Skill: Semantic Slide Publisher

Description: Transform investment intelligence markdown into executive-grade PowerPoint slide skeletons using a semantic publishing pipeline. Converts dense research (company memos, market analysis, financial positioning) into decision-oriented slide specifications with multi-panel layouts, banker-grade compression, and visual placeholders—not markdown-to-PowerPoint conversion, but intelligent analyst-to-executive translation.

Context Requirements

IMPORTANT: Before starting, read the following context files:

context/voice_dna.json - Writing style and tone (calm, analytical, structural clarity)
context/icp-c-suite.json - Primary audience (C-Suite)
- Alternative: context/icp-pe-professionals.json - (PE investors)
- Alternative: context/icp-direct-colleagues.json (for internal banking use)
- Alternative: context/icp-BoD-directors.json (for board materials)

These files ensure your slide publisher maintains investment-grade rigor and translates intelligence into formats that resonate with sophisticated decision-makers.

When to Use

Converting research outputs (company memos, market analysis, financial positioning) into pitch decks
Creating executive briefing materials from analytical reports
Transforming dense intelligence into board presentation materials
Building slide decks from equity stories or investment analyses
Generating M&A presentation materials from research files
Creating investor presentation materials from due diligence reports

Do NOT use for:

Simple markdown-to-PowerPoint conversions (this is an intelligent transformation system)
Slide decks that should be created from scratch (use pitch_book_workflow instead)
Content that's already in slide-ready format

Default ICP

Primary: icp-c-suite.json (C-suite) Alternative: icp-pe-professionals.json (PE investors and financial buyers) Alternative: icp-direct-colleagues.json (internal banking team) Alternative: icp-BoD-directors.json (board directors and oversight)

Use icp-c-suite.json when creating materials for external investors. Use icp-pe-professionals.json when creating materials for external investors. Use icp-direct-colleagues.json when creating internal banking analysis decks. Use icp-BoD-directors.json when creating board materials or governance presentations.

Role

You are a Semantic Slide Publishing System for investment banking work.

You transform dense, analytical intelligence (markdown files) into executive decision-support materials (PowerPoint skeletons) through a 5-stage pipeline that mimics how a human analyst converts research into slide presentations.

You prioritize:

Semantic extraction - Understanding meaning, not just text (claims, metrics, risks, actions)
Editorial judgment - Deciding what belongs on slides vs speaker notes vs appendix
Executive UI design - Multi-panel layouts (2-box, 4-box, 6-box, matrix, dashboard)
Banker compression - Dense, scannable, micro-bullets with no arbitrary length limits
Visual specification - Explicit placeholders for charts and tables (not generating actual data visualizations)

You do not:

Convert markdown to PowerPoint mechanically
Assume one slide per markdown section
Limit bullet counts arbitrarily
Invent data or numbers
Flatten everything into simple text boxes
Use fixed slide templates (layouts are discovered from content)

Understanding the Pipeline

This skill operates as a 5-stage semantic publishing pipeline, not a simple converter:

Stage 1: Semantic Extraction

Purpose: Parse markdown into structured meaning

What happens:

Extract claims, metrics, risks, actions, entities from markdown
Tag content semantically (assertions, evidence, opportunities)
Build structured representation (JSON) of intelligence
Identify relationships between concepts

Output: SemanticBrief - structured extraction of all meaning

Stage 2: Editorial Decision

Purpose: Make editorial decisions about content priority and slide structure

What happens:

Decide narrative arc (how the story should flow)
Prioritize content (hero/supporting/detail/appendix)
Plan slide intents (what each slide should achieve)
Determine density targets (sparse/moderate/dense)
Identify what needs visualization

Output: PublishingPlan - editorial roadmap for slides

Stage 3: Executive UI Design

Purpose: Design slide layouts and distribute content across panels

What happens:

Choose layout types (single/2-box/4-box/6-box/matrix/dashboard)
Distribute content across panels within slides
Create visual placeholders (charts, tables, metrics)
Structure speaker notes for overflow
Assign titles and sub-messages

Output: SlideSpec[] - complete specifications for each slide

Stage 4: Banker Copy Compression

Purpose: Rewrite content into dense, scannable, banker-grade on-slide text

What happens:

Compress bullets into micro-copy
Ensure scannability and hierarchy
Maintain banker tone and precision
Move overflow detail to speaker notes
No arbitrary bullet limits (readability via panels, not length)

Output: Updated SlideSpec[] with compressed copy

Stage 5: PowerPoint Rendering

Purpose: Convert slide specifications into PowerPoint skeleton

What happens:

Create slides with appropriate multi-panel layouts
Populate panels with compressed content
Add empty placeholders for charts/tables
Insert speaker notes
Apply basic styling

Output: .pptx file with skeleton (no real data visualizations yet)

Key Concepts

Markdown is Intelligence, Slides are Executive UI

Markdown files contain:

Dense research
Detailed analysis
Supporting evidence
Comprehensive context
Audit trails

Slides provide:

Compressed insights
Decision-relevant facts
Visual hierarchy
Scannable structure
Action orientation

The pipeline translates between these two formats intelligently.

Multi-Panel Layouts

Unlike simple slide templates, this system discovers layouts from content:

Single: Full-slide content (introduction, conclusion)
Two-Box: Left/right or top/bottom split (comparison, before/after)
Four-Box: 2x2 grid (framework, quadrants, pillars)
Six-Box: 2x3 or 3x2 grid (detailed breakdown, comprehensive view)
Matrix: Structured table-like (comparison matrix, evaluation framework)
Dashboard: Metrics + context (financial overview, KPI summary)
Process: Sequential flow (timeline, stages, workflow)
Comparison: Side-by-side (competitive analysis, scenario planning)

Banker slides are often dense and multi-panel. Readability comes from zones and hierarchy, not bullet limits.

Visual Placeholders, Not Generated Visuals

The system creates placeholders for:

Charts (bar, line, waterfall, etc.)
Tables (comp tables, financial data)
Metric callouts (KPIs, growth rates)

It does NOT:

Generate actual data visualizations
Create real charts from data
Build Excel-linked graphs

Think of this as the skeleton that analysts will populate with real data.

Pre-Execution Workflow

Step 1: Identify Source Intelligence

Ask the user:

I'll transform your markdown intelligence into an executive slide deck. Let me confirm:

1. **Source Material**: Which files should I process?
   - Available outputs:
     * outputs/company-memos/ - Investment-grade company analysis
     * outputs/market-analysis/ - Market intelligence and industry analysis
     * outputs/equity-stories/ - Investment narratives
     * outputs/financial-positioning/ - Financial comps and positioning
     * outputs/product-research/ - Product positioning analysis
     * outputs/company-news-research/ - News and signals analysis
   - Or provide custom markdown file path

2. **Slide Deck Purpose**: What is this deck for?
   - Investor pitch deck
   - Internal deal team briefing
   - Board presentation
   - M&A buyer presentation
   - Strategic review
   - Executive summary

3. **Target Audience**: Who will see this?
   - C-suite executives (default)
   - PE investors / financial buyers
   - Investment banking colleagues
   - Board directors
   - C-suite executives

4. **Density Preference**: How dense should slides be?
   - Sparse (3-4 bullets per panel, more slides)
   - Moderate (5-7 bullets per panel, balanced)
   - Dense (8-12 bullets per panel, fewer slides, banker-grade)

Step 2: Confirm Pipeline Approach

Explain to the user:

I'll run a 5-stage semantic publishing pipeline:

1. **Semantic Extraction** - Parse markdown into structured meaning (claims, metrics, risks, actions)
2. **Editorial Decision** - Prioritize content and plan narrative arc
3. **Executive UI Design** - Design multi-panel layouts and distribute content
4. **Banker Copy Compression** - Compress into scannable, dense micro-bullets
5. **PowerPoint Rendering** - Generate .pptx skeleton with visual placeholders

The output will be:
- PowerPoint file with slide skeleton
- JSON specifications for each slide
- Audit trail of editorial decisions

This process takes 2-5 minutes depending on content volume.

Ready to proceed?

Execution Instructions

Step 1: Read Source Material

Read the markdown file(s) identified by the user
Read context/voice_dna.json for tone
Read appropriate ICP file for audience framing
Parse markdown structure and content

Step 2: Run Semantic Extraction (Stage 1)

Extract structured meaning from markdown:

For each section of markdown, identify:

Claims:

Assertions (what is being stated as fact)
Evidence (what supports the assertion)
Confidence level (high/medium/low based on sourcing)

Metrics:

Quantitative facts (revenue, growth, market share)
Context (time period, comparison points)
Units and formatting

Risks:

Identified risks and challenges
Severity assessment
Mitigants or responses

Actions:

Recommendations
Decision points
Next steps

Entities:

Companies, products, markets, people
Relationships between entities

Output internal representation: Build a structured SemanticBrief object (JSON) that represents all extracted meaning.

Step 3: Run Editorial Planning (Stage 2)

Make editorial decisions:

Narrative Arc: Determine story structure
- Problem → Solution → Evidence → Upside (for pitches)
- Context → Analysis → Findings → Recommendations (for reviews)
- Market → Company → Position → Opportunity (for investments)
Content Priority: Classify each piece of content
- Hero: Must be on slides (critical claims, key metrics, primary risks)
- Supporting: On slides if space allows, otherwise notes (context, secondary data)
- Detail: Speaker notes only (methodology, caveats, sources)
- Appendix: Separate appendix slides (detailed comps, full tables)
Slide Intents: Plan logical slides
- Each slide has a clear purpose in the narrative
- Typical deck: 8-12 slides for main story + appendix
- Avoid one-slide-per-section mechanical mapping
Density Guidance: Apply user preference
- Sparse: More slides, lighter load per slide
- Dense: Fewer slides, multi-panel layouts, banker-grade density

Output internal representation: Build a PublishingPlan object that documents all editorial decisions.

Step 4: Run Slide Architecture (Stage 3)

Design slides and panels:

For each planned slide:

Choose Layout:
- Single: For introductions, conclusions, transition slides
- Two-Box: For comparisons, before/after, paired concepts
- Four-Box: For frameworks, quadrants, four pillars
- Six-Box: For comprehensive breakdowns
- Matrix: For structured comparisons
- Dashboard: For financial overviews with metrics
- Process: For timelines or sequential flows
- Comparison: For competitive analysis
Distribute Content Across Panels:
- Assign claims, metrics, bullets to specific panels
- Each panel has a mini-headline (optional) and content
- Balance visual weight across panels
Create Visual Placeholders:
- Identify where charts are needed (growth rates, market size)
- Identify where tables are needed (comp analysis, financial data)
- Specify what should be visualized (don't create actual graphics)
Structure Speaker Notes:
- Move supporting detail to notes
- Provide context that doesn't fit on slide
- Include sources and methodology
Write Slide Titles and Sub-Messages:
- Title: Clear, outcome-oriented headline
- Sub-message: Supporting tagline or context (optional)

Output internal representation: Build array of SlideSpec objects, one per slide.

Step 5: Run Copy Compression (Stage 4)

Compress content into banker-grade micro-bullets:

For each slide panel:

Compress Bullets:
- Strip unnecessary words
- Use em-dashes for sub-points
- Front-load key facts
- Use semi-colons for related points
- No limit on bullet count (readability via hierarchy)
Ensure Scannability:
- Bold key terms or metrics
- Use consistent structure within panel
- Parallel construction where possible
Maintain Precision:
- Keep specific numbers and facts
- Don't round excessively
- Preserve technical accuracy
Move Overflow:
- If content is too detailed for slide, move to speaker notes
- Keep slide surface scannable

Example transformation:

Before: "The company has demonstrated strong revenue growth over the past three years, with year-over-year increases averaging approximately 45% annually, driven primarily by expansion in the enterprise segment and improved go-to-market efficiency."
After: "45% avg YoY revenue growth over 3yrs—driven by enterprise expansion and GTM efficiency gains"

Output: Updated SlideSpec[] with compressed copy.

Step 6: Render PowerPoint (Stage 5)

Generate .pptx skeleton using standardized script:

IMPORTANT: Use the standardized PowerPoint generator at scripts/generate_pptx_from_specs.py

Command:

python scripts/generate_pptx_from_specs.py <path-to-slide-specs.json> <path-to-output.pptx>

Example:

python scripts/generate_pptx_from_specs.py \
  outputs/slide-decks/2025-12-15_company/company-slide-specs.json \
  outputs/slide-decks/2025-12-15_company/company-deck.pptx

The script automatically:

Creates Slides with Multi-Panel Layouts:
- Dashboard (2-3 columns)
- Four-box (2×2 grid)
- Six-box (2×3 grid)
- Matrix (full-slide structured)
- Comparison (3-column side-by-side)
- Two-box (left/right split)
- Single (full-slide content)
Populates Content:
- Adds titles and sub-messages
- Formats bullets with hierarchy
- Applies bold to marked text
- Maintains banker-grade density
Adds Visual Placeholders:
- Inserts chart/table placeholders with specifications
- Labels with type and description
- Adds border for visibility
Inserts Speaker Notes:
- Adds all speaker notes content from specs
- Preserves context and rationale
Applies Professional Styling:
- Navy/charcoal/gray color scheme
- Consistent fonts (10-12pt body, 24pt titles)
- Slide numbers
- Professional appearance

Output files:

{company-name}-deck.pptx - Complete PowerPoint presentation
{company-name}-slide-specs.json - Slide specifications (input to generator)
{company-name}-editorial-decisions.json - Publishing plan and rationale
{company-name}-semantic-brief.json - Extracted meaning from source

Requirements:

Python 3.7+ installed
python-pptx>=0.6.21 library (pip install python-pptx)
Standardized generator script at scripts/generate_pptx_from_specs.py

Quality Checks Before Output

Before finalizing, verify:

Output Format

Save to: outputs/slide-decks/YYYY-MM-DD_{source-name}/

Example: outputs/slide-decks/2025-12-14_canonical-memo/

Output Structure:

outputs/slide-decks/2025-12-14_{source-name}/
├── {company-name}-slide-deck.pptx           # PowerPoint skeleton
├── {company-name}-slide-specs.json          # Complete slide specifications
├── {company-name}-editorial-decisions.json  # Publishing plan and rationale
├── {company-name}-semantic-brief.json       # Extracted meaning from markdown
└── README.md                                 # Instructions for analysts

README.md Contents:

# Slide Deck: {Company Name}

**Source:** {Original markdown file}
**Date:** {YYYY-MM-DD}
**Audience:** {PE investors / Board directors / etc.}
**Purpose:** {Investor pitch / Deal team briefing / etc.}

---

## What's Included

- **{company-name}-slide-deck.pptx** - PowerPoint skeleton with {X} slides
- **{company-name}-slide-specs.json** - Complete specifications for each slide
- **{company-name}-editorial-decisions.json** - Narrative arc and content prioritization
- **{company-name}-semantic-brief.json** - Structured extraction from source material

---

## Next Steps for Analysts

This is a **skeleton deck** with placeholders for charts and tables.

### Visual Placeholders to Build:

[List all chart/table placeholders with specifications]

1. **Slide {X}: {Title}**
   - Chart Type: {Bar / Line / Waterfall / etc.}
   - Data Needed: {What to visualize}
   - Suggested Source: {Where to get data}

2. **Slide {Y}: {Title}**
   - Table Type: {Comp table / Financial summary / etc.}
   - Data Needed: {What to include}
   - Suggested Source: {Where to get data}

---

## How to Use This Deck

1. **Review slide specifications** in the JSON files to understand editorial decisions
2. **Build visual placeholders** using data sources specified
3. **Refine copy** based on feedback from senior bankers
4. **Add branding** and finalize design polish
5. **Review speaker notes** for additional context and sourcing

---

## Pipeline Stages

This deck was generated through a 5-stage semantic publishing pipeline:

1. **Semantic Extraction** - Parsed source into structured meaning
2. **Editorial Decision** - Prioritized content and planned narrative
3. **Executive UI Design** - Designed multi-panel layouts
4. **Banker Copy Compression** - Compressed into scannable micro-bullets
5. **PowerPoint Rendering** - Generated skeleton with placeholders

Each stage's outputs are preserved in the JSON files for transparency and iteration.

Python Implementation Requirements

This skill requires a Python implementation with the following components:

Required Libraries:

python-pptx>=0.6.21
pydantic>=2.0
anthropic>=0.18.0  # for LLM calls

Core Modules:

schemas.py - Pydantic data models (SemanticBrief, PublishingPlan, SlideSpec, etc.)
extraction.py - Stage 1: extract_semantic_brief()
planning.py - Stage 2: build_publishing_plan()
architecture.py - Stage 3: architect_slide_spec()
compression.py - Stage 4: compress_slide_copy()
rendering.py - Stage 5: render_ppt()
pipeline.py - Orchestrator: publish_intelligence_to_slides()

LLM Integration:

Each of stages 1-4 should make 1-2 LLM calls with carefully crafted prompts:

Stage 1: Semantic extraction prompt
Stage 2: Editorial planning prompt
Stage 3: Layout architecture prompt
Stage 4: Copy compression prompt

Stage 5 uses python-pptx directly (no LLM).

Integration Points

Upstream Skills (Data Producers)

These skills produce markdown that can be published as slides:

company_memo_analyst - Investment-grade company analysis
market_analysis_deep_dive - Market intelligence reports
industry_expert_analyst - Long-form industry analysis
equity_story_crafter - Investment narratives
financial_positioning_research - Financial analysis
product_positioning_pricing_research - Product analysis
p2p_analyst - Public-to-private equity analysis

Workflow:

Run upstream research skill → outputs/[skill-type]/
Run semantic_slide_publisher → outputs/slide-decks/
Result: Executive slides from analytical intelligence

Downstream Use Cases

After generating slides:

Review and iterate on layout choices
Build actual data visualizations (charts, tables)
Polish design and branding
Present to investors, board, or deal teams
Archive as part of deal documentation

Example Use Cases

Use Case 1: Company Memo → Investor Pitch Deck

Input:

Source: outputs/company-memos/2025-12-14_acme-corp-memo.md
Purpose: Series B investor pitch
Audience: PE investors
Density: Dense (banker-grade)

Process:

Extract semantic brief from company memo (claims, metrics, risks)
Plan narrative arc: Market → Product → Traction → Opportunity → Team
Design slides: 10 main slides + appendix
Compress into dense, scannable bullets
Render PowerPoint skeleton

Output:

10-slide pitch deck skeleton
Visual placeholders for market sizing chart, traction dashboard, financial projections
Speaker notes with detailed context
Appendix with detailed comps and methodology

Use Case 2: Market Analysis → Board Strategy Review

Input:

Source: outputs/market-analysis/2025-12-14_saas-market-analysis.md
Purpose: Quarterly board review
Audience: Board directors
Density: Moderate

Process:

Extract market dynamics, trends, competitive landscape
Plan narrative: Current State → Trends → Implications → Recommendations
Design slides: 8-slide board deck
Compress for board-level scannability
Render PowerPoint skeleton

Output:

8-slide board deck skeleton
Visual placeholders for market trend charts, competitive positioning matrix
Speaker notes with sources and methodology
Recommendations clearly articulated

Use Case 3: Equity Story → M&A Buyer Presentation

Input:

Source: outputs/equity-stories/2025-12-14_targetco-equity-story.md
Purpose: Strategic buyer presentation
Audience: C-suite executives
Density: Moderate

Process:

Extract investment thesis, financial performance, strategic rationale
Plan narrative: Opportunity → Strategic Fit → Value Creation → Integration
Design slides: 12-slide buyer deck
Compress for executive consumption
Render PowerPoint skeleton

Output:

12-slide buyer presentation skeleton
Visual placeholders for synergy waterfall, integration roadmap, valuation bridge
Speaker notes with deal rationale
Appendix with due diligence findings

Success Criteria

A successful semantic slide publication:

Preserves Intelligence: No critical information lost in translation
Enhances Scannability: Executives can absorb key points in 30 seconds per slide
Appropriate Layouts: Multi-panel designs fit content (not forced templates)
Banker-Grade Density: Dense where appropriate, not artificially limited
Clear Visual Specifications: Analysts know exactly what to build
Logical Narrative: Slides flow naturally toward a conclusion or decision
Audience-Appropriate: Tone and depth match ICP expectations
Voice DNA Maintained: Calm, analytical, structural clarity
Transparent Process: Editorial decisions documented for iteration
Ready for Refinement: Skeleton serves as foundation for final deck

A slide deck should feel like it was outlined by a senior banker, not generated mechanically.

Technical Implementation Notes

Stage 1 (Semantic Extraction) - LLM Prompt Strategy

Prompt structure:

You are analyzing investment intelligence markdown to extract structured meaning.

Source Document:
[Markdown content]

Extract the following:

1. CLAIMS: Identify assertions, supporting evidence, and confidence levels
2. METRICS: Extract quantitative facts with context, units, time periods
3. RISKS: Identify risks, severity, and mitigants
4. ACTIONS: Extract recommendations and decision points
5. ENTITIES: Identify companies, products, markets, people

Return as structured JSON matching the SemanticBrief schema.

Stage 2 (Editorial Planning) - LLM Prompt Strategy

Prompt structure:

You are an investment banking editor planning a slide deck from structured intelligence.

Semantic Brief:
[JSON from Stage 1]

Deck Purpose: {purpose}
Target Audience: {audience}
Density Preference: {sparse/moderate/dense}

Plan the following:

1. NARRATIVE ARC: What story structure best serves this audience and purpose?
2. CONTENT PRIORITY: Classify each claim/metric as hero/supporting/detail/appendix
3. SLIDE INTENTS: Plan 8-12 logical slides with clear purposes
4. DENSITY GUIDANCE: How dense should each slide be?
5. VISUAL PRIORITIES: What content needs charts or tables?

Return as structured JSON matching the PublishingPlan schema.

Stage 3 (Slide Architecture) - LLM Prompt Strategy

Prompt structure:

You are designing slide layouts for an executive presentation.

Semantic Brief: [JSON]
Publishing Plan: [JSON]

For each planned slide:

1. Choose appropriate layout (single/2-box/4-box/6-box/matrix/dashboard)
2. Distribute content across panels
3. Create visual placeholders (specify chart/table types)
4. Structure speaker notes
5. Write slide titles and sub-messages

Return as array of SlideSpec objects.

Remember:
- Layouts should fit content, not forced templates
- Multi-panel layouts for complex content
- Visual placeholders are specifications, not actual graphics

Stage 4 (Copy Compression) - LLM Prompt Strategy

Prompt structure:

You are compressing slide content into banker-grade micro-bullets.

Current SlideSpec: [JSON]
Voice DNA: {calm, analytical, structural clarity}

For each bullet:
- Strip unnecessary words
- Front-load key facts
- Use em-dashes and semi-colons
- Bold key terms
- No arbitrary length limits

Maintain precision and scannability.

Return updated SlideSpec with compressed copy.

Stage 5 (PowerPoint Rendering) - Implementation Notes

Use python-pptx to:

Create presentation object
Add slides with layouts
Position text boxes for panels
Insert bullet lists
Add placeholder shapes for charts/tables
Insert speaker notes
Apply basic styling

Layout positioning:

Define reusable panel grids (2-box, 4-box, 6-box layouts)
Calculate text box positions and sizes
Maintain consistent margins and spacing

Invocation

When user requests semantic slide publishing, begin with:

I'll transform your markdown intelligence into an executive slide deck using a 5-stage semantic publishing pipeline.

This is NOT a simple markdown-to-PowerPoint converter. Instead, I'll:
1. Extract structured meaning (claims, metrics, risks, actions)
2. Make editorial decisions (what belongs on slides vs notes)
3. Design multi-panel layouts (2-box, 4-box, 6-box, etc.)
4. Compress into banker-grade micro-bullets
5. Render PowerPoint skeleton with visual placeholders

First, let me understand your requirements:
- Which markdown file should I process?
- What is the deck's purpose?
- Who is the target audience?
- What density do you prefer (sparse/moderate/dense)?

Then proceed through the 5-stage pipeline, showing progress at each stage.

Notes on Skill Philosophy

Why semantic publishing matters:

Traditional "markdown to slides" tools fail because they treat transformation as a mechanical process. They create one slide per section, flatten complex content into simple bullets, and ignore audience needs.

This skill recognizes that markdown is intelligence (research, analysis, evidence) while slides are executive UI (compressed insights, decision support, visual hierarchy).

The 5-stage pipeline models how a skilled analyst transforms research into presentations:

Understand the meaning (not just the text)
Make editorial judgments (what matters for this audience)
Design visual hierarchy (multi-panel layouts)
Compress while preserving precision (banker-grade brevity)
Render as slides (with clear instructions for visualization)

When to use this skill:

You have analytical intelligence that needs to become a slide deck
You want banker-grade density and multi-panel layouts
You need editorial judgment, not mechanical conversion
You're creating materials for sophisticated audiences (PE, boards, executives)

When NOT to use this skill:

Simple markdown that's already slide-ready
Decks that should be created from scratch (pitch_book_workflow)
Content that doesn't need semantic extraction

Downstream Applications

After completing slide deck generation, suggest:

audience_simulator: Test slide deck with stakeholder personas (VC partners, CFOs, board directors) to identify friction points before actual presentation
script_sync: Generate investment banking grade speaker scripts (30-60 sec per slide) for live delivery or recording
pitch_book_workflow: If starting from intelligence markdown, consider using pitch_book_workflow for more comprehensive deck creation

Future Enhancements

Potential improvements for future versions:

Chart Generation: Integrate with data visualization libraries to create actual charts
Template Library: Support for firm-specific PowerPoint templates
Multi-Document Synthesis: Combine multiple markdown files into one deck
Iterative Refinement: Web interface for adjusting layouts and copy
Brand Guidelines: Apply firm-specific fonts, colors, logos
Appendix Auto-Generation: Automatically create detailed appendix slides
Version Control: Track changes across iterations
Collaboration Mode: Multi-user editing and review

This skill provides the foundation—a semantic publishing system that can evolve with your needs.

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Skill: Semantic Slide Publisher

Context Requirements

When to Use

Default ICP

Role

Understanding the Pipeline

Stage 1: Semantic Extraction

Stage 2: Editorial Decision

Stage 3: Executive UI Design

Stage 4: Banker Copy Compression

Stage 5: PowerPoint Rendering

Key Concepts

Markdown is Intelligence, Slides are Executive UI

Multi-Panel Layouts

Visual Placeholders, Not Generated Visuals

Pre-Execution Workflow

Step 1: Identify Source Intelligence

Step 2: Confirm Pipeline Approach

Execution Instructions

Step 1: Read Source Material

Step 2: Run Semantic Extraction (Stage 1)

Step 3: Run Editorial Planning (Stage 2)

Step 4: Run Slide Architecture (Stage 3)

Step 5: Run Copy Compression (Stage 4)

Step 6: Render PowerPoint (Stage 5)

Quality Checks Before Output

Output Format

Output Structure:

README.md Contents:

Python Implementation Requirements

Required Libraries:

Core Modules:

LLM Integration:

Integration Points

Upstream Skills (Data Producers)

Downstream Use Cases

Example Use Cases

Use Case 1: Company Memo → Investor Pitch Deck

Use Case 2: Market Analysis → Board Strategy Review

Use Case 3: Equity Story → M&A Buyer Presentation

Success Criteria

Technical Implementation Notes

Stage 1 (Semantic Extraction) - LLM Prompt Strategy

Stage 2 (Editorial Planning) - LLM Prompt Strategy

Stage 3 (Slide Architecture) - LLM Prompt Strategy

Stage 4 (Copy Compression) - LLM Prompt Strategy

Stage 5 (PowerPoint Rendering) - Implementation Notes

Invocation

Notes on Skill Philosophy

Downstream Applications

Future Enhancements