Banker Memo Md

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...

Security Scan

The skill's required files, small helper script, and runtime instructions are coherent with its stated purpose (producing a banker-grade Markdown memo from a local raw-data/ JSON snapshot directory) and do not request unrelated credentials, installs, or network endpoints.

Purpose & Capability

Name/description (produce investment-banker memo from CN raw-data JSON snapshots) matches the included assets: an instruction prompt (SKILL.md + references prompt) and a small helper script that templates the prompt from a local raw-data directory. No unrelated environment variables, binaries, or external services are requested.

Instruction Scope

The SKILL.md explicitly scopes data access to the provided {raw_dir} JSON files and defines exactly what outputs to write (analysis.md and data-provenance.md). It does not instruct the agent to read unrelated system files or environment variables, nor to transmit data to external endpoints. The usage section suggests running a local CLI and optional post-processing scripts (sync_provenance.py) which are outside the skill; those are noted as user steps but not built into the skill.

Install Mechanism

No install spec is present (instruction-only) and the single helper script only reads template text and lists local JSON files. There are no downloads, package installs, or archives written by the skill.

Credentials

The skill declares no required environment variables, credentials, or config paths. The only data access is to the raw-data JSON snapshots the skill is designed to consume; that is proportionate to its stated purpose. Note: those JSON files themselves may contain sensitive information — see user guidance.

Persistence & Privilege

The skill is not always-enabled; it is user-invocable and allows normal autonomous invocation (platform default). It does not request persistent system-wide privileges or attempt to modify other skills' configs.

Assessment

This skill appears coherent and purpose-aligned, but before installing: (1) inspect the raw-data/ JSON files you will feed it to ensure they contain only the intended company data and no unrelated secrets or private credentials; (2) verify any external post-processing scripts you may run (e.g., sync_provenance.py) are trusted; (3) run the build_md_prompt.py script locally to confirm it only templates the prompt and does not access network resources (it does not in the provided code); (4) be mindful that the agent will write files to the specified out_dir — ensure the agent's filesystem permissions are appropriately limited; (5) if you prefer tighter control, disable autonomous invocation for this skill or run it interactively so the agent cannot run it without your explicit action.

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 to raw-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)

1. 每个硬数字必须溯源: X 亿元（src: income） 或 Y%（src: company_performance）
2. 禁用 Wind / 万得 / 同花顺 / Bloomberg / 彭博 — 这些不是安装的 MCP, source_authenticity_check gate 会拦截
3. 不写模糊数字 — "约 XX 亿" / "大约" 必须加 [EST] + 推理依据
4. Q4 单季变化用 pp 单位 — +3.18pp 不要 % (避开 HARD_NUMBER 误判)
5. Peer 数字必须标 [EST, per sector consensus] — 永远不能挂一个权威名称
6. 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}/:

1. analysis.md — 2500-4500 字 8 节 memo
2. data-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 2

Quality 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.py PASS + source_authenticity_check.py PASS