version: "1.0.0" name: moody-s-rating-analysis description: > Produce a Rating Pitch Deck for a company using Moody's GenAI MCP tools, delivered as an editable PowerPoint (.pptx) saved to disk. Use this skill whenever the user asks to create a rating pitch, rating pitch deck, credit pitch, rating presentation, or rating pitch report. Also trigger when they ask for a comprehensive credit overview combining sector analysis, company financials, SWOT, peer comparison, and ESG into a single deck or presentation. Trigger even if they just name a company and say "pitch deck", "rating deck", or "credit deck".

Rating Pitch Skill

Generates a Moody's Rating Pitch Deck as an editable PowerPoint (.pptx) from a single MCP data pass. The Python builder (scripts/build_pptx.py) opens the official Moody's Corp 2026 PowerPoint template, removes its demo slides, and populates the template's built-in layouts (Cover, Agenda, Dividers, 1/2/3-column content, Back Cover, Disclaimer) with the resolved payload data. The builder produces native, editable PowerPoint charts and tables end-to-end — no HTML preview, no in-chat artifact.

## ⚠️ CRITICAL — NON-NEGOTIABLE OUTPUT CONTRACT

Every run of this skill MUST produce an editable .pptx. Specifically:

- The skill MUST save the resolved payload.json to

~/Desktop/rating-pitch/<company>-<YYYYMMDD-HHMMSS>/ and run scripts/build_pptx.py

against it to produce the editable rating_pitch.pptx alongside it.

- The LLM MUST NOT stream the deck content as inline HTML, Markdown, JSON dumps, or

any other in-chat artifact in lieu of building the .pptx. The .pptx itself is the

deliverable.

- The final assistant message MUST point the user at the full path to the generated

rating_pitch.pptx so they can open it.

- If data gathering fails partially, still build the .pptx 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_pptx.py — the deck builder. Takes a JSON payload and emits a .pptx.
• scripts/requirements.txt — Python dependencies (python-pptx). Unchanged.
• assets/Moody_Corp_Template.pptx — official Moody's 2026 Corp PowerPoint template.

The builder opens this file, clears its demo slides, and populates its layouts. Do not modify.

• 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

Peer data (for each peer)

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 .pptx 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 .pptx 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.

• 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 = [measure, score] or [] for header rows.

• esg_analysis — table of CIS/E/S/G scores plus 3-5 commentary bullets.

Target first in every peer table.

Step 4 — Build the editable .pptx

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. Ensure python-pptx is available. If the user doesn't have it:

``bash python3 -m venv .venv && .venv/bin/pip install -r <skill-dir>/scripts/requirements.txt ` or simply pip install python-pptx` if their environment allows it.

1. Run the builder:

``bash python3 <skill-dir>/scripts/build_pptx.py <output-dir>/payload.json <output-dir>/rating_pitch.pptx ``

1. Open the deck: open <output-dir>/rating_pitch.pptx
2. The final assistant message gives the full <output-dir>/rating_pitch.pptx path so the

user can open the editable deck.

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": [
    {"title": "", "source": "", "date": "", "url": "", "tool": ""}
  ],
  "sections": {
    "sector_overview": {
      "overview_bullets": ["...", "...", "..."],
      "watchlist_bullets": ["...", "...", "..."],
      "takeaway_bullets": ["...", "...", "..."]
    },
    "moodys_view": {
      "outlook_summary": "Two to four sentences (plain text or <p>...</p>).",
      "company_positioning": "One-line positioning statement.",
      "outlook_distribution": [
        {"category": "Stable",   "count": 11, "color": "#BDBFC3"},
        {"category": "Positive", "count": 5,  "color": "#5EB6BB"},
        {"category": "Negative", "count": 3,  "color": "#F09613"},
        {"category": "Under Review", "count": 1, "color": "#ED1B2E"}
      ]
    },
    "macro_outlook": {
      "gdp_table": {
        "year_columns": ["2023", "2024", "2025F", "2026F"],
        "rows": [{"country": "United States", "values": ["2.9", "2.8", "2.0", "1.8"]}]
      },
      "gdp_commentary": ["...", "...", "..."]
    },
    "rating_actions_ytd": [
      {"date": "Nov 20, 2025", "company": "...", "summary": "..."}
    ],
    "financial_analysis": {
      "commentary": ["...", "..."],
      "rating_history": [
        {"date": "Sep 2025", "rating": "Baa3", "outlook": "Negative",
         "direction": "Affirmation", "reason": "..."}
      ],
      "rating_chart_data": [8, 8, 7, 7, 7]
    },
    "revenue_distribution": {
      "by_segment":   [{"name": "Commercial Airplanes", "percentage": 45.2}],
      "by_geography": [{"name": "United States", "percentage": 55.0}],
      "commentary": ["...", "..."]
    },
    "swot": {
      "strengths":     ["...", "...", "..."],
      "weaknesses":    ["...", "...", "..."],
      "opportunities": ["...", "...", "..."],
      "threats":       ["...", "...", "..."]
    },
    "key_metrics": {
      "periods":      ["2021", "2022", "2023", "2024", "LTM Sep25"],
      "revenue":      [62286, 66608, 77794, 66517, 80757],
      "ebit_margin":  [-2.5, 4.1, -1.0, -16.1, -8.2],
      "debt_ebitda":  [-15.9, 8.5, 10.2, -6.8, -15.9],
      "rcf_net_debt": [-5.0, 10.1, 5.5, -8.3, -1.3]
    },
    "strategic_updates": {
      "recent":  ["...", "..."],
      "forward": ["...", "..."]
    },
    "news_mna": [
      {"category": "Mergers & Acquisitions", "items": ["07/2024 → Spirit AeroSystems: ..."]}
    ],
    "external_trends": [
      {"category": "Macro & Sector Trends", "items": ["..."]}
    ],
    "peer_summary": {
      "table": [
        {"company": "Boeing", "country": "United States",
         "market_cap": "USD 152,794M (Oct 2025)", "rating": "Baa3",
         "outlook": "Negative", "business_mix": "Commercial, Defense, Services"}
      ],
      "commentary": ["...", "..."]
    },
    "peer_financials": {
      "columns": ["Revenue", "EBITDA", "EBITDA Mg%", "CAPEX", "R&D/Rev",
                  "Debt/EBITDA", "FFO/Debt%", "FCF/Debt%", "RCF/Debt%"],
      "rows": [
        {"company": "Boeing", "period": "FY2024", "currency": "USD",
         "values": ["66,517", "(7,913)", "--", "(2,230)", "0.06",
                    "(6.81)", "(6.15)", "(26.57)", "(6.15)"]}
      ]
    },
    "peer_debt_charts": {
      "rcf_net_debt": [
        {"company": "Gen Dynamics", "value": 36.93, "period": "FY2024"},
        {"company": "Airbus",       "value": 32.0,  "period": "FY2024"},
        {"company": "Boeing",       "value": -6.15, "period": "FY2024"}
      ],
      "debt_ebitda": [
        {"company": "Airbus",       "value": 1.55,  "period": "FY2024"},
        {"company": "Gen Dynamics", "value": 1.62,  "period": "FY2024"},
        {"company": "Boeing",       "value": -6.81, "period": "FY2024"}
      ],
      "commentary": ["Two-sentence commentary."]
    },
    "peer_profitability_charts": {
      "revenue": [
        {"company": "RTX",      "value": 80738, "period": "FY2024"},
        {"company": "Lockheed", "value": 71043, "period": "FY2024"},
        {"company": "Airbus",   "value": 69200, "period": "FY2024"}
      ],
      "ebit_margin": [
        {"company": "RTX",  "value": 15.0, "period": "FY2024"},
        {"company": "BAE",  "value": 11.9, "period": "FY2024"},
        {"company": "Airbus","value": 10.7, "period": "FY2024"}
      ],
      "commentary": ["Two-sentence commentary."]
    },
    "peer_scatter": {
      "margin_vs_leverage": [{"company": "Boeing", "x": -6.81, "y": -16.1}],
      "fcf_vs_rcf":         [{"company": "Boeing", "x": -6.15, "y": -26.57}],
      "commentary": ["Two-sentence commentary."]
    },
    "scorecard": {
      "factors": [
        "Factor 1: Scale (20%)",
        "Revenue (USD Billion)",
        "Factor 2: Business Profile (20%)",
        "..."
      ],
      "is_header": [true, false, true, false],
      "companies": ["Boeing", "Airbus", "Lockheed"],
      "values": [
        [[], [], []],
        [[], ["73.3", "Aaa"], ["69.2", "Aaa"], ["71.0", "Aaa"]]
      ]
    },
    "esg_analysis": {
      "table": [
        {"company": "Boeing", "cis": "CIS-4", "environmental": "E-3",
         "social": "S-4", "governance": "G-4"}
      ],
      "commentary": ["...", "..."]
    }
  }
}

Deck structure

The Python builder emits these 23 slides, in this order:

1. Cover
2. Part 1 divider
3. Sector Overview (3-column chips)
4. Moody's View (outlook text + positioning + outlook pie)
5. Global Macro Outlook (GDP table + takeaways)
6. Rating Actions YTD (table)
7. Part 2 divider
8. Financial Analysis (bullets + rating history + rating trajectory line)
9. Revenue Distribution (two pies + commentary)
10. SWOT (2×2)
11. Key Financial Metrics (four bar charts)
12. Strategic Updates (2 columns)
13. News, M&A & Leadership
14. External Trends, Pressures & Risks
15. Part 3 divider
16. Peer Comparison Summary (table + commentary)
17. Detailed Peer Comparison (wide financial table)
18. Peer Comparison — Debt (two horizontal bar charts)
19. Peer Comparison — Profitability (two horizontal bar charts)
20. Peer Scatter Plots (two scatters)
21. Scorecard Comparison (multi-column factor table)
22. ESG Analysis (table + commentary)
23. Sources (appendix)

The builder's brand palette: NAVY=#0A1264, BRIGHT=#005EFF, MID=#35397E, PURPLE=#7677A7, PALE=#A2A2C4, VERY_PALE=#CFD0E1, PINK=#D9017A, TEAL=#4FB3AE, GOLD=#C4A51A, LIGHT_GRAY=#D7D8D7. Outlook pie palette (case-insensitive): Stable → LIGHT_GRAY, Positive → TEAL, Negative → GOLD, Under Review → BRIGHT.

Tips

• Run ALL data-gathering tool calls in a single parallel batch.
• Keep the target company first in every peer table — the .pptx 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 merged header rows in the .pptx.

• If you can't get real data for a section, leave arrays empty — the builder degrades

gracefully rather than erroring.

• Dates in the deck 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 .pptx.

• Revenue value labels must use comma-separated thousands with zero decimal places

(#,##0). Use "#,##0" as the y_format argument in add_bar_chart for the .pptx.

• 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 getEntityFinancials response for each company and use that. A company reporting in 2025 must show "FY2025", not "FY2024". Anchoring on the sample year is a silent data-accuracy bug.