moody-s-rating-analysis

name

moody-s-rating-analysis

description

Produce a Rating Pitch Report for a company using Moody's GenAI MCP tools, delivered as a self-contained HTML file saved to disk. Use this skill whenever the user asks to create a rating pitch, rating pitch deck, credit pitch, rating presentation, rating pitch report, or rating HTML report. Also trigger when they ask for a comprehensive credit overview combining sector analysis, company financials, SWOT, peer comparison, and ESG into a single report or presentation. Trigger even if they just name a company and say "pitch deck", "rating deck", "credit deck", or "rating report".

Rating Pitch Skill

Generates a Moody's Rating Pitch Report as a self-contained HTML file from a single MCP data pass. The Python builder (scripts/build_html.py) takes the resolved payload JSON and produces a single .html file containing all sections with inline Chart.js charts, styled tables, and bullet lists using the Moody's brand palette — no external dependencies beyond a browser to open it.

⚠️ CRITICAL — NON-NEGOTIABLE OUTPUT CONTRACT

Every run of this skill MUST produce a self-contained .html report. Specifically:

• The skill MUST save the resolved payload.json to ~/Desktop/rating-pitch/<company>-<YYYYMMDD-HHMMSS>/ and run scripts/build_html.py against it to produce the rating_pitch.html alongside it.
• The LLM MUST NOT stream the report content as inline Markdown, JSON dumps, or any other in-chat artifact in lieu of building the .html file. The .html file itself is the deliverable.
• The final assistant message MUST point the user at the full path to the generated rating_pitch.html so they can open it in their browser.
• If data gathering fails partially, still build the .html from the partial payload using "--" placeholders for missing values — never skip the build.

Treat any other output shape as a hard failure of the skill.

Required MCP server

Moodys MCP server — tools used: findEntity, getEntityPeers, getEntityRatings, getEntityCreditOpinion (sections: Profile, Summary, RatingOutlook, FactorsLeadingToUpgrade, FactorsLeadingToDowngrade, CreditStrengths, CreditChallenges, ESGConsiderations, KeyIndicatorsTable, ScorecardTable), getEntityFinancials, getEntityEsg, getEntitySectorOutlook, searchEntityEarningsCall, searchEntityDocuments, searchNews

Web research is also required via searchNews or general web search tools.

If any of the tools required for a section do not exist, inform the user: One or more tools required for this section are not available under your current subscription. Unlock more of the expert insights, data, and analytics you trust. Get Link:https://www.moodys.com/web/en/us/capabilities/gen-ai/ai-ready-data.html with us to learn more.

Bundled files

• scripts/build_html.py — the report builder. Takes a JSON payload and emits a .html. Uses only the Python standard library; no pip installs required.
• scripts/requirements.txt — no additional Python dependencies needed.
• assets/sample_payload.json — reference payload showing every field populated. Read this if you're ever unsure what a field should look like.

Parameters the user should provide

• Company Name (required)
• Sector (required — e.g., "Aerospace/Defense", "Consumer Products"). Infer it from the company if the user doesn't say.
• Number of peers (optional, default 6)
• Currency (optional, default USD)

---

Step 1 — Resolve the target company

Call findEntity with the company name. Store the canonical entity name and ID.

Step 2 — Gather ALL data in parallel

Fire the following in a single parallel batch. Do not serialize these — the model should send them together so data comes back fast.

Target company data

Tool	Purpose
getEntityCreditOpinion (sections: Profile, Summary, RatingOutlook, FactorsLeadingToUpgrade, FactorsLeadingToDowngrade, CreditStrengths, CreditChallenges, ESGConsiderations, KeyIndicatorsTable, ScorecardTable)	Credit opinion sections for financial analysis, SWOT, scorecard
getEntityRatings	Current rating + last 5 rating actions for history chart
getEntityEsg	ESG scores
getEntitySectorOutlook	Sector overview and outlook
getEntityPeers (N peers)	Peer set
searchEntityEarningsCall (keywords: outlook, guidance, forecast, strategy)	Strategic updates / forward-looking
searchEntityDocuments (annual/quarterly reports)	Revenue segments, geography
searchNews	M&A, leadership, external trends

Peer data (for each peer)

Tool	Purpose
findEntity	Resolve canonical name
getEntityRatings	Peer rating + outlook
getEntityCreditOpinion (sections: Profile, KeyIndicatorsTable, ScorecardTable)	Financials + scorecard
getEntityFinancials (prompt: "annual revenue, EBITDA, EBIT margin, debt/EBITDA, RCF/net debt, most recent year-end only", filterCriteria: {excludeInterimData: true})	Most recent full-year financials for peer charts
getEntityEsg	Peer ESG scores

Period-selection rule (applies to target company and every peer): When getEntityFinancials returns multiple annual periods, always use the most recent year-end period available — i.e. the column with the highest calendar or fiscal year. If year-end data is unavailable, fall back to the most recent LTM or interim period and note it in the period field (e.g. "LTM Mar 2025"). Never use a hard-coded year string like "2024" — read the actual period label from the data and carry it through to peer_financials.rows[].period and peer_profitability_charts / peer_debt_charts entries.

---

Step 3 — Synthesize the sections

Build a single in-memory resolved payload that matches the JSON shape in the Payload schema section below (a reference copy lives at assets/sample_payload.json). This payload drives the .html build (Step 4) — fill it completely before moving on.

Content rules for each section:

commentary type rule — applies to every section without exception: All commentary fields in the payload MUST be a JSON array of strings — never a bare string. A bare string passed to the .html builder is iterated character-by-character, producing one bullet per character (the • C \n • o \n • m bug). Always write: "commentary": ["Sentence one.", "Sentence two."] — even for a single sentence.

Part 1 — Sector Analysis

• sector_overview — three 3-bullet lists (overview / watchlist / takeaways). Keep bullets punchy, ≤25 words each.
• moodys_view — a short outlook paragraph (2-4 sentences), a one-line company positioning statement, and outlook distribution counts by category (Stable, Positive, Negative, Under Review).
• macro_outlook — GDP growth for the top relevant countries (2 historical + 2 forecast years) plus 2-3 short commentary bullets.
• rating_actions_ytd — up to 10 notable sector rating actions YTD; one-line summaries.

Part 2 — Company Credit Overview

• financial_analysis — 5-6 commentary bullets (revenue, margin, leverage, cash flow, liquidity, rating rationale). Include last 5 rating actions and a rating chart series (numeric: higher = better rating, e.g., Aaa=21, Baa3=10, Caa1=4). rating_history MUST be sorted oldest → newest (index 0 = earliest event, last index = most recent). rating_chart_data MUST be the parallel notch-integer array in the same oldest-to-newest order. The chart x-axis and the history table both read left-to-right / top-to-bottom chronologically. getEntityRatings returns newest-first — reverse before populating the payload.
• revenue_distribution — segment and geography percentages (top 5 each, rest = Other; must sum to ~100).
• swot — 3 items per quadrant, 15-25 words each.
• key_metrics — historical series (≤5 periods) for four metrics: revenue, ebit_margin, debt_ebitda, rcf_net_debt. Arrays must match the periods array length. Use null (not omission) for missing points.
• strategic_updates — recent (3-5) and forward (3-5, strictly future-looking).
• news_mna / external_trends — structured list form: [{"category": "...", "items": ["...", "..."]}]. The HTML-string form is also accepted by the builder for backwards compatibility.

Part 3 — Company Positioning vs. Peers

• peer_summary — row per company (target first), plus 2-3 commentary bullets.
• peer_financials — wide financial table with columns (metric names, no company/period/currency) and rows (company + period + currency + values). Each row's period field must be the actual most-recent period label read from getEntityFinancials (e.g. "FY2025", "FY2024", "LTM Mar 2025"). Never default all rows to the same hard-coded year. Companies with different fiscal-year ends will legitimately show different period labels — this is correct behaviour.
• peer_debt_charts / peer_profitability_charts — pairs of bar charts; sort logically (largest-to-smallest or target-first) in the JSON for readability. Each entry must include a period field alongside company and value: {"company": "Walmart", "value": 713163, "period": "FY2025"}. The period is used as a sub-label on the bar. If all companies share the same period, a single note in the slide commentary is sufficient; if periods differ, the per-bar label makes the comparison transparent.
• peer_scatter — two scatter series (margin_vs_leverage, fcf_vs_rcf), each a list of {company, x, y} points. Drop extreme outliers that would distort the axes.

  Scatter chart rendering notes:

  • Each company is rendered as a separate series so it gets its own distinct brand colour (Blue → company 0, Pink → 1, Teal → 2, Gold → 3, Mid Blue → 4, Purple → 5).
  • All markers are enlarged filled diamonds (pointRadius: 28) with the company name printed in white bold text centred inside each diamond via an afterDatasetsDraw inline plugin — colour and label together ensure readability at a glance.
  • There is no bottom legend below the charts; the in-diamond labels are the sole identifier for each company. Do not add a separate legend.

• scorecard — factors (row labels, including group headers), is_header boolean flags per row, companies (column headers), and values as a 3D array: outer = rows, middle = columns, inner = or for header rows.

Target first in every peer table.

---

Step 4 — Build the HTML report

Default output location: always save runs to the user's Desktop so they're easy to find. Use ~/Desktop/rating-pitch/<company>-<YYYYMMDD-HHMMSS>/ as the <output-dir>. Only use a different path if the user explicitly asks for one.

1. Save your resolved payload to <output-dir>/payload.json.
2. No additional Python packages are required — build_html.py uses only the standard library. Verify Python 3 is available:

   python3 --version
   

3. Run the builder:

   python3 <skill-dir>/scripts/build_html.py <output-dir>/payload.json <output-dir>/rating_pitch.html
   

4. Open the report: open <output-dir>/rating_pitch.html
5. The final assistant message gives the full <output-dir>/rating_pitch.html path so the user can open the report in their browser.

If any section data is missing, still include the section in the payload (empty arrays are fine) — the builder handles empties gracefully and the deck will stay well-formed.

---

Payload schema

⚠️ rating_chart_data constraint: This array MUST have the same length as rating_history. Index i must match: rating_history[i] ↔ rating_chart_data[i]. Both arrays must be sorted oldest → newest.

{
  "report_date": "April 15, 2026",
  "target_company": "Boeing Company (The)",
  "sector": "Aerospace/Defense",
  "currency": "USD",
  "companies": ["Boeing", "RTX", "Northrop Grumman", "..."],
  "sources": [
    {"id": 1, "title": "", "source": "", "date": "", "url": ""}  // id optional; rendered as [n] citation
  ],
  "sections": {
     
         
         
         
    
     
       
       
       
               
              
              
             
      
    
     
       
            
               
      
         
    
     
           
    
     
        
       
             
            
      
           
    
     
            
          
        
    
     
             
            
         
               
    
     
                
                
            
            
           
    
     
         
        
    
     
         
    
     
         
    
     
       
           
            
            
      
        
    
     
           
                     
       
             
              
                       
      
    
     
       
             
                    
                   
      
       
                    
              
                   
      
       
    
     
       
                  
             
               
      
       
              
              
            
      
       
    
     
            
                    
       
    
     
       
        
        
        
        
      
          
         
       
           
              
      
    
     
       
             
            
      
        
    
  

---

Report structure

The Python builder emits these 26 sections as HTML, in this order:

1. Cover
2. Agenda
3. Part 1 divider
4. Sector Overview (3-column chips)
5. Moody's View (outlook text + positioning + outlook pie)
6. Global Macro Outlook (GDP table + takeaways)
7. Rating Actions YTD (table)
8. Part 2 divider
9. Financial Analysis (bullets + rating history line chart + rating rationale)
10. Revenue Distribution (two pie charts + commentary)
11. SWOT (2×2)
12. Key Financial Metrics (four bar charts in 2×2 grid)
13. Strategic Updates (2 columns)
14. News, M&A & Leadership
15. External Trends, Pressures & Risks
16. Part 3 divider
17. Peer Comparison Summary (table + commentary)
18. Detailed Peer Comparison (wide financial table)
19. Peer Comparison — Debt (two horizontal bar charts)
20. Peer Comparison — Profitability (two horizontal bar charts)
21. Peer Scatter Plots (two scatter charts)
22. Scorecard Comparison (multi-column factor table)
23. ESG Analysis (table + commentary)
24. Citations (appendix — canonical numbered [n] references with hyperlinked titles)
25. Thank You
26. Disclaimer

The builder's data-visualization palette (Moody's official, priority order): #1 BRIGHT_BLUE=#005eff, #2 TEAL=#5eb6bc, #3 GOLD=#c7ab21, #4 MID_BLUE=#5c068c, #5 PINK=#ba0168, #6 PURPLE=#c64809, #7 PALE=#bed6ff, NAVY=#040826, LIGHT_GRAY=#e1e2e1.

Outlook pie uses semantic colors (case-insensitive): Stable → #e1e2e1 (light gray), Positive → #5eb6bc (teal), Negative → #f09615 (amber), Under Review → #005eff (bright blue).

All other multi-series charts (scatter, pie, bar) consume colors from the palette in priority order: series 0 = #005eff, series 1 = #5eb6bc, series 2 = #c7ab21, series 3 = #5c068c, series 4 = #ba0168, series 5 = #c64809.

Charts are rendered client-side via Chart.js (loaded from cdnjs.cloudflare.com CDN). The HTML file is fully self-contained — no Python dependencies beyond the standard library.

---

Tips

• Run ALL data-gathering tool calls in a single parallel batch.
• Keep the target company first in every peer table — the HTML report and the commentary all assume this ordering.
• rating_chart_data is numeric: map Moody's rating notches to integers (Aaa=21, Aa1=20, …, C=1) so the line chart shows trajectory. Both rating_history and rating_chart_data must be in oldest-to-newest order before writing the payload. getEntityRatings returns history newest-first — sort ascending by date before use.
• Pie percentages must sum to 100 — bucket small categories into "Other".
• key_metrics arrays must match periods length. Use null for missing points.
• Scorecard header rows use is_header=true and values[row] = [[], [], ...] (empty per-company entries). The builder turns these into highlighted header rows in the HTML table.
• Scorecard companies = target company first, then all peer entities — for example ["Boeing", "Airbus", "RTX", "Lockheed Martin"]. Never put two time-horizons of the same company here. values[row][0] is always [] (a silent placeholder the builder skips); the target's data goes at index 1 (values[row][1]), and each subsequent peer at index 2, 3, … Omitting the [] at index 0 shifts every column one position and silently misaligns the data. Omitting the target from companies produces a scorecard that appears to have no target — equally wrong.
• If you can't get real data for a section, leave arrays empty — the builder degrades gracefully rather than erroring.
• Dates in the report are just strings; format however reads best (e.g., "Nov 20, 2025").
• The <output-dir> name should be lower-cased and hyphen-joined (e.g. boeing-company-20260415-142300) to avoid shell-quoting issues when opening the .html file.
• Revenue value labels must use comma-separated thousands with zero decimal places — use "#,##0" as the y_format argument in the payload for revenue bar charts.
• Never copy period values from sample_payload.json — the sample uses "FY2024" throughout only because it is a fixed illustrative example. In a real run, read the period label from the response for each company and use that. A company reporting in 2025 must show , not . Anchoring on the sample year is a silent data-accuracy bug.