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- thiagofernandes1987-create/APEX
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- 2026년 7월 21일 11:53
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설치 방법
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
소스 파일 검토
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
메뉴
기본적으로 소스를 먼저 확인하는 Prompt가 선택됩니다. 직접 명령으로 전환하거나 로컬 사본을 다운로드할 수도 있습니다.
설치 여부를 결정하기 전에 SKILL.md와 SkillsMP에 표시된 보조 파일을 읽어 보세요.
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
직접 명령은 검토 Prompt를 거치지 않습니다. 실행하기 전에 소스를 확인하세요.
npx skills add https://github.com/thiagofernandes1987-create/APEX --skill tear-sheet명령은 한 줄로 유지됩니다. 복사하기 전에 가로로 스크롤해 전체 내용을 확인하세요.
로컬 사본을 원하시나요? SkillsMP에서 현재 제공할 수 있는 파일을 다운로드하세요.
Token-aware reasoning workflow with real tools: picks an operating mode to control cost, runs a structured pipeline (decompose → validate → verify → snapshot), and gives Claude Program-of-Thought, RK4/Euler, a code gate, and a safe skill router. Use when: multi-step or high-stakes tasks, real math, precise computation, audits, or the user mentions APEX, PoT, pipeline, or scientific mode.
**v00.33.0**: Ingested from antigravity-awesome-skills community repo
run multiple local CLI agents in parallel (separate tmux sessions)
SOC 직업 분류 기준
SKILL.md 표시 중
| name | tear-sheet |
| description | "condition: Dados financeiros desatualizados ou ausentes" |
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({
: [
({
: companyName,
: ,
: ,
: .,
: ,
}),
],
: { : ., : , : . },
: { : },
: .,
});
() {
fields.(
({
: [
({ : f. + , : , : , : }),
({ : f., : , : }),
],
: { : },
})
);
}
noBorder = { : ., : , : };
noBorders = { : noBorder, : noBorder, : noBorder, : noBorder };
noShading = { : ., : , : };
kvTable = ({
: [
({
: [
({
: (leftFields),
: { : , : . },
: noBorders,
: noShading,
}),
({
: (rightFields),
: { : , : . },
: noBorders,
: noShading,
}),
],
}),
],
: { : , : . },
});
[banner, kvTable];
}
() {
({
: [
({
: text,
: ,
: ,
: .,
: ,
}),
],
: { : , : },
: {
: { : ., : , : . },
},
});
}
() {
fontSize = options. || ;
headerFill = options. ? . : .;
headerTextColor = options. ? . : ;
cellBorders = {
: { : ., : , : . },
: { : ., : , : . },
: { : ., : , : . },
: { : ., : , : . },
};
cellMargins = { : , : , : , : };
() {
( val !== ) ;
cleaned = val.(, ).();
cleaned !== && !(cleaned);
}
headerRow = ({
: headers.(
({
: [
({
: [
({
: h,
: ,
: fontSize,
: headerTextColor,
: ,
}),
],
}),
],
: { : ., : , : headerFill },
: cellBorders,
: cellMargins,
})
),
});
dataRows = rows.( {
fill = rowIdx % === ? . : ;
({
: row.( {
align = colIdx > && (cell)
? .
: .;
({
: [
({
: [
({ : cell, : fontSize, : }),
],
: align,
}),
],
: { : ., : , : fill },
: cellBorders,
: cellMargins,
});
}),
});
});
({
: [headerRow, ...dataRows],
: { : , : . },
});
}
() {
indent =
style ===
? { : , : }
: { : };
items.(
({
: [
({ : , : , : }),
({ : item, : , : }),
],
: indent,
: { : },
})
);
}
() {
({
: [
({
: [
({
: ,
: ,
: ,
: .,
: ,
}),
],
: .,
}),
({
: [
({
: ,
: ,
: ,
: .,
: ,
}),
],
: .,
}),
],
});
}
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.
Analyze —
Use this skill when the task requires tear sheet capabilities.