[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-openai-moody-s-rating-analysis":3,"mdc-cj9brw-key":36,"related-repo-openai-moody-s-rating-analysis":9429,"related-org-openai-moody-s-rating-analysis":9552},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":25,"repoUrl":26,"updatedAt":27,"license":28,"forks":29,"topics":30,"repo":31,"sourceUrl":34,"mdContent":35},"moody-s-rating-analysis","generate Moody's rating pitch reports","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\".\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"openai","OpenAI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fopenai.png",[12,16,19,22],{"name":13,"slug":14,"type":15},"Research","research","tag",{"name":17,"slug":18,"type":15},"Reporting","reporting",{"name":20,"slug":21,"type":15},"HTML","html",{"name":23,"slug":24,"type":15},"Finance","finance",3992,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins","2026-06-30T19:00:57.102",null,465,[],{"repoUrl":26,"stars":25,"forks":29,"topics":32,"description":33},[],"OpenAI Plugins","https:\u002F\u002Fgithub.com\u002Fopenai\u002Fplugins\u002Ftree\u002FHEAD\u002Fplugins\u002Fmoody-s\u002Fskills\u002Fmoody-s-rating-analysis","---\nname: moody-s-rating-analysis\ndescription: >\n Produce a Rating Pitch Report for a company using Moody's GenAI MCP tools, delivered as a\n self-contained HTML file saved to disk. Use this skill whenever the user asks to create\n a rating pitch, rating pitch deck, credit pitch, rating presentation, rating pitch\n report, or rating HTML report. Also trigger when they ask for a comprehensive credit\n overview combining sector analysis, company financials, SWOT, peer comparison, and ESG\n into a single report or presentation. Trigger even if they just name a company and say\n \"pitch deck\", \"rating deck\", \"credit deck\", or \"rating report\".\n---\n\n# Rating Pitch Skill\n\nGenerates a Moody's Rating Pitch Report as a self-contained HTML file from a single MCP\ndata pass. The Python builder (`scripts\u002Fbuild_html.py`) takes the resolved payload JSON and\nproduces a single `.html` file containing all sections with inline Chart.js charts, styled\ntables, and bullet lists using the Moody's brand palette — no external dependencies beyond\na browser to open it.\n\n> ## ⚠️ CRITICAL — NON-NEGOTIABLE OUTPUT CONTRACT\n>\n> Every run of this skill MUST produce a self-contained `.html` report. Specifically:\n>\n> - The skill **MUST** save the resolved `payload.json` to\n> `~\u002FDesktop\u002Frating-pitch\u002F\u003Ccompany>-\u003CYYYYMMDD-HHMMSS>\u002F` and run `scripts\u002Fbuild_html.py`\n> against it to produce the `rating_pitch.html` alongside it.\n> - The LLM **MUST NOT** stream the report content as inline Markdown, JSON dumps, or\n> any other in-chat artifact in lieu of building the `.html` file. The `.html` file itself\n> is the deliverable.\n> - The final assistant message **MUST** point the user at the full path to the generated\n> `rating_pitch.html` so they can open it in their browser.\n> - If data gathering fails partially, still build the `.html` from the partial payload\n> using `\"--\"` placeholders for missing values — never skip the build.\n>\n> Treat any other output shape as a hard failure of the skill.\n\n## Required MCP server\n\n`Moodys MCP server` — tools used: `findEntity`, `getEntityPeers`, `getEntityRatings`,\n`getEntityCreditOpinion` (sections: Profile, Summary, RatingOutlook,\nFactorsLeadingToUpgrade, FactorsLeadingToDowngrade, CreditStrengths, CreditChallenges,\nESGConsiderations, KeyIndicatorsTable, ScorecardTable), `getEntityFinancials`,\n`getEntityEsg`, `getEntitySectorOutlook`, `searchEntityEarningsCall`,\n`searchEntityDocuments`, `searchNews`\n\nWeb research is also required via searchNews or general web search tools.\n\nIf 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:\u002F\u002Fwww.moodys.com\u002Fweb\u002Fen\u002Fus\u002Fcapabilities\u002Fgen-ai\u002Fai-ready-data.html with us to learn more.\n\n\n## Bundled files\n\n- `scripts\u002Fbuild_html.py` — the report builder. Takes a JSON payload and emits a `.html`.\n Uses only the Python standard library; no pip installs required.\n- `scripts\u002Frequirements.txt` — no additional Python dependencies needed.\n- `assets\u002Fsample_payload.json` — reference payload showing every field populated. Read this\n if you're ever unsure what a field should look like.\n\n## Parameters the user should provide\n\n- **Company Name** (required)\n- **Sector** (required — e.g., \"Aerospace\u002FDefense\", \"Consumer Products\"). Infer it from\n the company if the user doesn't say.\n- **Number of peers** (optional, default 6)\n- **Currency** (optional, default USD)\n\n---\n\n## Step 1 — Resolve the target company\n\nCall `findEntity` with the company name. Store the canonical entity name and ID.\n\n## Step 2 — Gather ALL data in parallel\n\nFire the following in a **single parallel batch**. Do not serialize these — the model\nshould send them together so data comes back fast.\n\n### Target company data\n\n| Tool | Purpose |\n|------|---------|\n| `getEntityCreditOpinion` (sections: Profile, Summary, RatingOutlook, FactorsLeadingToUpgrade, FactorsLeadingToDowngrade, CreditStrengths, CreditChallenges, ESGConsiderations, KeyIndicatorsTable, ScorecardTable) | Credit opinion sections for financial analysis, SWOT, scorecard |\n| `getEntityRatings` | Current rating + last 5 rating actions for history chart |\n| `getEntityEsg` | ESG scores |\n| `getEntitySectorOutlook` | Sector overview and outlook |\n| `getEntityPeers` (N peers) | Peer set |\n| `searchEntityEarningsCall` (keywords: outlook, guidance, forecast, strategy) | Strategic updates \u002F forward-looking |\n| `searchEntityDocuments` (annual\u002Fquarterly reports) | Revenue segments, geography |\n| `searchNews` | M&A, leadership, external trends |\n\n### Peer data (for each peer)\n\n| Tool | Purpose |\n|------|---------|\n| `findEntity` | Resolve canonical name |\n| `getEntityRatings` | Peer rating + outlook |\n| `getEntityCreditOpinion` (sections: Profile, KeyIndicatorsTable, ScorecardTable) | Financials + scorecard |\n| `getEntityFinancials` (prompt: `\"annual revenue, EBITDA, EBIT margin, debt\u002FEBITDA, RCF\u002Fnet debt, most recent year-end only\"`, filterCriteria: `{excludeInterimData: true}`) | Most recent full-year financials for peer charts |\n| `getEntityEsg` | Peer ESG scores |\n\n**Period-selection rule (applies to target company and every peer):**\nWhen `getEntityFinancials` returns multiple annual periods, always use the\n**most recent year-end period available** — i.e. the column with the highest\ncalendar or fiscal year. If year-end data is unavailable, fall back to the most\nrecent LTM or interim period and note it in the `period` field (e.g. `\"LTM Mar 2025\"`).\nNever use a hard-coded year string like `\"2024\"` — read the actual period label\nfrom the data and carry it through to `peer_financials.rows[].period` and\n`peer_profitability_charts` \u002F `peer_debt_charts` entries.\n\n---\n\n## Step 3 — Synthesize the sections\n\nBuild a single in-memory **resolved payload** that matches the JSON shape in the **Payload\nschema** section below (a reference copy lives at `assets\u002Fsample_payload.json`). This\npayload drives the .html build (Step 4) — fill it completely before moving on.\n\nContent rules for each section:\n\n> **`commentary` type rule — applies to every section without exception:**\n> All `commentary` fields in the payload MUST be a **JSON array of strings** — never a\n> bare string. A bare string passed to the .html builder is iterated character-by-character,\n> producing one bullet per character (the `• C \\n • o \\n • m` bug). Always write:\n> `\"commentary\": [\"Sentence one.\", \"Sentence two.\"]` — even for a single sentence.\n\n### Part 1 — Sector Analysis\n\n- **sector_overview** — three 3-bullet lists (overview \u002F watchlist \u002F takeaways). Keep\n bullets punchy, ≤25 words each.\n- **moodys_view** — a short outlook paragraph (2-4 sentences), a one-line company\n positioning statement, and outlook distribution counts by category (Stable, Positive,\n Negative, Under Review).\n- **macro_outlook** — GDP growth for the top relevant countries (2 historical + 2\n forecast years) plus 2-3 short commentary bullets.\n- **rating_actions_ytd** — up to 10 notable sector rating actions YTD; one-line summaries.\n\n### Part 2 — Company Credit Overview\n\n- **financial_analysis** — 5-6 commentary bullets (revenue, margin, leverage, cash flow,\n liquidity, rating rationale). Include last 5 rating actions and a rating chart series\n (numeric: higher = better rating, e.g., Aaa=21, Baa3=10, Caa1=4).\n **`rating_history` MUST be sorted oldest → newest** (index 0 = earliest event,\n last index = most recent). `rating_chart_data` MUST be the parallel notch-integer\n array in the same oldest-to-newest order. The chart x-axis and the history table\n both read left-to-right \u002F top-to-bottom chronologically. `getEntityRatings` returns\n newest-first — reverse before populating the payload.\n- **revenue_distribution** — segment and geography percentages (top 5 each, rest = Other;\n must sum to ~100).\n- **swot** — 3 items per quadrant, 15-25 words each.\n- **key_metrics** — historical series (≤5 periods) for four metrics: revenue,\n ebit_margin, debt_ebitda, rcf_net_debt. Arrays must match the `periods` array length.\n Use `null` (not omission) for missing points.\n- **strategic_updates** — `recent` (3-5) and `forward` (3-5, strictly future-looking).\n- **news_mna** \u002F **external_trends** — structured list form:\n `[{\"category\": \"...\", \"items\": [\"...\", \"...\"]}]`. The HTML-string form is also accepted\n by the builder for backwards compatibility.\n\n### Part 3 — Company Positioning vs. Peers\n\n- **peer_summary** — row per company (target first), plus 2-3 commentary bullets.\n- **peer_financials** — wide financial table with `columns` (metric names, no\n company\u002Fperiod\u002Fcurrency) and `rows` (company + period + currency + values).\n Each row's `period` field **must be the actual most-recent period label read from\n `getEntityFinancials`** (e.g. `\"FY2025\"`, `\"FY2024\"`, `\"LTM Mar 2025\"`). Never\n default all rows to the same hard-coded year. Companies with different fiscal-year\n ends will legitimately show different period labels — this is correct behaviour.\n- **peer_debt_charts** \u002F **peer_profitability_charts** — pairs of bar charts; sort\n logically (largest-to-smallest or target-first) in the JSON for readability.\n Each entry **must include a `period` field** alongside `company` and `value`:\n `{\"company\": \"Walmart\", \"value\": 713163, \"period\": \"FY2025\"}`.\n The `period` is used as a sub-label on the bar. If all companies share the same\n period, a single note in the slide commentary is sufficient; if periods differ,\n the per-bar label makes the comparison transparent.\n- **peer_scatter** — two scatter series (`margin_vs_leverage`, `fcf_vs_rcf`), each a list\n of `{company, x, y}` points. Drop extreme outliers that would distort the axes.\n > **Scatter chart rendering notes:**\n > - Each company is rendered as a **separate series** so it gets its own distinct brand colour\n > (Blue → company 0, Pink → 1, Teal → 2, Gold → 3, Mid Blue → 4, Purple → 5).\n > - All markers are **enlarged filled diamonds** (`pointRadius: 28`) with the company name\n > printed in **white bold text centred inside** each diamond via an `afterDatasetsDraw`\n > inline plugin — colour and label together ensure readability at a glance.\n > - There is **no bottom legend** below the charts; the in-diamond labels are the sole\n > identifier for each company. Do not add a separate legend.\n- **scorecard** — `factors` (row labels, including group headers), `is_header` boolean\n flags per row, `companies` (column headers), and `values` as a 3D array: outer = rows,\n middle = columns, inner = `[measure, score]` or `[]` for header rows.\n > **SCORECARD CONTRACT — READ CAREFULLY:**\n > - `companies` must list **the target company first, followed by peer entities** (e.g.\n > `[\"Boeing\", \"Airbus\", \"RTX\", \"Lockheed Martin\"]`). Never put two time-horizons of the\n > same company here — that produces a scorecard with no peers. The first entry is the\n > target; its LTM scorecard data goes at `values[row][1]`.\n > - `values[row]` is **1-indexed against `companies`**: index `0` in every row is always `[]`\n > (a silent placeholder the builder skips). `companies[0]` maps to `values[row][1]`,\n > `companies[1]` maps to `values[row][2]`, and so on. Omitting the `[]` at index 0 will\n > shift every peer column one position and silently misalign the data.\n > - Header rows (`is_header=true`) use `values[row] = [[], [], [], ...]` — one `[]` per company\n > plus one for the placeholder. Length must equal `len(companies) + 1`.\n > - **Quick checklist before writing the scorecard payload:**\n > 1. `len(companies)` = number of peer entities (not counting the target).\n > 2. Every non-header `values[row]` has length `len(companies) + 1`.\n > 3. `values[row][0]` is always `[]`.\n > 4. `values[row][i+1]` contains `[\"metric_value\", \"ScoreLabel\"]` for `companies[i]`.\n- **esg_analysis** — table of CIS\u002FE\u002FS\u002FG scores plus 3-5 commentary bullets.\n\nTarget first in every peer table.\n\n---\n\n## Step 4 — Build the HTML report\n\n**Default output location: always save runs to the user's Desktop** so they're easy to\nfind. Use `~\u002FDesktop\u002Frating-pitch\u002F\u003Ccompany>-\u003CYYYYMMDD-HHMMSS>\u002F` as the `\u003Coutput-dir>`.\nOnly use a different path if the user explicitly asks for one.\n\n1. Save your resolved payload to `\u003Coutput-dir>\u002Fpayload.json`.\n2. No additional Python packages are required — `build_html.py` uses only the standard\n library. Verify Python 3 is available:\n ```bash\n python3 --version\n ```\n3. Run the builder:\n ```bash\n python3 \u003Cskill-dir>\u002Fscripts\u002Fbuild_html.py \u003Coutput-dir>\u002Fpayload.json \u003Coutput-dir>\u002Frating_pitch.html\n ```\n4. Open the report: `open \u003Coutput-dir>\u002Frating_pitch.html`\n5. The final assistant message gives the full `\u003Coutput-dir>\u002Frating_pitch.html` path so the\n user can open the report in their browser.\n\nIf any section data is missing, still include the section in the payload (empty arrays\nare fine) — the builder handles empties gracefully and the deck will stay well-formed.\n\n---\n\n## Payload schema\n\n> ⚠️ **`rating_chart_data` constraint:** This array MUST have the same length as\n> `rating_history`. Index `i` must match: `rating_history[i] ↔ rating_chart_data[i]`.\n> Both arrays must be sorted **oldest → newest**.\n\n```json\n{\n \"report_date\": \"April 15, 2026\",\n \"target_company\": \"Boeing Company (The)\",\n \"sector\": \"Aerospace\u002FDefense\",\n \"currency\": \"USD\",\n \"companies\": [\"Boeing\", \"RTX\", \"Northrop Grumman\", \"...\"],\n \"sources\": [\n {\"id\": 1, \"title\": \"\", \"source\": \"\", \"date\": \"\", \"url\": \"\"} \u002F\u002F id optional; rendered as [n] citation\n ],\n \"sections\": {\n \"sector_overview\": {\n \"overview_bullets\": [\"...\", \"...\", \"...\"],\n \"watchlist_bullets\": [\"...\", \"...\", \"...\"],\n \"takeaway_bullets\": [\"...\", \"...\", \"...\"]\n },\n \"moodys_view\": {\n \"outlook_summary\": \"Two to four sentences (plain text or \u003Cp>...\u003C\u002Fp>).\",\n \"company_positioning\": \"One-line positioning statement.\",\n \"outlook_distribution\": [\n {\"category\": \"Stable\", \"count\": 11, \"color\": \"#BDBFC3\"},\n {\"category\": \"Positive\", \"count\": 5, \"color\": \"#5EB6BB\"},\n {\"category\": \"Negative\", \"count\": 3, \"color\": \"#F09613\"},\n {\"category\": \"Under Review\", \"count\": 1, \"color\": \"#ED1B2E\"}\n ]\n },\n \"macro_outlook\": {\n \"gdp_table\": {\n \"year_columns\": [\"2023\", \"2024\", \"2025F\", \"2026F\"],\n \"rows\": [{\"country\": \"United States\", \"values\": [\"2.9\", \"2.8\", \"2.0\", \"1.8\"]}]\n },\n \"gdp_commentary\": [\"...\", \"...\", \"...\"]\n },\n \"rating_actions_ytd\": [\n {\"date\": \"Nov 20, 2025\", \"company\": \"...\", \"summary\": \"...\"}\n ],\n \"financial_analysis\": {\n \"commentary\": [\"...\", \"...\"],\n \"rating_history\": [\n {\"date\": \"Sep 2025\", \"rating\": \"Baa3\", \"outlook\": \"Negative\",\n \"direction\": \"Affirmation\", \"reason\": \"...\"}\n ],\n \"rating_chart_data\": [8, 8, 7, 7, 7]\n },\n \"revenue_distribution\": {\n \"by_segment\": [{\"name\": \"Commercial Airplanes\", \"percentage\": 45.2}],\n \"by_geography\": [{\"name\": \"United States\", \"percentage\": 55.0}],\n \"commentary\": [\"...\", \"...\"]\n },\n \"swot\": {\n \"strengths\": [\"...\", \"...\", \"...\"],\n \"weaknesses\": [\"...\", \"...\", \"...\"],\n \"opportunities\": [\"...\", \"...\", \"...\"],\n \"threats\": [\"...\", \"...\", \"...\"]\n },\n \"key_metrics\": {\n \"periods\": [\"2021\", \"2022\", \"2023\", \"2024\", \"LTM Sep25\"],\n \"revenue\": [62286, 66608, 77794, 66517, 80757],\n \"ebit_margin\": [-2.5, 4.1, -1.0, -16.1, -8.2],\n \"debt_ebitda\": [-15.9, 8.5, 10.2, -6.8, -15.9],\n \"rcf_net_debt\": [-5.0, 10.1, 5.5, -8.3, -1.3]\n },\n \"strategic_updates\": {\n \"recent\": [\"...\", \"...\"],\n \"forward\": [\"...\", \"...\"]\n },\n \"news_mna\": [\n {\"category\": \"Mergers & Acquisitions\", \"items\": [\"07\u002F2024 → Spirit AeroSystems: ...\"]}\n ],\n \"external_trends\": [\n {\"category\": \"Macro & Sector Trends\", \"items\": [\"...\"]}\n ],\n \"peer_summary\": {\n \"table\": [\n {\"company\": \"Boeing\", \"country\": \"United States\",\n \"market_cap\": \"USD 152,794M (Oct 2025)\", \"rating\": \"Baa3\",\n \"outlook\": \"Negative\", \"business_mix\": \"Commercial, Defense, Services\"}\n ],\n \"commentary\": [\"...\", \"...\"]\n },\n \"peer_financials\": {\n \"columns\": [\"Revenue\", \"EBITDA\", \"EBITDA Mg%\", \"CAPEX\", \"R&D\u002FRev\",\n \"Debt\u002FEBITDA\", \"FFO\u002FDebt%\", \"FCF\u002FDebt%\", \"RCF\u002FDebt%\"],\n \"rows\": [\n {\"company\": \"Boeing\", \"period\": \"FY2024\", \"currency\": \"USD\",\n \"values\": [\"66,517\", \"(7,913)\", \"--\", \"(2,230)\", \"0.06\",\n \"(6.81)\", \"(6.15)\", \"(26.57)\", \"(6.15)\"]}\n ]\n },\n \"peer_debt_charts\": {\n \"rcf_net_debt\": [\n {\"company\": \"Gen Dynamics\", \"value\": 36.93, \"period\": \"FY2024\"},\n {\"company\": \"Airbus\", \"value\": 32.0, \"period\": \"FY2024\"},\n {\"company\": \"Boeing\", \"value\": -6.15, \"period\": \"FY2024\"}\n ],\n \"debt_ebitda\": [\n {\"company\": \"Airbus\", \"value\": 1.55, \"period\": \"FY2024\"},\n {\"company\": \"Gen Dynamics\", \"value\": 1.62, \"period\": \"FY2024\"},\n {\"company\": \"Boeing\", \"value\": -6.81, \"period\": \"FY2024\"}\n ],\n \"commentary\": [\"Two-sentence commentary.\"]\n },\n \"peer_profitability_charts\": {\n \"revenue\": [\n {\"company\": \"RTX\", \"value\": 80738, \"period\": \"FY2024\"},\n {\"company\": \"Lockheed\", \"value\": 71043, \"period\": \"FY2024\"},\n {\"company\": \"Airbus\", \"value\": 69200, \"period\": \"FY2024\"}\n ],\n \"ebit_margin\": [\n {\"company\": \"RTX\", \"value\": 15.0, \"period\": \"FY2024\"},\n {\"company\": \"BAE\", \"value\": 11.9, \"period\": \"FY2024\"},\n {\"company\": \"Airbus\",\"value\": 10.7, \"period\": \"FY2024\"}\n ],\n \"commentary\": [\"Two-sentence commentary.\"]\n },\n \"peer_scatter\": {\n \"margin_vs_leverage\": [{\"company\": \"Boeing\", \"x\": -6.81, \"y\": -16.1}],\n \"fcf_vs_rcf\": [{\"company\": \"Boeing\", \"x\": -6.15, \"y\": -26.57}],\n \"commentary\": [\"Two-sentence commentary.\"]\n },\n \"scorecard\": {\n \"factors\": [\n \"Factor 1: Scale (20%)\",\n \"Revenue (USD Billion)\",\n \"Factor 2: Business Profile (20%)\",\n \"...\"\n ],\n \"is_header\": [true, false, true, false],\n \"companies\": [\"\u003CTARGET>\", \"\u003CPEER_1>\", \"\u003CPEER_2>\"],\n \"values\": [\n [[], [], [], []],\n [[], [\"\u003Ctarget_rev>\", \"\u003Ctarget_score>\"], [\"\u003Cpeer1_rev>\", \"\u003Cpeer1_score>\"], [\"\u003Cpeer2_rev>\", \"\u003Cpeer2_score>\"]]\n ]\n },\n \"esg_analysis\": {\n \"table\": [\n {\"company\": \"Boeing\", \"cis\": \"CIS-4\", \"environmental\": \"E-3\",\n \"social\": \"S-4\", \"governance\": \"G-4\"}\n ],\n \"commentary\": [\"...\", \"...\"]\n }\n }\n}\n```\n\n---\n\n## Report structure\n\nThe Python builder emits these 26 sections as HTML, in this order:\n\n1. Cover\n2. Agenda\n3. Part 1 divider\n4. Sector Overview (3-column chips)\n5. Moody's View (outlook text + positioning + outlook pie)\n6. Global Macro Outlook (GDP table + takeaways)\n7. Rating Actions YTD (table)\n8. Part 2 divider\n9. Financial Analysis (bullets + rating history line chart + rating rationale)\n10. Revenue Distribution (two pie charts + commentary)\n11. SWOT (2×2)\n12. Key Financial Metrics (four bar charts in 2×2 grid)\n13. Strategic Updates (2 columns)\n14. News, M&A & Leadership\n15. External Trends, Pressures & Risks\n16. Part 3 divider\n17. Peer Comparison Summary (table + commentary)\n18. Detailed Peer Comparison (wide financial table)\n19. Peer Comparison — Debt (two horizontal bar charts)\n20. Peer Comparison — Profitability (two horizontal bar charts)\n21. Peer Scatter Plots (two scatter charts)\n22. Scorecard Comparison (multi-column factor table)\n23. ESG Analysis (table + commentary)\n24. Citations (appendix — canonical numbered [n] references with hyperlinked titles)\n25. Thank You\n26. Disclaimer\n\nThe builder's data-visualization palette (Moody's official, priority order):\n`#1 BRIGHT_BLUE=#005eff`, `#2 TEAL=#5eb6bc`, `#3 GOLD=#c7ab21`, `#4 MID_BLUE=#5c068c`,\n`#5 PINK=#ba0168`, `#6 PURPLE=#c64809`, `#7 PALE=#bed6ff`, `NAVY=#040826`,\n`LIGHT_GRAY=#e1e2e1`.\n\nOutlook pie uses **semantic** colors (case-insensitive):\n`Stable → #e1e2e1` (light gray), `Positive → #5eb6bc` (teal),\n`Negative → #f09615` (amber), `Under Review → #005eff` (bright blue).\n\nAll other multi-series charts (scatter, pie, bar) consume colors from the palette in\npriority order: series 0 = `#005eff`, series 1 = `#5eb6bc`, series 2 = `#c7ab21`,\nseries 3 = `#5c068c`, series 4 = `#ba0168`, series 5 = `#c64809`.\n\nCharts are rendered client-side via Chart.js (loaded from cdnjs.cloudflare.com CDN).\nThe HTML file is fully self-contained — no Python dependencies beyond the standard library.\n\n---\n\n## Tips\n\n- Run ALL data-gathering tool calls in a single parallel batch.\n- Keep the target company first in every peer table — the HTML report and the\n commentary all assume this ordering.\n- `rating_chart_data` is numeric: map Moody's rating notches to integers (`Aaa=21, Aa1=20,\n …, C=1`) so the line chart shows trajectory. Both `rating_history` and\n `rating_chart_data` **must be in oldest-to-newest order** before writing the payload.\n `getEntityRatings` returns history newest-first — sort ascending by date before use.\n- Pie percentages must sum to 100 — bucket small categories into \"Other\".\n- `key_metrics` arrays must match `periods` length. Use `null` for missing points.\n- Scorecard header rows use `is_header=true` and `values[row] = [[], [], ...]` (empty\n per-company entries). The builder turns these into highlighted header rows in the HTML table.\n- **Scorecard `companies` = target company first, then all peer entities** — for\n example `[\"Boeing\", \"Airbus\", \"RTX\", \"Lockheed Martin\"]`. Never put two time-horizons\n of the same company here. `values[row][0]` is always `[]` (a silent placeholder the\n builder skips); the target's data goes at index 1 (`values[row][1]`), and each\n subsequent peer at index 2, 3, … Omitting the `[]` at index 0 shifts every column one\n position and silently misaligns the data. Omitting the target from `companies` produces\n a scorecard that appears to have no target — equally wrong.\n- If you can't get real data for a section, leave arrays empty — the builder degrades\n gracefully rather than erroring.\n- Dates in the report are just strings; format however reads best (e.g., \"Nov 20, 2025\").\n- The `\u003Coutput-dir>` name should be lower-cased and hyphen-joined (e.g.\n `boeing-company-20260415-142300`) to avoid shell-quoting issues when opening the\n `.html` file.\n- Revenue value labels must use comma-separated thousands with zero decimal places —\n use `\"#,##0\"` as the `y_format` argument in the payload for revenue bar charts.\n- **Never copy `period` values from `sample_payload.json`** — the sample uses\n `\"FY2024\"` throughout only because it is a fixed illustrative example. In a real\n run, read the period label from the `getEntityFinancials` response for each\n company and use that. 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In a real\nrun, read the period label from the ",{"type":42,"tag":57,"props":9403,"children":9405},{"className":9404},[],[9406],{"type":48,"value":267},{"type":48,"value":9408}," response for each\ncompany and use that. A company reporting in 2025 must show ",{"type":42,"tag":57,"props":9410,"children":9412},{"className":9411},[],[9413],{"type":48,"value":1179},{"type":48,"value":9415},", not\n",{"type":42,"tag":57,"props":9417,"children":9419},{"className":9418},[],[9420],{"type":48,"value":1186},{"type":48,"value":9422},". Anchoring on the sample year is a silent data-accuracy bug.",{"type":42,"tag":9424,"props":9425,"children":9426},"style",{},[9427],{"type":48,"value":9428},"html .light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html.light .shiki span {color: var(--shiki-light);background: var(--shiki-light-bg);font-style: var(--shiki-light-font-style);font-weight: var(--shiki-light-font-weight);text-decoration: var(--shiki-light-text-decoration);}html .default .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .shiki span {color: var(--shiki-default);background: var(--shiki-default-bg);font-style: var(--shiki-default-font-style);font-weight: var(--shiki-default-font-weight);text-decoration: var(--shiki-default-text-decoration);}html .dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}html.dark .shiki span {color: var(--shiki-dark);background: var(--shiki-dark-bg);font-style: var(--shiki-dark-font-style);font-weight: var(--shiki-dark-font-weight);text-decoration: var(--shiki-dark-text-decoration);}",{"items":9430,"total":9551},[9431,9449,9465,9477,9497,9519,9539],{"slug":9432,"name":9432,"fn":9433,"description":9434,"org":9435,"tags":9436,"stars":25,"repoUrl":26,"updatedAt":27},"accessibility-and-inclusive-visualization","make data visualizations accessible","Make data visualizations accessible and inclusive. Use when the user needs chart or diagram accessibility guidance, text alternatives for complex visuals, color and contrast review, keyboard support, reduced-motion behavior for animation or parallax, or an accessibility QA workflow for exported figures, UML-like diagrams, and dashboards.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9437,9440,9443,9446],{"name":9438,"slug":9439,"type":15},"Accessibility","accessibility",{"name":9441,"slug":9442,"type":15},"Charts","charts",{"name":9444,"slug":9445,"type":15},"Data Visualization","data-visualization",{"name":9447,"slug":9448,"type":15},"Design","design",{"slug":9450,"name":9450,"fn":9451,"description":9452,"org":9453,"tags":9454,"stars":25,"repoUrl":26,"updatedAt":9464},"agent-browser","automate browser interactions for agents","Browser automation CLI for AI agents. Use when the user needs to interact with websites, verify dev server output, test web apps, navigate pages, fill forms, click buttons, take screenshots, extract data, or automate any browser task. Also triggers when a dev server starts so you can verify it visually.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9455,9458,9461],{"name":9456,"slug":9457,"type":15},"Agents","agents",{"name":9459,"slug":9460,"type":15},"Browser Automation","browser-automation",{"name":9462,"slug":9463,"type":15},"Testing","testing","2026-04-06T18:41:03.44016",{"slug":9466,"name":9466,"fn":9467,"description":9468,"org":9469,"tags":9470,"stars":25,"repoUrl":26,"updatedAt":9476},"agent-browser-verify","verify dev server output with automated browser","Automated browser verification for dev servers. Triggers when a dev server starts to run a visual gut-check with agent-browser — verifies the page loads, checks for console errors, validates key UI elements, and reports pass\u002Ffail before continuing.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9471,9472,9475],{"name":9459,"slug":9460,"type":15},{"name":9473,"slug":9474,"type":15},"Local Development","local-development",{"name":9462,"slug":9463,"type":15},"2026-04-06T18:41:17.526867",{"slug":9478,"name":9478,"fn":9479,"description":9480,"org":9481,"tags":9482,"stars":25,"repoUrl":26,"updatedAt":9496},"agents-sdk","build AI agents on Cloudflare Workers","Build AI agents on Cloudflare Workers using the Agents SDK. Load when creating stateful agents, durable workflows, real-time WebSocket apps, scheduled tasks, MCP servers, or chat applications. Covers Agent class, state management, callable RPC, Workflows integration, and React hooks. Biases towards retrieval from Cloudflare docs over pre-trained knowledge.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9483,9484,9487,9490,9493],{"name":9456,"slug":9457,"type":15},{"name":9485,"slug":9486,"type":15},"Cloudflare Workers","cloudflare-workers",{"name":9488,"slug":9489,"type":15},"SDK","sdk",{"name":9491,"slug":9492,"type":15},"Serverless","serverless",{"name":9494,"slug":9495,"type":15},"WebSockets","websockets","2026-04-06T18:39:51.717063",{"slug":9498,"name":9498,"fn":9499,"description":9500,"org":9501,"tags":9502,"stars":25,"repoUrl":26,"updatedAt":9518},"ai-elements","build chat UIs with AI Elements","AI Elements component library guidance — pre-built React components for AI interfaces built on shadcn\u002Fui. Use when building chat UIs, message displays, tool call rendering, streaming responses, reasoning panels, or any AI-native interface with the AI SDK.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9503,9506,9509,9512,9515],{"name":9504,"slug":9505,"type":15},"Frontend","frontend",{"name":9507,"slug":9508,"type":15},"React","react",{"name":9510,"slug":9511,"type":15},"shadcn\u002Fui","shadcn-ui",{"name":9513,"slug":9514,"type":15},"UI Components","ui-components",{"name":9516,"slug":9517,"type":15},"Vercel","vercel","2026-04-06T18:40:59.619419",{"slug":9520,"name":9520,"fn":9521,"description":9522,"org":9523,"tags":9524,"stars":25,"repoUrl":26,"updatedAt":9538},"ai-gateway","configure Vercel AI Gateway","Vercel AI Gateway expert guidance. Use when configuring model routing, provider failover, cost tracking, or managing multiple AI providers through a unified API.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9525,9528,9531,9534,9537],{"name":9526,"slug":9527,"type":15},"AI Infrastructure","ai-infrastructure",{"name":9529,"slug":9530,"type":15},"Cost Optimization","cost-optimization",{"name":9532,"slug":9533,"type":15},"LLM","llm",{"name":9535,"slug":9536,"type":15},"Performance","performance",{"name":9516,"slug":9517,"type":15},"2026-04-06T18:40:44.377464",{"slug":9540,"name":9540,"fn":9541,"description":9542,"org":9543,"tags":9544,"stars":25,"repoUrl":26,"updatedAt":9550},"ai-generation-persistence","implement persistence patterns for AI generations","AI generation persistence patterns — unique IDs, addressable URLs, database storage, and cost tracking for every LLM generation",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9545,9546,9549],{"name":9529,"slug":9530,"type":15},{"name":9547,"slug":9548,"type":15},"Database","database",{"name":9532,"slug":9533,"type":15},"2026-04-06T18:41:08.513425",600,{"items":9553,"total":9750},[9554,9575,9598,9615,9631,9648,9667,9679,9693,9707,9719,9734],{"slug":9555,"name":9555,"fn":9556,"description":9557,"org":9558,"tags":9559,"stars":9572,"repoUrl":9573,"updatedAt":9574},"prior-auth-packet-builder","build healthcare prior authorization packets","Build a concise prior authorization packet from local case files and payer policy docs.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9560,9563,9566,9569],{"name":9561,"slug":9562,"type":15},"Documents","documents",{"name":9564,"slug":9565,"type":15},"Healthcare","healthcare",{"name":9567,"slug":9568,"type":15},"Insurance","insurance",{"name":9570,"slug":9571,"type":15},"Regulatory Compliance","regulatory-compliance",28169,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fopenai-agents-python","2026-04-16T05:11:39.180399",{"slug":9576,"name":9576,"fn":9577,"description":9578,"org":9579,"tags":9580,"stars":9595,"repoUrl":9596,"updatedAt":9597},"aspnet-core","build ASP.NET Core web applications","Build, review, refactor, or architect ASP.NET Core web applications using current official guidance for .NET web development. Use when working on Blazor Web Apps, Razor Pages, MVC, Minimal APIs, controller-based Web APIs, SignalR, gRPC, middleware, dependency injection, configuration, authentication, authorization, testing, performance, deployment, or ASP.NET Core upgrades.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9581,9584,9586,9589,9592],{"name":9582,"slug":9583,"type":15},".NET","dotnet",{"name":9585,"slug":9576,"type":15},"ASP.NET Core",{"name":9587,"slug":9588,"type":15},"Blazor","blazor",{"name":9590,"slug":9591,"type":15},"C#","csharp",{"name":9593,"slug":9594,"type":15},"Web Development","web-development",23787,"https:\u002F\u002Fgithub.com\u002Fopenai\u002Fskills","2026-04-12T05:07:02.819491",{"slug":9599,"name":9599,"fn":9600,"description":9601,"org":9602,"tags":9603,"stars":9595,"repoUrl":9596,"updatedAt":9614},"chatgpt-apps","build ChatGPT Apps SDK applications","Build, scaffold, refactor, and troubleshoot ChatGPT Apps SDK applications that combine an MCP server and widget UI. Use when Codex needs to design tools, register UI resources, wire the MCP Apps bridge or ChatGPT compatibility APIs, apply Apps SDK metadata or CSP or domain settings, or produce a docs-aligned project scaffold. Prefer a docs-first workflow by invoking the openai-docs skill or OpenAI developer docs MCP tools before generating code.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9604,9607,9610,9613],{"name":9605,"slug":9606,"type":15},"Apps SDK","apps-sdk",{"name":9608,"slug":9609,"type":15},"ChatGPT","chatgpt",{"name":9611,"slug":9612,"type":15},"MCP","mcp",{"name":9,"slug":8,"type":15},"2026-04-12T05:07:05.468097",{"slug":9616,"name":9616,"fn":9617,"description":9618,"org":9619,"tags":9620,"stars":9595,"repoUrl":9596,"updatedAt":9630},"cli-creator","build CLIs from API docs","Build a composable CLI for Codex from API docs, an OpenAPI spec, existing curl examples, an SDK, a web app, an admin tool, or a local script. Use when the user wants Codex to create a command-line tool that can run from any repo, expose composable read\u002Fwrite commands, return stable JSON, manage auth, and pair with a companion skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9621,9624,9627],{"name":9622,"slug":9623,"type":15},"API Development","api-development",{"name":9625,"slug":9626,"type":15},"CLI","cli",{"name":9628,"slug":9629,"type":15},"Codex","codex","2026-04-12T05:07:04.132762",{"slug":9632,"name":9632,"fn":9633,"description":9634,"org":9635,"tags":9636,"stars":9595,"repoUrl":9596,"updatedAt":9647},"cloudflare-deploy","deploy projects to Cloudflare","Deploy applications and infrastructure to Cloudflare using Workers, Pages, and related platform services. Use when the user asks to deploy, host, publish, or set up a project on Cloudflare.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9637,9640,9643,9644],{"name":9638,"slug":9639,"type":15},"Cloudflare","cloudflare",{"name":9641,"slug":9642,"type":15},"Cloudflare Pages","cloudflare-pages",{"name":9485,"slug":9486,"type":15},{"name":9645,"slug":9646,"type":15},"Deployment","deployment","2026-04-12T05:07:14.275118",{"slug":9649,"name":9649,"fn":9650,"description":9651,"org":9652,"tags":9653,"stars":9595,"repoUrl":9596,"updatedAt":9666},"define-goal","define and set measurable project goals","Help the user define a concrete, measurable goal before starting work, especially when they ask to use the goal tool, create a goal, set an objective, clarify success criteria, or turn a fuzzy intention into a quantitative outcome. Use this skill for goal creation and goal refinement only; it does not manage durable snapshots, decision logs, or long-running execution artifacts.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9654,9657,9660,9663],{"name":9655,"slug":9656,"type":15},"Productivity","productivity",{"name":9658,"slug":9659,"type":15},"Project Management","project-management",{"name":9661,"slug":9662,"type":15},"Strategy","strategy",{"name":9664,"slug":9665,"type":15},"Task Management","task-management","2026-05-23T06:17:16.870838",{"slug":9668,"name":9668,"fn":9669,"description":9670,"org":9671,"tags":9672,"stars":9595,"repoUrl":9596,"updatedAt":9678},"figma","translate Figma designs into code","Use the Figma MCP server to fetch design context, screenshots, variables, and assets from Figma, and to translate Figma nodes into production code. Trigger when a task involves Figma URLs, node IDs, design-to-code implementation, or Figma MCP setup and troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9673,9674,9676,9677],{"name":9447,"slug":9448,"type":15},{"name":9675,"slug":9668,"type":15},"Figma",{"name":9504,"slug":9505,"type":15},{"name":9611,"slug":9612,"type":15},"2026-04-12T05:06:47.939943",{"slug":9680,"name":9680,"fn":9681,"description":9682,"org":9683,"tags":9684,"stars":9595,"repoUrl":9596,"updatedAt":9692},"figma-code-connect-components","connect Figma designs to code components","Connects Figma design components to code components using Code Connect mapping tools. Use when user says \"code connect\", \"connect this component to code\", \"map this component\", \"link component to code\", \"create code connect mapping\", or wants to establish mappings between Figma designs and code implementations. For canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9685,9686,9689,9690,9691],{"name":9447,"slug":9448,"type":15},{"name":9687,"slug":9688,"type":15},"Design System","design-system",{"name":9675,"slug":9668,"type":15},{"name":9504,"slug":9505,"type":15},{"name":9513,"slug":9514,"type":15},"2026-05-10T05:59:52.971881",{"slug":9694,"name":9694,"fn":9695,"description":9696,"org":9697,"tags":9698,"stars":9595,"repoUrl":9596,"updatedAt":9706},"figma-create-design-system-rules","generate design system rules from Figma","Generates custom design system rules for the user's codebase. Use when user says \"create design system rules\", \"generate rules for my project\", \"set up design rules\", \"customize design system guidelines\", or wants to establish project-specific conventions for Figma-to-code workflows. Requires Figma MCP server connection.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9699,9700,9701,9704,9705],{"name":9447,"slug":9448,"type":15},{"name":9687,"slug":9688,"type":15},{"name":9702,"slug":9703,"type":15},"Documentation","documentation",{"name":9675,"slug":9668,"type":15},{"name":9504,"slug":9505,"type":15},"2026-05-16T06:07:47.821474",{"slug":9708,"name":9708,"fn":9709,"description":9710,"org":9711,"tags":9712,"stars":9595,"repoUrl":9596,"updatedAt":9718},"figma-implement-design","translate Figma designs into application code","Translates Figma designs into production-ready application code with 1:1 visual fidelity. Use when implementing UI code from Figma files, when user mentions \"implement design\", \"generate code\", \"implement component\", provides Figma URLs, or asks to build components matching Figma specs. For Figma canvas writes via `use_figma`, use `figma-use`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9713,9714,9715,9716,9717],{"name":9447,"slug":9448,"type":15},{"name":9675,"slug":9668,"type":15},{"name":9504,"slug":9505,"type":15},{"name":9513,"slug":9514,"type":15},{"name":9593,"slug":9594,"type":15},"2026-05-16T06:07:40.583615",{"slug":9720,"name":9720,"fn":9721,"description":9722,"org":9723,"tags":9724,"stars":9595,"repoUrl":9596,"updatedAt":9733},"hatch-pet","create animated pets for Codex","Create, repair, validate, visually QA, and package Codex-compatible animated pets and pet spritesheets from character art, generated images, company or prospect brand cues, or visual references. Use when a user wants a lightweight-worker Codex pet workflow, a non-pixel custom pet style, a prospect or company mascot pet, or a full 8x9 animated pet atlas with transparent unused cells, QA contact sheets, and pet.json packaging. This skill composes the installed $imagegen system skill for visual generation and uses bundled scripts for deterministic spritesheet assembly.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9725,9728,9729,9732],{"name":9726,"slug":9727,"type":15},"Animation","animation",{"name":9628,"slug":9629,"type":15},{"name":9730,"slug":9731,"type":15},"Creative","creative",{"name":9447,"slug":9448,"type":15},"2026-05-02T05:31:48.48485",{"slug":9735,"name":9735,"fn":9736,"description":9737,"org":9738,"tags":9739,"stars":9595,"repoUrl":9596,"updatedAt":9749},"imagegen","generate and edit raster images","Generate or edit raster images when the task benefits from AI-created bitmap visuals such as photos, illustrations, textures, sprites, mockups, or transparent-background cutouts. Use when Codex should create a brand-new image, transform an existing image, or derive visual variants from references, and the output should be a bitmap asset rather than repo-native code or vector. Do not use when the task is better handled by editing existing SVG\u002Fvector\u002Fcode-native assets, extending an established icon or logo system, or building the visual directly in HTML\u002FCSS\u002Fcanvas.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9740,9741,9742,9745,9748],{"name":9730,"slug":9731,"type":15},{"name":9447,"slug":9448,"type":15},{"name":9743,"slug":9744,"type":15},"Image Generation","image-generation",{"name":9746,"slug":9747,"type":15},"Images","images",{"name":9,"slug":8,"type":15},"2026-05-15T06:23:24.312127",675]