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声明式表驱动：FSM 与路由层只读本表，不硬编码模块逻辑。
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from typing import Any
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from .models import JobType
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#: records 中合法的记录键
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RECORD_KEYS = (
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"education",
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"work_experience",
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"internship_experience",
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"project_experience",
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"campus_experience",
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"competition",
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)
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@dataclass(frozen=True, slots=True)
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class ModuleSpec:
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name: str # 队列元素唯一名
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kind: str # "record_chat" | "record_form" | "tags"
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record_type: str | None # records 目标键；None = 需用户先选类型或运行时决定
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multi: bool # 多段模块（确认后发 AddAnother）
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core_fields: tuple[str, ...] # 核心字段（缺一不进确认）
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optional_fields: tuple[str, ...]
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components: tuple[str, ...] # 须进 STAGE_COMPONENTS 白名单
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prompt: str # 模块开放式提问话术
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skippable: bool = True
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_CONFIRM = ("RecordFields", "ExperienceConfirmCard")
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_CONFIRM_MULTI = ("RecordFields", "ExperienceConfirmCard", "AddAnother")
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def _record(
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name: str,
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record_type: str | None,
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multi: bool,
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prompt: str,
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picker: bool = False,
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kind: str = "record_fields",
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) -> ModuleSpec:
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components = (("ChoiceChips",) if picker else ()) + (_CONFIRM_MULTI if multi else _CONFIRM)
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return ModuleSpec(
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name=name,
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kind=kind,
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record_type=record_type,
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multi=multi,
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core_fields=(),
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optional_fields=("description",),
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components=components,
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prompt=prompt,
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)
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ENRICHMENT_MODULES: dict[str, ModuleSpec] = {
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spec.name: spec
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for spec in (
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_record(
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"internship",
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"internship_experience",
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True,
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"补充一段实习经历，包括公司、职位和时间。",
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),
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_record(
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"more_work",
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"work_experience",
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True,
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"还有其他工作经历吗？填写公司、职位和时间。",
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),
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_record(
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"project",
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"project_experience",
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True,
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"填写一个你做过的项目，包括名称、你的角色和时间。",
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),
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_record(
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"education",
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"education",
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True,
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"补充一段教育经历，包括学校、专业、学历和时间。",
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),
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_record(
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"campus_experience",
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"campus_experience",
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True,
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"补充一段校园经历，包括组织、角色和时间。",
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),
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ModuleSpec(
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name="competition",
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kind="record_form",
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record_type="competition",
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multi=True,
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core_fields=("name", "award", "date"),
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optional_fields=("description",),
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components=("CompetitionFields", "ExperienceConfirmCard", "AddAnother"),
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prompt="有竞赛获奖经历吗？填写竞赛名称、奖项和获奖月份。",
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),
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ModuleSpec(
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name="skills",
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kind="tags",
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record_type=None,
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multi=False,
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core_fields=(),
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optional_fields=("skills",),
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components=("TagsInput",),
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prompt="列一下你的技能，逐个添加，可以留空。",
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),
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ModuleSpec(
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name="certificates",
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kind="tags",
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record_type=None,
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multi=False,
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core_fields=(),
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optional_fields=("certificates",),
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components=("TagsInput",),
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prompt="列一下你的证书，可以留空。",
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),
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)
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}
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ENRICHMENT_QUEUES: dict[JobType, tuple[str, ...]] = {
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JobType.CAMPUS: (
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"internship",
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"project",
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"competition",
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"skills",
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"certificates",
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),
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JobType.SOCIAL: (
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"more_work",
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"project",
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"education",
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"skills",
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"certificates",
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),
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JobType.INTERNSHIP: (
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"campus_experience",
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"project",
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"competition",
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"skills",
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"certificates",
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),
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}
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def module_by_name(name: str) -> ModuleSpec:
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return ENRICHMENT_MODULES[name]
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PICKER_OPTIONS: dict[str, tuple[tuple[str, str], ...]] = {}
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TAG_SEQUENCE = ("skills", "certificates")
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TAG_TITLES = {"skills": "你的技能", "certificates": "你的证书"}
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_DEFAULT_SKILLS = ("沟通协调", "问题解决", "团队协作")
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_SKILL_RULES = (
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(("前端", "frontend", "web"), ("HTML", "CSS", "JavaScript", "TypeScript", "Vue", "React", "Git")),
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(("java",), ("Java", "Spring Boot", "MySQL", "Redis", "Git")),
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(("后端", "backend"), ("Python", "Java", "MySQL", "Redis", "Docker", "Git")),
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(("数据", "算法", "ai", "人工智能"), ("Python", "SQL", "Pandas", "机器学习", "Git")),
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(("产品",), ("需求分析", "原型设计", "数据分析", "项目管理")),
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)
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_DESCRIPTION_SKILLS = (
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(("python",), "Python"),
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(("fastapi",), "FastAPI"),
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(("django",), "Django"),
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(("flask",), "Flask"),
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(("java",), "Java"),
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(("spring boot", "springboot", "spring"), "Spring Boot"),
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(("mysql",), "MySQL"),
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(("postgres", "postgresql"), "PostgreSQL"),
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(("redis",), "Redis"),
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(("docker",), "Docker"),
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(("kubernetes", "k8s"), "Kubernetes"),
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(("vue",), "Vue"),
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(("react",), "React"),
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(("typescript",), "TypeScript"),
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(("javascript",), "JavaScript"),
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(("sql",), "SQL"),
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(("pandas",), "Pandas"),
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(("机器学习", "machine learning"), "机器学习"),
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)
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def skill_suggestions(
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target_position: str | None, profile: dict[str, Any] | None = None
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) -> list[str]:
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"""Suggest skills from target role and facts already supplied by the user."""
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normalized = (target_position or "").strip().lower()
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base_suggestions: list[str] = []
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for keywords, rule_suggestions in _SKILL_RULES:
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if any(keyword in normalized for keyword in keywords):
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base_suggestions = list(rule_suggestions)
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break
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if not base_suggestions:
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base_suggestions = list(_DEFAULT_SKILLS)
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profile = profile or {}
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supplied = _profile_text(profile).lower()
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for aliases, skill in _DESCRIPTION_SKILLS:
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if any(alias in supplied for alias in aliases):
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base_suggestions.append(skill)
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existing = {
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str(skill).strip().casefold()
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for skill in ((profile.get("tags") or {}).get("skills") or [])
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if str(skill).strip()
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}
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result: list[str] = []
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seen: set[str] = set()
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for skill in base_suggestions:
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key = skill.casefold()
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if key not in seen and key not in existing:
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result.append(skill)
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seen.add(key)
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return result[:8]
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def _profile_text(profile: dict[str, Any]) -> str:
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"""Collect user-entered descriptions only; never infer skills from resume examples."""
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values: list[str] = []
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entries: list[Any] = [profile.get("anchor")]
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entries.extend(profile.get("experiences") or [])
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for records in (profile.get("records") or {}).values():
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entries.extend(records or [])
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for entry in entries:
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if not isinstance(entry, dict):
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continue
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description = entry.get("description")
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if description:
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values.append(str(description))
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values.extend(str(item) for item in (entry.get("highlights") or []) if item)
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return "\n".join(values)
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