Longbridge Value Investing
helsome/folio
Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies.
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server.
$ npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install w95/awesome-claude-corporate-skills tear-sheet --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/02-finance-accounting/spglobal-tear-sheet .claude/skills/tear-sheet && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "tear-sheet" agent skill from https://github.com/w95/awesome-claude-corporate-skills/tree/main/02-finance-accounting/spglobal-tear-sheet into .claude/skills/tear-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tear-sheet", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/w95/awesome-claude-corporate-skills/tree/main/02-finance-accounting/spglobal-tear-sheetType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install w95/awesome-claude-corporate-skills tear-sheet --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/02-finance-accounting/spglobal-tear-sheet .agents/skills/tear-sheet && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "tear-sheet" agent skill from https://github.com/w95/awesome-claude-corporate-skills/tree/main/02-finance-accounting/spglobal-tear-sheet into .agents/skills/tear-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tear-sheet", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install w95/awesome-claude-corporate-skills tear-sheet --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/02-finance-accounting/spglobal-tear-sheet .cursor/skills/tear-sheet && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "tear-sheet" agent skill from https://github.com/w95/awesome-claude-corporate-skills/tree/main/02-finance-accounting/spglobal-tear-sheet into .cursor/skills/tear-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tear-sheet", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/w95/awesome-claude-corporate-skills.git --path 02-finance-accounting/spglobal-tear-sheet--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install w95/awesome-claude-corporate-skills tear-sheet --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/02-finance-accounting/spglobal-tear-sheet .gemini/skills/tear-sheet && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "tear-sheet" agent skill from https://github.com/w95/awesome-claude-corporate-skills/tree/main/02-finance-accounting/spglobal-tear-sheet into .gemini/skills/tear-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tear-sheet", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install w95/awesome-claude-corporate-skills tear-sheetInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/02-finance-accounting/spglobal-tear-sheet .github/skills/tear-sheet && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "tear-sheet" agent skill from https://github.com/w95/awesome-claude-corporate-skills/tree/main/02-finance-accounting/spglobal-tear-sheet into .github/skills/tear-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tear-sheet", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install w95/awesome-claude-corporate-skills tear-sheet --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/w95/awesome-claude-corporate-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/02-finance-accounting/spglobal-tear-sheet .opencode/skills/tear-sheet && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "tear-sheet" agent skill from https://github.com/w95/awesome-claude-corporate-skills/tree/main/02-finance-accounting/spglobal-tear-sheet into .opencode/skills/tear-sheet/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "tear-sheet", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
tear-sheetGenerate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server.
Tear Sheet is an agent skill from w95/awesome-claude-corporate-skills. Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial…
Its SKILL.md is about 7.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including reference files (for example `references/corp-dev.md`, `references/equity-research.md` and `references/ib-ma.md`).
It sits in Business, Finance & HR, covering Stock and market analysis, Sales call preparation and Accounting and bookkeeping. It works with Model Context Protocol. The repository describes itself as: 166 production-ready Claude AI skills organized by corporate role — executive leadership, finance, HR, marketing, sales, legal, operations, engineering, product, data, customer…. The licence is Apache-2.0.
4 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 78dbc7c. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
No scripts in the folder and no shell commands in SKILL.md (its code samples are javascript and bash).
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Tear Sheet loads about 7.8k tokens when it runs, and up to ~19k if it reads all its reference files. Until then it costs about 192 tokens; SKILL.md has 3,156 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.
The full file from w95/awesome-claude-corporate-skills at commit 78dbc7c, republished under its Apache-2.0 licence (© w95). 3,156 words, ~7,765 tokens.
.claude/skills/tear-sheet/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.Generate audience-specific company tear sheets by pulling live data from S&P Capital IQ via the S&P Global MCP tools and formatting the result as a professional Word document.
These are sensible defaults. To customize for your firm's brand, modify this section — common changes include swapping the color palette, changing the font (Calibri is standard at many banks), and updating the disclaimer text.
Colors:
Typography (sizes in half-points for docx-js):
Company Header Banner:
borders: none and shading: none on all cells. Set column widths to 50% each. Place left-column fields (ticker, HQ, founded, employees) as separate paragraphs in the left cell. Place right-column fields (market cap, EV, stock price, shares outstanding) in the right cell. Each field is a single paragraph: bold run for the label, regular run for the value.Section Headers:
paragraph.borders.bottom = { style: BorderStyle.SINGLE, size: 1, color: "CCCCCC" }. Do not use doc.addParagraph() with a separate horizontal rule element. Do not use thematicBreak. The border must be on the heading paragraph itself with 0pt spacing after, so the rule sits tight against the header text.Bullet Formatting:
Tables (financial data only):
Layout:
Number formatting:
Footer (document footer, not inline): Place the source attribution and disclaimer in the actual document footer (repeated on every page), not as inline body text at the bottom. The footer is exactly two lines, centered, on every page:
You MUST use these exact functions to create document elements. Do NOT write custom docx-js styling code. Copy these functions into your generated Node script and call them. The Style Configuration prose above remains as documentation; these functions are the enforcement mechanism.
const docx = require("docx");
const {
Document, Paragraph, TextRun, Table, TableRow, TableCell,
WidthType, AlignmentType, BorderStyle, ShadingType,
Header, Footer, PageNumber, HeadingLevel, TableLayoutType,
convertInchesToTwip
} = docx;
// ── Color constants ──
const COLORS = {
PRIMARY: "1F3864",
ACCENT: "2E75B6",
TABLE_HEADER_FILL: "D6E4F0",
TABLE_ALT_ROW: "F2F2F2",
TABLE_BORDER: "CCCCCC",
HEADER_TEXT: "FFFFFF",
FOOTER_TEXT: "666666",
};
const FONT = "Arial";
// ── 1. createHeaderBanner ──
// Returns an array of docx elements: [banner paragraph, key-value table]
function createHeaderBanner(companyName, leftFields, rightFields) {
// leftFields / rightFields: arrays of { label: string, value: string }
const banner = new Paragraph({
children: [
new TextRun({
text: companyName,
bold: true,
size: 36, // 18pt
color: COLORS.HEADER_TEXT,
font: FONT,
}),
],
shading: { type: ShadingType.CLEAR, color: "auto", fill: COLORS.PRIMARY },
spacing: { after: 0 },
alignment: AlignmentType.LEFT,
});
function buildCellParagraphs(fields) {
return fields.map(
(f) =>
new Paragraph({
children: [
new TextRun({ text: f.label + " ", bold: true, size: 18, font: FONT }),
new TextRun({ text: f.value, size: 18, font: FONT }),
],
spacing: { after: 40 },
})
);
}
const noBorder = { style: BorderStyle.NONE, size: 0, color: "FFFFFF" };
const noBorders = { top: noBorder, bottom: noBorder, left: noBorder, right: noBorder };
const noShading = { type: ShadingType.CLEAR, color: "auto", fill: "FFFFFF" };
const kvTable = new Table({
rows: [
new TableRow({
children: [
new TableCell({
children: buildCellParagraphs(leftFields),
width: { size: 50, type: WidthType.PERCENTAGE },
borders: noBorders,
shading: noShading,
}),
new TableCell({
children: buildCellParagraphs(rightFields),
width: { size: 50, type: WidthType.PERCENTAGE },
borders: noBorders,
shading: noShading,
}),
],
}),
],
width: { size: 100, type: WidthType.PERCENTAGE },
});
return [banner, kvTable];
}
// ── 2. createSectionHeader ──
// Returns a single Paragraph with bottom border rule
function createSectionHeader(text) {
return new Paragraph({
children: [
new TextRun({
text: text,
bold: true,
size: 22, // 11pt
color: COLORS.PRIMARY,
font: FONT,
}),
],
spacing: { before: 240, after: 0 }, // 12pt before, 0pt after
border: {
bottom: { style: BorderStyle.SINGLE, size: 1, color: COLORS.TABLE_BORDER },
},
});
}
// ── 3. createTable ──
// headers: string[], rows: string[][], options: { accentHeader?, fontSize? }
function createTable(headers, rows, options = {}) {
const fontSize = options.fontSize || 17; // 8.5pt default
const headerFill = options.accentHeader ? COLORS.ACCENT : COLORS.TABLE_HEADER_FILL;
const headerTextColor = options.accentHeader ? COLORS.HEADER_TEXT : "000000";
const cellBorders = {
top: { style: BorderStyle.SINGLE, size: 1, color: COLORS.TABLE_BORDER },
bottom: { style: BorderStyle.SINGLE, size: 1, color: COLORS.TABLE_BORDER },
left: { style: BorderStyle.SINGLE, size: 1, color: COLORS.TABLE_BORDER },
right: { style: BorderStyle.SINGLE, size: 1, color: COLORS.TABLE_BORDER },
};
const cellMargins = { top: 40, bottom: 40, left: 80, right: 80 };
function isNumeric(val) {
if (typeof val !== "string") return false;
const cleaned = val.replace(/[,$%()]/g, "").trim();
return cleaned !== "" && !isNaN(cleaned);
}
// Header row
const headerRow = new TableRow({
children: headers.map(
(h) =>
new TableCell({
children: [
new Paragraph({
children: [
new TextRun({
text: h,
bold: true,
size: fontSize,
color: headerTextColor,
font: FONT,
}),
],
}),
],
shading: { type: ShadingType.CLEAR, color: "auto", fill: headerFill },
borders: cellBorders,
margins: cellMargins,
})
),
});
// Data rows with alternating shading
const dataRows = rows.map((row, rowIdx) => {
const fill = rowIdx % 2 === 1 ? COLORS.TABLE_ALT_ROW : "FFFFFF";
return new TableRow({
children: row.map((cell, colIdx) => {
const align = colIdx > 0 && isNumeric(cell)
? AlignmentType.RIGHT
: AlignmentType.LEFT;
return new TableCell({
children: [
new Paragraph({
children: [
new TextRun({ text: cell, size: fontSize, font: FONT }),
],
alignment: align,
}),
],
shading: { type: ShadingType.CLEAR, color: "auto", fill: fill },
borders: cellBorders,
margins: cellMargins,
});
}),
});
});
return new Table({
rows: [headerRow, ...dataRows],
width: { size: 100, type: WidthType.PERCENTAGE },
});
}
// ── 4. createBulletList ──
// items: string[], style: "synthesis" | "informational"
function createBulletList(items, style = "synthesis") {
const indent =
style === "synthesis"
? { left: 360, hanging: 180 } // 360 DXA left, hanging indent for bullet
: { left: 180 }; // 180 DXA, no hanging
return items.map(
(item) =>
new Paragraph({
children: [
new TextRun({ text: "• ", font: FONT, size: 18 }),
new TextRun({ text: item, font: FONT, size: 18 }),
],
indent: indent,
spacing: { after: 60 },
})
);
}
// ── 5. createFooter ──
// date: string (e.g., "February 23, 2026")
function createFooter(date) {
return new Footer({
children: [
new Paragraph({
children: [
new TextRun({
text: `Data: S&P Capital IQ via Kensho | Analysis: AI-generated | ${date}`,
italics: true,
size: 14, // 7pt
color: COLORS.FOOTER_TEXT,
font: FONT,
}),
],
alignment: AlignmentType.CENTER,
}),
new Paragraph({
children: [
new TextRun({
text: "For informational purposes only. Not investment advice.",
italics: true,
size: 14,
color: COLORS.FOOTER_TEXT,
font: FONT,
}),
],
alignment: AlignmentType.CENTER,
}),
],
});
}Usage in generated scripts:
createHeaderBanner(...) instead of manually building banner paragraphs and tablescreateSectionHeader(...) for every section title — never manually set paragraph borderscreateTable(...) for all tabular data — financial summaries, trading comps, M&A activity, relationship tables, funding history, etc. Pass { accentHeader: true } for M&A activity tables (IB/M&A template). For non-numeric tables (e.g., relationships, ownership), the function still works correctly — it only right-aligns cells that contain numeric values.createBulletList(items, "synthesis") for earnings highlights, strategic fit, integration considerations, and conversation starterscreateBulletList(items, "informational") for relationship entriescreateFooter(date) to the Document constructor's footers.default propertyWhat these functions eliminate:
ShadingType.CLEAR everywhere)border.bottom on the paragraph itself)borders: none)• character only)Gather up to four things before proceeding:
If the user doesn't specify an audience, ask.
Read the corresponding reference file from this skill's directory:
references/equity-research.mdreferences/ib-ma.mdreferences/corp-dev.mdreferences/sales-bd.mdEach reference defines sections, a query plan, formatting guidance, and page length defaults.
First: Create the intermediate file directory:
mkdir -p /tmp/tear-sheet/Use the S&P Global MCP tools (also known as the Kensho LLM-ready API). Claude will have access to structured tools for financial data, company information, market data, consensus estimates, earnings transcripts, M&A transactions, and business relationships. The query plans in each reference file describe what data to retrieve for each section — map these to the appropriate S&P Global tools available in the conversation.
After each query step, immediately write the retrieved data to the intermediate file(s) specified in the reference file's query plan. Do not defer writes — data written to disk is protected from context degradation in long conversations.
Query strategy: Each reference file includes a query plan with 4-6 data retrieval steps. These are starting points, not rigid constraints. Prioritize data completeness over minimizing calls:
User-specified comps: If the user provided comparable companies, query financials and multiples for each comp explicitly. If no comps were provided, use whatever peer data the tools return, or identify peers from the company's sector using the competitors tool.
Optional context from the user: Listen for additional context the user provides naturally. If they mention who the acquirer is ("we're looking at this for our platform"), what they sell ("we sell data analytics to banks"), or who the likely buyers are ("this would be interesting to Salesforce or Microsoft"), incorporate that context into the relevant synthesis sections (Strategic Fit, Conversation Starters, Deal Angle). Don't prompt for this information — just use it if offered.
Private company handling: CIQ includes private company data, so query the same way. However, expect sparser results. When generating for a private company:
After all data collection is complete and intermediate files are written, compute all derived metrics in a single dedicated pass. This is a calculation-only step — no new MCP queries.
Read all intermediate files back into context, then compute:
Validation (moved from Arithmetic Validation): During this calculation pass, enforce all arithmetic checks:
If a validation fails: attempt recalculation from raw data. If still inconsistent, flag the metric as "N/A" rather than publishing incorrect numbers. Quiet math errors in a tear sheet destroy credibility.
Write results to /tmp/tear-sheet/calculations.csv with columns: metric,value,formula,components
Example rows:
metric,value,formula,components
gross_margin_fy2024,72.4%,gross_profit/revenue,"9524/13159"
revenue_growth_fy2024,12.3%,(current-prior)/prior,"13159/11716"
net_debt_fy2024,2150,total_debt-cash,"4200-2050"Before generating the document, verify that all intermediate files are present and populated.
Read each intermediate file via separate read operations and print a verification summary:
=== Tear Sheet Data Verification ===
company-profile.txt: ✓ (12 fields)
financials.csv: ✓ (36 rows)
segments.csv: ✓ (8 rows)
valuation.csv: ✓ (5 rows)
calculations.csv: ✓ (18 rows)
earnings.txt: ✓ (populated)
relationships.txt: ⚠ MISSING
peer-comps.csv: ✓ (12 rows)
================================Soft gate: If any file expected for the current audience type is missing or empty, print a warning but continue. The tear sheet handles missing data gracefully with "N/A" and section skipping. However, the warning ensures visibility into what data was lost.
Critical rule: The files — not your memory of earlier conversation — are the single source of truth for every number in the document. When generating the DOCX in Step 4, read values from the intermediate files. Do not rely on conversation context for financial data.
Read /mnt/skills/public/docx/SKILL.md for docx creation mechanics (docx-js via Node). Apply the Style Configuration above plus the section-specific formatting in the reference file.
Page length defaults (user can override):
If content exceeds the target, each reference file specifies which sections to cut first.
Output filename: [CompanyName]_TearSheet_[Audience]_[YYYYMMDD].docx
Example: Nvidia_TearSheet_CorpDev_20260220.docx
Save to /mnt/user-data/outputs/ and present to the user.
These override everything else:
All data retrieved from MCP tools must be persisted to structured intermediate files before document generation. These files — not conversation context — are the single source of truth for every number in the document.
Setup: At the start of Step 3, create the working directory:
mkdir -p /tmp/tear-sheet/Write-after-query mandate: After each MCP query step completes, immediately write the retrieved data to the appropriate intermediate file(s). Do not wait until all queries finish. Each reference file's query plan specifies which file(s) to write after each step.
File schemas:
| File | Format | Columns / Structure | Used By |
|---|---|---|---|
/tmp/tear-sheet/company-profile.txt | Key-value text | name, ticker, exchange, HQ, sector, industry, founded, employees, market_cap, enterprise_value, stock_price, 52wk_high, 52wk_low, shares_outstanding, beta, ownership | All |
/tmp/tear-sheet/financials.csv | CSV | period,line_item,value,source | All |
/tmp/tear-sheet/segments.csv | CSV | period,segment_name,revenue,source | ER, IB, CD |
/tmp/tear-sheet/valuation.csv | CSV | metric,trailing,forward,source | ER, IB, CD |
/tmp/tear-sheet/consensus.csv | CSV | metric,fy_year,value,source | ER |
/tmp/tear-sheet/earnings.txt | Structured text | Quarter, date, key quotes, guidance, key drivers | ER, IB, Sales |
/tmp/tear-sheet/relationships.txt | Structured text | Customers, suppliers, partners, competitors — each with descriptors | IB, CD, Sales |
/tmp/tear-sheet/peer-comps.csv | CSV | ticker,metric,value,source | ER, IB, CD |
/tmp/tear-sheet/ma-activity.csv | CSV | date,target,deal_value,type,rationale,source | IB, CD |
/tmp/tear-sheet/calculations.csv | CSV | metric,value,formula,components | All (written in Step 3b) |
Abbreviations: ER = Equity Research, IB = IB/M&A, CD = Corp Dev, Sales = Sales/BD.
Not every audience type uses every file — the reference files define which query steps apply. Files not relevant to the current audience type need not be created.
Raw values only. Intermediate files store raw values as returned by the tools. Do not pre-compute margins, growth rates, or other derived metrics in these files — that happens in Step 3b.
Page budget enforcement: Each reference file specifies a default page length and a numbered cut order. If the rendered document exceeds the target, apply cuts in the order specified — do not attempt to shrink font sizes or margins below the template minimums. The cut order is a strict priority stack: cut section 1 completely before touching section 2.
→ Arithmetic validation is now enforced in Step 3b (Calculate Derived Metrics). All margin calculations, growth rates, segment totals, percentage columns, and valuation cross-checks are validated during the dedicated calculation pass, before document generation begins. See Step 3b for the full validation checklist.
© w95, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files (references) in 02-finance-accounting/spglobal-tear-sheet of w95/awesome-claude-corporate-skills.
Open the folder on GitHubat commit 78dbc7c
We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in w95/awesome-claude-corporate-skills, which our catalogue first saw on October 7, 2026.
Tear Sheet next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Tear Sheet this skillw95/awesome-claude-corporate-skills | 237 | 1 repos | ~7.8k | Automated safety check: Pass | Apache-2.0 | |
| Longbridge Value Investinghelsome/folio | 269 | 2 repos | ~1.2k | Automated safety check: Pass | MIT | |
| Stock APIzhangxiangliang/stock-api | 2k | — | ~507 | Automated safety check: Pass | MIT | |
| Tradingview MCPatilaahmettaner/tradingview-mcp | 5k | — | ~1.3k | Automated safety check: Pass | MIT | |
| Agentic Trading DeskOft3r/agentic-trading-desk | 306 | — | ~5.1k | Automated safety check: Pass | MIT | |
| Earnings AnalysisWind-Alice/AliceMarket | 130 | 3 repos | ~2.2k | Automated safety check: Pass | None |
helsome/folio
Value investing analysis using Graham (NCAV/net-net/defensive-investor) and Buffett (economic moat/ROE/FCF) methodologies.
zhangxiangliang/stock-api
Fetch real-time stock quotes, K-line (candlestick) history, and search symbols for China A-shares, Hong Kong, and US markets.
atilaahmettaner/tradingview-mcp
AI Trading Intelligence — live prices, 30+ technical indicators, backtesting (6 strategies), walk-forward overfitting detection, trade logs, equity curves, licensed news sentiment (Marketaux), and…
Oft3r/agentic-trading-desk
Personal trading desk for short-term technical analysis on stocks/ETFs via Robinhood MCP.
Wind-Alice/AliceMarket
Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage.
ApocData/ApocData-skill
A-share data service with structured announcement parsing: every announcement carries an AI summary, category, importance level and sentiment, fully traceable to source.
w95/awesome-claude-corporate-skills
Generate or improve a company-specific data analysis skill by extracting tribal knowledge from analysts.
w95/awesome-claude-corporate-skills
Framework for competitive landscape analysis across any industry.
w95/awesome-claude-corporate-skills
Research a company using Common Room data. An agent skill from w95/awesome-claude-corporate-skills.
w95/awesome-claude-corporate-skills
Prepare for a customer or prospect call using Common Room signals.
w95/awesome-claude-corporate-skills
Generate personalized outreach messages using Common Room signals.
w95/awesome-claude-corporate-skills
Write correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.).
Works with
Categories
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Tear Sheet is an agent skill from w95/awesome-claude-corporate-skills. Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server.
Tear Sheet fits situations like: the user asks for a tear sheet; company one-pager; company profile; company snapshot.
Run `npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a claude-code`. Or copy the skill folder (02-finance-accounting/spglobal-tear-sheet in w95/awesome-claude-corporate-skills) into .claude/skills/tear-sheet in your project. Claude Code loads it when a task matches its description.
Run `npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a codex`. Or copy the skill folder (02-finance-accounting/spglobal-tear-sheet in w95/awesome-claude-corporate-skills) into .agents/skills/tear-sheet in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add w95/awesome-claude-corporate-skills --skill tear-sheet -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/tear-sheet, .gemini/skills/tear-sheet, .github/skills/tear-sheet and .opencode/skills/tear-sheet in your project.
SKILL.md names no scripts, command-line tools or credentials: Tear Sheet is instructions for the agent only.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.
Tear Sheet is published under the Apache-2.0 licence (from the LICENSE file in the skill folder). It allows redistribution, so the full SKILL.md is shown on this page.
About 7.8k tokens (SKILL.md is roughly 31k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 11k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Tear Sheet: Longbridge Value Investing (helsome/folio, 269 stars), Stock API (zhangxiangliang/stock-api, 2k stars), Tradingview MCP (atilaahmettaner/tradingview-mcp, 5k stars) and Agentic Trading Desk (Oft3r/agentic-trading-desk, 306 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
w95 (a GitHub user) maintains it in w95/awesome-claude-corporate-skills, which has 237 GitHub stars. The repository holds 39 skills in this directory. The repository was last updated on February 26, 2026.
Source: w95/awesome-claude-corporate-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.