Banker Slides Pptx

Turn a banker-memo analysis.md + data-provenance.md into an investment-banker-craft .pptx (real tables, bar/line charts, risk heatmap, scenario grid — NOT pa...

Security Scan

The skill appears to do what its name says (turn analysis.md + provenance into a structured slides outline and renderer), but there are a few inconsistencies and privacy/supply-chain gaps you should understand before installing.

Purpose & Capability

Name/description, the prompt template, and the two included scripts align: the skill transforms an existing analysis.md + data-provenance.md into a structured slides-outline.md and emits JS for pptxgenjs-style rendering. No unrelated credentials, paths, or exotic capabilities are requested.

Instruction Scope

build_pptx_prompt.py explicitly reads analysis.md and data-provenance.md and inlines their full contents into the generated prompt so the LLM receives the raw documents. This is intentional for functionality (so the agent can extract numbers) but means whatever is in those files will be sent to whatever LLM endpoint the agent uses. The SKILL.md does not prominently warn about that data-leakage-to-LLM behavior. Also the renderer expects to parse slides-outline.md and emit pptxgenjs primitives; the scripts reference file I/O and may call subprocesses (subprocess is imported). Confirm whether the agent will run these scripts locally or send their outputs to a remote model.

Install Mechanism

No install spec (instruction-only) — lower risk for arbitrary downloads. However the renderer emits pptxgenjs-targeted JS; there is no declared requirement for Node/npm or pptxgenjs. The code imports subprocess and may call external tooling (truncated portion not visible). You should verify runtime expectations (Node, npm packages, or a separate JS runner) before use.

Credentials

No environment variables, credentials, or config paths are requested. The skill operates on local deliverable_dir files (analysis.md, data-provenance.md) and does not require unrelated secrets.

Persistence & Privilege

always:false and disable-model-invocation is default; the skill does not request permanent system presence or attempt to modify other skills. No evidence it writes persistent agent-wide configuration beyond reading/writing files in the deliverable directory.

What to consider before installing

This skill is largely coherent for producing banker-style PPTX outlines, but check the following before installing: 1) Sensitive-data warning: build_pptx_prompt.py will inline the full analysis.md and data-provenance.md into the LLM prompt — that content will be sent to whatever model endpoint the agent uses. Do not provide confidential data unless you're comfortable with that. 2) Runtime dependencies: the renderer targets pptxgenjs (Node/JS). The package does not declare Node/npm or pptxgenjs as requirements; confirm your environment has the needed runtime or review the remainder of build_outline_deck_v2.py to see whether it invokes node/subprocess. 3) Audit for outbound execution: inspect the remainder of build_outline_deck_v2.py for any subprocess calls or network operations before running. 4) Test on non-sensitive sample data first to confirm the pipeline (slides-outline.md → .pptx) behaves as expected. If you want, provide the rest of the renderer script and I can review the truncated portions for any subprocess/network calls.

Step 2 of the banker pipeline: analysis.md + data-provenance.md → slides-outline.md (structured) → .pptx with real banker visual primitives.

Pipeline position

banker-memo-md (Step 1) → analysis.md + data-provenance.md
                              │
                              ▼
                    banker-slides-pptx (THIS SKILL)
                              │
                    ┌─────────┴─────────┐
                    ▼                   ▼
           slides-outline.md    (rendered by)
           (structured YAML     build_outline_deck_v2.py
            blocks per slide)        │
                                     ▼
                                   .pptx

What makes this different from a generic "write some slides" prompt

Most LLM-written slide outlines are prose ("Slide 4: Key takeaway is X"). That's useless — the renderer can only emit a paragraph.

This skill forces the agent to write structured layout data the renderer can parse into real pptxgenjs primitives:

• stat-cards → addShape + big-number addText cards
• 3y-table / peer-table / scenario-table → real addTable() with column widths + cell fill
• bar-chart / line-chart → real addChart() with X-axis + Y-series data
• risk-heatmap → 3×3 colored grid with risk items placed by (severity, likelihood)
• callout-box → full-width gold-bordered box + large-font verdict text

The prompt reads analysis.md already written by banker-memo-md, extracts the numbers, and emits structured outline blocks. The renderer does no LLM work — it's a deterministic layout emitter.

Outline schema (what the agent writes)

Each slide uses a ## Slide N — <Title> heading followed by Layout: + type-specific YAML-style fields.

cover

## Slide 1 — Cover
Layout: cover
English-title: China Vanke Co., Ltd.
Chinese-subtitle: 万科A · 房地产开发 · 投行深度研究
Ticker: 000002.SZ
Date: 2026-04

divider (section divider between parts)

## Slide N — Section Divider
Layout: divider
Chinese-title: 财务深度诊断
English-subtitle: Financial Deep-Dive

stat-cards (3-4 big-number cards)

## Slide N — Executive Summary Stats
Layout: stat-cards
Cards:
- label: 2024 营收
  value: "3,431.76"
  unit: 亿元
- label: 归母净亏损
  value: "-494.78"
  unit: 亿元
  highlight: red
- label: ROE 2024
  value: "-21.82"
  unit: "%"
  highlight: red
- label: 资产负债率
  value: "73.66"
  unit: "%"
  highlight: amber

table (3Y YoY / peer / any matrix)

## Slide N — 3Y Financial Trend
Layout: table
Headers: ["指标", "2022", "2023", "2024", "YoY 24v23"]
Rows:
- ["营收（亿元）", "5,038.4", "4,657.4", "3,431.8", "-26.3%"]
- ["归母净利（亿元）", "226.2", "67.7", "-494.8", "N/M"]
- ["ROE（%）", "9.45", "4.93", "-21.82", "-26.75pp"]
- ["毛利率（%）", "19.55", "15.31", "10.17", "-5.14pp"]
- ["资产负债率（%）", "76.14", "75.29", "73.66", "-1.63pp"]
Note: YoY 计算: 2024 值 - 2023 值

bar-chart / line-chart

## Slide N — Revenue & Profit Trend
Layout: bar-chart
X-axis: ["2022", "2023", "2024"]
Y-series:
- name: 营收（亿元）
  values: [5038, 4657, 3432]
  color: "C9A84C"
- name: 归母净利（亿元）
  values: [226, 68, -495]
  color: "D4AF37"
Note: 营收 3 年 -32%，净利 2024 转负

risk-heatmap (3×3 severity × likelihood)

## Slide N — Risk Heatmap
Layout: risk-heatmap
Risks:
- name: 存量减值压力
  severity: 高
  likelihood: 高
- name: 美元债重组
  severity: 高
  likelihood: 中
- name: 交付延期
  severity: 中
  likelihood: 中
- name: 数据口径风险
  severity: 中
  likelihood: 低

scenario-table (valuation scenarios)

## Slide N — Valuation Scenarios
Layout: scenario-table
Scenarios:
- name: 悲观
  assumption: "PE 10x · EPS -4.48 → N/M; 2025 仍巨亏"
  target: "2.0 元"
  upside: "-49%"
  color: red
- name: 基础
  assumption: "化债方案落地 + 2025 净利回正"
  target: "3.0 元"
  upside: "-24%"
  color: amber
- name: 乐观
  assumption: "政府背书 + OLED 转型兑现（类比 BOE）"
  target: "5.5 元"
  upside: "+40%"
  color: green

callout-box (large verdict stamp)

## Slide N — Credit View
Layout: callout-box
Title: 授信建议
Tags: ["拒绝承做", "Sell", "Target 2-3 元"]
Message: ROE -21.82% + 资产负债率 73.66% + 经营现金流持续失血（估算）+ 2025 多个项目公司贷款逾期 → 当前节点无法给予新增授信。深圳市政府化债方案落地前，仅密切关注，不入授信池。

bullets (fallback, sparingly)

## Slide N — Key Points
Layout: bullets
Points:
- 论点 1 + 数据支撑 (src: ...)
- 论点 2

Deck composition (required structure)

The prompt enforces this structure (12-15 slides typical):

1. Cover (1 slide)
2. Executive Summary (1 slide, stat-cards layout)
3. Section Divider: Company (1 slide, divider)
4. Company Profile (1 slide, table)
5. Section Divider: Industry (1 slide, divider)
6. Industry Position (1 slide, bar-chart or table with peer share)
7. Section Divider: Financial (1 slide, divider)
8. 3Y Trend (1 slide, table)
9. Profitability Chart (1 slide, bar-chart or line-chart)
10. Section Divider: Valuation & Risk (1 slide, divider)
11. Peer Comparison (1 slide, table)
12. Valuation Scenarios (1 slide, scenario-table)
13. Risk Heatmap (1 slide, risk-heatmap)
14. Credit / Investment View (1 slide, callout-box)
15. Data Sources (1 slide, bullets listing raw-data files)

Agent may merge divider slides into adjacent content when narrative is tight, but target stays 13-15.

Hard constraints

1. Every number in outline must already be in analysis.md — the renderer will run slide_data_audit against provenance; numbers not in provenance will fail the gate
2. Use rounded banker notation for stat cards: "3,431.76 亿元" → display as "3,432" + unit "亿" (the underlying analysis.md has the exact value; cards round for visual)
3. Peer comp numbers stay [EST] — the table cells for peer rows should include [EST] suffix in value string
4. Chart Y-series values must be numeric (not strings), color codes 6-char hex without #
5. Heatmap severity/likelihood must be one of 高/中/低 — renderer maps to 3×3 grid

Usage

# Pre-flight: analysis.md + data-provenance.md already written by banker-memo-md
ls <deliverable>/{analysis.md,data-provenance.md}

# Build + dispatch prompt
python3 scripts/build_pptx_prompt.py <deliverable> <ts_code> <name_cn> \
        <name_en> > /tmp/prompt.md
openclaw agent --agent main --thinking high --json --timeout 600 \
        --message "$(cat /tmp/prompt.md)"
# Agent writes slides-outline.md in the deliverable dir

# Render the deck
python3 scripts/build_outline_deck_v2.py <deliverable> <ts_code> <name_cn> <name_en>

# Validate
python3 <cn-ci-scripts>/sync_provenance.py <deliverable>
python3 <cn-ci-scripts>/validate-delivery.py --strict-mcp <deliverable>

Quality checklist

• slides-outline.md has 12-15 slides
• Contains ≥1 each of: stat-cards, table, bar-chart or line-chart, risk-heatmap, scenario-table, callout-box
• NOT all bullets (that's the fallback layout — using it for > 2 slides means the prompt failed)
• All numbers traceable to provenance (slide_data_audit PASS)
• Compile passes cn_typo_scan
• Rendered .pptx has actual charts (not placeholders) when outline specifies bar-chart / line-chart