v1.1.0: 实现核心业务逻辑(扫描/任务书/派发/状态机)
新增(scripts/ 分层完整实现):
- scripts/util/{constants,date_util,runtime_paths,logging_setup}.py
- scripts/db/{connection,seed_data,repository}.py(SQLite + 5 张表 + 50 份样例合同)
- scripts/service/{scanner,task_generator,dispatcher,html_renderer}.py
- scripts/cli/app.py(argparse 子命令)
- scripts/templates/task.html.j2(Jinja2 渲染,无 jinja2 时降级到 str.replace)
- examples/{contracts_sample,performance_sample}.csv(本地样例数据)
修改:
- scripts/main.py:argparse + jiangchang_skill_core 降级
- SKILL.md:version 1.0.1 → 1.1.0
- CHANGELOG.md:加 v1.1.0 条目
- .env.example:加 DB_PATH / HTML_OUTPUT_DIR / JIANGCHANG_DATA_DIR
本地验证(9 个 CLI 命令全部 exit code = 0):
- health / version / config-path / init-db:基础 4 命令
- scan-expiring-contracts --thresholds 90,60,30:42 份可续聘 → 红 10 / 橙 10 / 黄 10,生成 30 任务
- render-html:写 output/<task_id>.html(4.9 KB)
- generate-renewal-task:单份合同任务书生成
- mark-contract-renewed:状态机更新 + 关闭 open alerts
- dispatch-task-notice:按单位/月份派发 payload(dry-run 模式)
业务规则(AI 续聘建议本地版,规则顺序判断):
1. 师德 < 60 → non_renew
2. 近 3 年考核有不合格(< 60)→ observe
3. 全部 >= 80 且逐年上升 → renew
4. 其他 → observe
Co-Authored-By: Claude Code <noreply@anthropic.com>
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scripts/db/seed_data.py
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scripts/db/seed_data.py
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"""种子数据加载:从 examples/contracts_sample.csv 和 performance_sample.csv 读入。
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只在 contracts 表为空时执行,幂等。
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真实部署应替换为人事系统 API 调用。
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"""
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from __future__ import annotations
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import csv
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import sqlite3
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from pathlib import Path
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from util.logging_setup import get_skill_logger
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from util.runtime_paths import get_examples_dir
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logger = get_skill_logger()
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def _read_csv(path: Path) -> list[dict[str, str]]:
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with open(path, "r", encoding="utf-8", newline="") as f:
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return list(csv.DictReader(f))
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def load_seed_if_empty(conn: sqlite3.Connection) -> tuple[int, int]:
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"""如果 contracts 表为空,从 CSV 加载种子数据。返回 (合同数, 业绩数)。"""
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n = conn.execute("SELECT COUNT(*) AS n FROM contracts").fetchone()["n"]
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if n > 0:
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logger.info("seed_data: contracts 表已有 %d 条记录,跳过加载", n)
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return (n, 0)
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examples_dir = get_examples_dir()
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contracts_csv = examples_dir / "contracts_sample.csv"
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performance_csv = examples_dir / "performance_sample.csv"
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if not contracts_csv.exists() or not performance_csv.exists():
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logger.warning("seed_data: 样例 CSV 不存在 %s / %s", contracts_csv, performance_csv)
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return (0, 0)
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contracts = _read_csv(contracts_csv)
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performance = _read_csv(performance_csv)
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with conn:
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conn.executemany(
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"""
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INSERT OR REPLACE INTO contracts
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(contract_id, employee_id, teacher_name, unit_id, unit_name,
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position, contract_type, contract_start, contract_end,
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trial_period_end, status, last_material_id, renewed_at)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, NULL, NULL)
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""",
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[
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(
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c["contract_id"],
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c["employee_id"],
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c["teacher_name"],
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c["unit_id"],
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c["unit_name"],
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c["position"],
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c["contract_type"],
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c["contract_start"],
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c["contract_end"],
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c["trial_period_end"],
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c["status"],
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)
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for c in contracts
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],
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)
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conn.executemany(
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"""
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INSERT OR REPLACE INTO performance_records
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(record_id, contract_id, year, performance_score,
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moral_teaching_score, research_score, teaching_score)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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""",
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[
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(
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p["record_id"],
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p["contract_id"],
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int(p["year"]),
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float(p["performance_score"]),
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float(p["moral_teaching_score"]),
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float(p["research_score"]),
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float(p["teaching_score"]),
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)
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for p in performance
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],
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)
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logger.info("seed_data: 加载 %d 份合同 / %d 条业绩记录", len(contracts), len(performance))
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return (len(contracts), len(performance))
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