moody-s-rating-analysis

# 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 | 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 `.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. 3. Run the builder: ```bash python3 <skill-dir>/scripts/build_pptx.py <output-dir>/payload.json <output-dir>/rating_pitch.pptx ``` 4. Open the deck: `open <output-dir>/rating_pitch.pptx` 5. 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**. ```json { "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}],