Market Daily Review

{
  "version": 1,
  "task": {
    "placeholder": "补充复盘口径或特别要求（可选）",
    "required": false
  },
  "fields": [
    {
      "key": "date",
      "label": "复盘日期",
      "type": "date",
      "help": "留空时使用最近已完成的 A 股交易日"
    },
    {
      "key": "focus",
      "label": "重点关注",
      "type": "text",
      "placeholder": "例如：北向持仓、半导体板块、龙虎榜"
    }
  ],
  "prompt_template": "{{#task}}任务与材料：\n{{task}}\n\n{{/task}}{{#attachments}}用户上传的材料（已放入工作区）：\n{{attachments}}\n\n{{/attachments}}执行 A 股收盘复盘。{{#date}}以 {{date}} 为目标日期。{{/date}}未指定日期时以最近已完成的 A 股交易日为目标日期。{{#focus}}重点关注 {{focus}}。{{/focus}}先核验交易日，再覆盖指数表现与估值、市场宽度、涨跌停情绪、行业与概念、龙虎榜、大宗交易、两融、北向持仓和风险提示，逐项标注数据接口与日期并说明滞后或缺失数据，输出中文报告。"
}

Market Daily Review

Use this skill to generate factual A-share after-close review reports. Prefer Pandadata as the data source, keep every statistic traceable to an interface and data date, and never invent missing figures.

Workflow

1. Determine the target date. If the user does not provide one, use the latest completed A-share trading day. Check get_last_trade_date and get_trade_cal; if the target date is closed, return a short "今日休市" note instead of a full report.
2. Load pandadata-api before making real API calls. Use its method index or search script to confirm parameters and fields; do not guess Pandadata signatures.
3. Collect data in this order:
   • Trading calendar and stock universe: get_last_trade_date, get_trade_cal, get_trade_list.
   • Index performance and valuation: get_index_daily, get_index_indicator.
   • Market breadth and sentiment: get_stock_daily or get_stock_rt_daily, plus get_stock_status_change.
   • Hot sectors and concepts: get_industry_constituents, get_concept_list, get_concept_constituents.
   • Funds and notable trades: get_lhb_list, get_lhb_detail, get_block_trade, get_margin, get_hsgt_hold.
4. Compute breadth and ranking metrics from raw rows: rising/falling counts, limit-up/limit-down counts, turnover leaders, industry/concept leaders, 龙虎榜 top net buy/sell names, block-trade discount/premium distribution, margin balance change, and northbound holding changes.
5. Generate Markdown using references/report-template.md. Save the report to reports/daily/YYYYMMDD.md unless the user gives another path.
6. Run scripts/validate_report.py <report-path> after writing the report. Fix missing sections, missing source notes, or missing data-date labels before presenting the result.

Pandadata Reference

Read references/pandadata-map.md when planning calls, selecting fields, or deciding how to degrade if a data interface is unavailable. The map is a routing aid only; the exact call contract must still come from pandadata-api.

Report Rules

• Write in Chinese unless the user requests another language.
• Use absolute dates such as 2026-06-11; avoid ambiguous "today" in the final report body.
• Mark T+1 datasets clearly. Margin financing, northbound holdings, and some exchange disclosures may lag the market date.
• State the limit-up/limit-down counting rules, including whether ST stocks and one-price boards are included.
• Keep the report factual: summarize structure, flows, and anomalies; do not give tomorrow's trading instructions or personalized investment advice.
• When a data call fails, keep the report useful by generating available sections and adding a concise missing-data note under "数据说明".

Automation

When the user asks for an automated daily review, create an after-close task for trading days only, preferably after 18:30 Asia/Shanghai so delayed datasets have time to settle. Make the task idempotent: if reports/daily/YYYYMMDD.md already exists, regenerate and overwrite it.