- name
- banker-memo-md
- description
- Produce an investment-banker-grade research memo (analysis.md + data-provenance.md) from CN raw-data/ JSON snapshots. Use when the user asks for "投行 md" / "银行家级分析 md" / "banker memo markdown" on an A-share, H-share, or non-listed CN company that has raw-data/ populated by cn-client-investigation Phase 3.5. This is STEP 1 of 2 in the banker pipeline — pair with banker-slides-pptx for the deck.
Banker Memo (MD)
Step 1 of the banker pipeline: raw-data/ → analysis.md + data-provenance.md.
This skill is the prompt that drives the agent to write a banker-grade research memo. It does not generate slides or run gates — those belong to banker-slides-pptx and validate-delivery.py respectively.
Pipeline position
┌────────────────────┐ ┌──────────────────┐ ┌────────────────────┐
│ Phase 3.5 raw-data│ ──▶ │ banker-memo-md │ ──▶ │ banker-slides-pptx │
│ (cn-client-inv.) │ │ (THIS SKILL) │ │ (step 2) │
│ → raw-data/*.json │ │ → analysis.md │ │ → slides-outline │
│ │ │ → provenance.md │ │ → .pptx │
└────────────────────┘ └──────────────────┘ └────────────────────┘Why split from the deck skill
Earlier banker-memo bundled both MD + outline generation in one prompt. Problems:
- Outline was an afterthought — agent ran out of attention budget
- MD prompt constraints (8 sections, peer benchmarking, SOTP) got diluted by "also design 12 slides"
- Impossible to iterate on MD quality without re-running the slide outline
Splitting lets each prompt focus:
- This skill: pure research discipline — 8-section framework, data flags, 4C's credit view, specific [EST] tagging
banker-slides-pptx: pure visual design — structured layout schema the renderer can parse into real pptxgenjs tables/charts
Prompt template
Canonical prompt at references/banker_memo_md_prompt.md. Placeholders:
{ts_code},{name_cn},{industry}— target identifiers{raw_dir}— path toraw-data/holding MCP JSON snapshots{out_dir}— where to write analysis.md + data-provenance.md{file_list}— auto-discovered raw-data files{uscc}— unified social credit code from PrimeMatrix filename
Build the prompt via scripts/build_md_prompt.py.
Framework enforced by the prompt
1. Executive Summary (300-500 字)
- 一句话核心观点 (thesis) + 3 supporting bullets
- 授信 / 投资建议 (specific 额度 + 期限 + 利率 OR Buy/Hold/Sell + 目标价)
- 1-2 关键风险
2. Company Profile
- 沿革 (成立 + 上市 + 经营期限)
- 主业拆解 (industry field 展开到 sub-segments)
- 股权与资本结构 (注册资本 + 股本 + 市值 + 法人)
3. Industry Dynamics
- 赛道特征 (cyclicality, tech shifts, policy drivers)
- 中国位置 (份额估算, 以 [EST, per sector consensus] 标注)
- 主要对手 3-5 家 (国内 + 海外, 每家标 [EST])
- 政策驱动 (十四五 / 专项补贴 / 产业政策)
4. Financial Deep-Dive (表格为主)
- 3Y 年度对比表 (营收 / 净利 / ROE / 毛利率 / 资产负债率 + YoY)
- 季度趋势 (YTD 累积 cumulative fields 展开)
- 异常 flag (QoQ 跳变 > 5pp 必须点出)
- 数据口径 flag (若 income 反推 vs company_performance 不一致, 必须指出)
5. Peer Comparison
- 3-5 家同业表 (公司 / 代码 / 市值 / PE / PB / ROE + 备注)
- 每个 peer 数字必须标
[EST, per sector consensus]或[未核实] - 禁用 Wind / 同花顺 / 万得 / 彭博作为来源
- 相对估值分位 (target PE vs peer median)
6. Valuation
- 当前 PE/PB/PS (from daily_basic)
- 历史区间 (PB 历史 min-max, 是否破净)
- SOTP 分部估值 (成熟业务 PB / 成长业务 PS 等)
- 3 档目标价: 悲观 / 基础 / 乐观 + 假设 + 空间
7. Risk Factors
- 表格: 经营 / 财务 / 行业 / 治理 / 数据 5 类
- 每项有量化依据 + 严重程度 (高 / 中 / 低)
8. Credit / Investment View
信贷口径 (4C's):
- Character: 国资背景 / 实控人稳定 / 治理透明度
- Capacity: 营收规模 + 偿债能力指标
- Capital: 注册资本 + 净资产结构
- Collateral: 抵押物 specialised 程度 + 清算折价
具体授信建议: 额度区间 + 期限 + 利率 (LPR+bp) + 增信要求 + 财务承诺
投资口径: Buy / Hold / Sell + 目标价 + 催化剂 + 反向风险
Hard constraints (enforced by prompt, checked by gates)
- 每个硬数字必须溯源:
X 亿元（src: income）或Y%（src: company_performance） - 禁用 Wind / 万得 / 同花顺 / Bloomberg / 彭博 — 这些不是安装的 MCP,
source_authenticity_checkgate 会拦截 - 不写模糊数字 — "约 XX 亿" / "大约" 必须加
[EST]+ 推理依据 - Q4 单季变化用 pp 单位 —
+3.18pp不要%(避开 HARD_NUMBER 误判) - Peer 数字必须标
[EST, per sector consensus]— 永远不能挂一个权威名称 - Data Flag 自审 — 若发现 income 反推 vs company_performance 净利率差异 > 0.3pp, 必须单独一段
> Data Flag N:提示需要人工核实
Output files
Writes only two files to {out_dir}/:
analysis.md— 2500-4500 字 8 节 memodata-provenance.md— 每个硬数字一行:| 指标 | 数值 | 单位 | 来源文件 stem | MCP tool |
Not slides-outline.md — that's the banker-slides-pptx skill's job.
Usage
# Pre-flight: raw-data/ already populated by cn-client-investigation Phase 3.5
ls <deliverable_dir>/raw-data/*.json
# Build + dispatch prompt
python3 scripts/build_md_prompt.py <ts_code> <name_cn> <industry> \
<raw_dir> <out_dir> > /tmp/prompt.md
openclaw agent --agent main --thinking high --json --timeout 600 \
--message "$(cat /tmp/prompt.md)"
# Agent writes analysis.md + data-provenance.md
# Close any discipline gaps (agent's own provenance table sometimes misses
# numbers it wrote into prose; this post-process bridges the last mile)
python3 <cn-ci-scripts>/sync_provenance.py <out_dir>
# Now hand off to banker-slides-pptx for Step 2Quality checklist
- [ ] 8 sections all present (ES / Profile / Industry / Financial / Peer / Valuation / Risk / 4C's)
- [ ] Every
\d+(亿元|%|元|倍)in analysis.md has a provenance row (or[EST]tag) - [ ] Peer comparison has ≥3 companies, all tagged
[EST, per sector consensus] - [ ] Valuation section has ≥2 methods (relative + SOTP or DCF) + 3 scenarios
- [ ] Risk section is a table with severity levels (not a bullet list)
- [ ] 4C's section gives a specific credit conclusion (额度 + 期限 + 利率 + 增信 + 财务承诺)
- [ ] At least 1
> Data Flag N:self-audit paragraph (if income vs company_performance diverge) - [ ]
provenance_verify.pyPASS +source_authenticity_check.pyPASS