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 ...
Scanned 9/12/2026
Install to Claude Code
Skills Directorynpx -y skills add stanfish06/skillquarium --skill moody-s-rating-analysis --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Moody S Rating Analysis?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/stanfish06-moody-s-rating-analysis)More formats (shields.io, HTML) on the badges page.
---
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 = `[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:
```bash
python3 --version
```
3. Run the builder:
```bash
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**.
```json
{
"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.
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
