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

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

  SCORECARD CONTRACT — READ CAREFULLY:

  • companies must list the target company first, followed by peer entities (e.g. ["Boeing", "Airbus", "RTX", "Lockheed Martin"]). Never put two time-horizons of the same company here — that produces a scorecard with no peers. The first entry is the target; its LTM scorecard data goes at values[row][1].
  • values[row] is 1-indexed against companies: index 0 in every row is always [] (a silent placeholder the builder skips). companies[0] maps to values[row][1], companies[1] maps to values[row][2], and so on. Omitting the [] at index 0 will shift every peer column one position and silently misalign the data.
  • Header rows (is_header=true) use values[row] = [[], [], [], ...] — one [] per company plus one for the placeholder. Length must equal len(companies) + 1.
  • Quick checklist before writing the scorecard payload:
    1. len(companies) = number of peer entities (not counting the target).
    2. Every non-header values[row] has length len(companies) + 1.
    3. values[row][0] is always [].
    4. values[row][i+1] contains ["metric_value", "ScoreLabel"] for companies[i].

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

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": {
    "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": ["<TARGET>", "<PEER_1>", "<PEER_2>"],
      "values": [
        [[], [], [], []],
        [[], ["<target_rev>", "<target_score>"], ["<peer1_rev>", "<peer1_score>"], ["<peer2_rev>", "<peer2_score>"]]
      ]
    },
    "esg_analysis": {
      "table": [
        {"company": "Boeing", "cis": "CIS-4", "environmental": "E-3",
         "social": "S-4", "governance": "G-4"}
      ],
      "commentary": ["...", "..."]
    }
  }
}

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 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.