Catalyst Calendar

Build and maintain a calendar of upcoming catalysts across a coverage universe — earnings dates, conferences, product launches, regulatory decisions, and macro events.

Summary: An equity research workflow for tracking upcoming catalysts across a coverage universe. It covers earnings dates, corporate events, industry conferences, regulatory decisions, and macro events, and produces a sortable calendar view with weekly previews and positioning implications.

catalyst tracker
event calendar
earnings calendar
upcoming events

user-provided-data web-market-data bloomberg factset

The workflow does not explicitly require reading files, but archiving past catalysts may imply reading stored data.

The workflow outputs an Excel workbook and a weekly preview note.

The instructions reference verifying against company IR pages, Bloomberg, FactSet, and optional Google Calendar integration.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

Bloomberg and FactSet may require credentials, but the directory does not define a credential contract.

User-provided coverage universe

The workflow begins with a user-defined list of companies, sectors, and time horizon.

Tickers, sector focus, macro event inclusion, and time horizon supplied by the user.

Company IR pages

Used for verifying earnings dates and corporate events closer to the date.

Public investor relations pages for date verification.

Bloomberg market data

Named as a verification source for earnings dates and event data.

Professional market data terminal for verification.

FactSet market data

Professional market data platform for verification.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

Pineify Finance Skill snapshot assessment

External data and credential needs vary

Company IR pages, Bloomberg, FactSet, and Google Calendar are referenced, while credential requirements and provider availability are not fully specified.

Dependency manifest is absent from the snapshot

Excel workbook output is required but no requirements.txt or equivalent manifest is included in the captured directory.

Financial output needs independent review

The workflow produces positioning recommendations but includes no explicit investment advice boundary.

No formal validation checks

The directory includes no validation scripts or formal quality control beyond informal guidance notes.


Earnings Analysis

Create professional equity research earnings update reports (8-12 pages, 3,000-5,000 words) analyzing quarterly results for companies already under coverage.

Summary: An equity research workflow for producing institutional-grade earnings update reports. It covers beat/miss analysis, segment breakdowns, margin and guidance analysis, updated estimates, and revised investment thesis, following JPMorgan, Goldman Sachs, and Morgan Stanley format standards.

earnings update
quarterly update
post-earnings report
Q1 results
Q2 results
Q3 results
Q4 results

sec-edgar user-provided-data web-market-data bloomberg factset

Python

Python 3 with matplotlib, pandas, and seaborn

Required for chart generation and data analysis.

Used for chart generation via matplotlib, pandas, and seaborn.

DOCX skill

The workflow depends on a DOCX skill for report creation.

Required for report creation in DOCX format.

XLS skill

Optional for model updates accompanying earnings updates.

The workflow reads earnings releases, 10-Q filings, transcripts, and investor presentations from the web and local files.

The workflow outputs a DOCX report and optionally an XLS model update.

The workflow requires web search for latest earnings data, SEC EDGAR filings, and company investor pages.

Python scripts are used for chart generation via matplotlib, pandas, and seaborn.

Python code execution is required for chart generation and data analysis.

Bloomberg and FactSet are named as consensus estimate sources but credential requirements are not specified.

SEC EDGAR filings

10-Q filings are required for earnings analysis with EDGAR hyperlinks.

SEC EDGAR for 10-Q and other regulatory filings.

Company earnings releases

Earnings releases are mandatory data sources for the report.

Company investor relations pages for earnings releases.

Earnings call transcripts

Transcripts are required for qualitative analysis.

Earnings call transcripts from Seeking Alpha or similar platforms.

Bloomberg consensus estimates

Named as a consensus estimate source but availability and credentials are not specified.

Professional market data terminal for consensus estimates.

FactSet consensus estimates

Professional market data platform for consensus estimates.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

Python libraries are named but no requirements.txt or equivalent manifest is included in the captured directory.

Network access for latest earnings data

The workflow mandates web search for latest earnings data and SEC EDGAR filings, but no rate-limiting or error-handling guidance is provided.

Python code execution required

The workflow requires Python with matplotlib, pandas, and seaborn for chart generation, but no requirements.txt or pinned versions are included.

Credential handling for market data providers is unspecified

Bloomberg and FactSet are named as consensus estimate sources but credential requirements are not documented.

Financial advice boundary not stated

The workflow produces investment recommendations with ratings and price targets but includes no investment advice disclaimer.


Earnings Preview

Build pre-earnings analysis with estimate models, scenario frameworks, and key metrics to watch before a company reports quarterly earnings.

Summary: An equity research workflow for preparing pre-earnings analysis. It covers consensus estimate gathering, sector-specific key metrics frameworks, bull/base/bear scenario analysis with stock price implications, catalyst checklists, and trading setup with options-implied move.

pre-earnings
earnings setup
what to watch for earnings
preview Q1
preview Q2
preview Q3
preview Q4

The workflow does not explicitly require reading files, but reviewing prior quarter earnings calls may imply reading stored data.

The workflow outputs a one-page earnings preview note.

The workflow requires web search for consensus estimates, earnings dates, and historical earnings reactions.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

Consensus estimates and whisper numbers may come from paid sources, but no credential contract is specified.

Consensus estimates

Consensus estimates for revenue, EPS, and key segment metrics are required.

Consensus estimates sourced via web search from Bloomberg, FactSet, or similar platforms.

Earnings date and time

Earnings date and time (pre-market vs. after-hours) are required.

Earnings date and time from company IR pages or financial calendars.

Prior quarter earnings call

Prior quarter earnings call commentary and guidance are reviewed.

Prior quarter earnings call transcripts or summaries.

Options-implied move data

Options-implied move is recommended for comparing market expectations to scenarios.

Options-implied move from options market data providers.

External data and credential needs vary

Consensus estimates, whisper numbers, and options-implied move data are referenced, while credential requirements and provider availability are not fully specified.

No requirements.txt or equivalent manifest is included in the captured directory.

The workflow produces trading setup recommendations but includes no explicit investment advice boundary.

No formal validation checks


Idea Generation

Systematic stock screening and investment idea sourcing combining quantitative screens, thematic research, and pattern recognition to surface new long and short ideas.

Summary: An equity research workflow for generating investment ideas through quantitative screens (value, growth, quality, short, special situation), thematic sweeps, and pattern recognition. It produces a prioritized shortlist with one-page summaries, comparison tables, and suggested next steps for diligence.

stock screen
find ideas
what looks interesting
new ideas
pitch me something

The workflow does not require reading files from the filesystem.

The workflow outputs a shortlist of ideas with one-page summaries and a comparison table.

The workflow requires web search for quantitative screens, thematic research, ownership data, and short interest.

Screening data may come from paid sources such as Bloomberg or FactSet, but no credential contract is specified.

Market data for screens

Quantitative screens require P/E, EV/EBITDA, FCF yield, revenue growth, margins, and other financial metrics.

Market data for financial metrics from Bloomberg, FactSet, or web sources.

Ownership and short interest data

Recommended for avoiding crowded trades but not explicitly required.

Ownership data, short interest, and analyst coverage from market data providers.

Insider transaction data

Insider buying and selling are used in value and short screens but not explicitly required.

Insider transaction data from SEC filings or data aggregators.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

External data and credential needs vary

Quantitative screens, ownership data, and short interest may require paid data sources, while credential requirements and provider availability are not fully specified.

The workflow produces investment ideas with thesis and catalyst recommendations but includes no explicit investment advice boundary.

No formal validation checks


Initiating Coverage

Create comprehensive equity research initiation reports (30-50 pages, 10,000-15,000 words) for first-time coverage of a company, covering company research, financial modeling, valuation, chart generation, and report assembly.

Summary: An equity research workflow for initiating coverage on a company. It spans five tasks: company research, three-statement financial modeling, DCF and comps valuation, chart generation, and institutional-grade report assembly. The final deliverable is a publication-ready DOCX report at JPMorgan/Goldman Sachs quality standards.

initiation report
initiate coverage
first-time coverage
company initiation
start coverage

Python

Required for chart generation and data analysis in Task 4.

Used for chart generation via matplotlib, pandas, and seaborn.

DOCX skill

The workflow depends on a DOCX skill for final report creation in Task 5.

Required for report creation in DOCX format.

XLSX skill

The workflow depends on an XLSX skill for financial model creation in Task 2.

Required for financial model creation in XLSX format.

The workflow reads task outputs including research documents, financial models, valuation analyses, and chart files across tasks.

The workflow outputs research documents, XLSX financial models, PDF valuation analyses, PNG charts, and a final DOCX report.

The workflow requires web search for company research, SEC EDGAR filings, and market data.

Python scripts are used for chart generation via matplotlib, pandas, and seaborn in Task 4.

Python code execution is required for chart generation and data analysis in Task 4.

Bloomberg and FactSet are named as data sources but credential requirements are not specified.

SEC EDGAR filings

10-K and 10-Q filings are required for company research and financial modeling.

SEC EDGAR for regulatory filings and financial statements.

Company investor relations

Company IR pages for earnings releases, presentations, and guidance.

Company investor relations pages for corporate disclosures.

Market data for valuation

Market data for comparable company analysis and valuation multiples.

Market data from Bloomberg, FactSet, or web sources for valuation.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

Network access for company research and filings

The workflow mandates web search for company research and SEC EDGAR filings, but no rate-limiting or error-handling guidance is provided.

Python code execution required

Bloomberg and FactSet are named as data sources but credential requirements are not documented.

Financial advice boundary not stated


Model Update

Update financial models with new data — quarterly earnings, management guidance, macro changes, or revised assumptions. Adjusts estimates, recalculates valuation, and flags material changes.

Summary: An equity research workflow for updating financial models after earnings, guidance changes, or assumption revisions. It covers plugging actuals, revising forward estimates, recalculating DCF and multiples-based valuation, and producing an estimate change summary with updated price targets.

update model
plug earnings
refresh estimates
update numbers
new guidance
revise estimates

Excel model

The workflow requires an existing Excel model to update with new data.

An existing financial model in Excel format is required as input.

The workflow reads existing Excel models to update them with new data.

The workflow outputs an updated Excel model and an estimate change summary.

The workflow requires web search for earnings releases, guidance updates, and consensus estimates.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

Consensus estimates may come from paid sources such as Bloomberg or FactSet, but no credential contract is specified.

Earnings releases

Earnings releases provide new quarterly actuals to plug into the model.

Company earnings releases from IR pages or financial news.

Management guidance

Guidance updates are a trigger for model updates but may not always be available.

Company forward guidance from earnings calls or press releases.

Consensus estimates

Consensus comparison is recommended but not explicitly required.

Street consensus estimates from Bloomberg, FactSet, or similar platforms.

External data and credential needs vary

Earnings releases, guidance updates, and consensus estimates may require paid data sources, while credential requirements and provider availability are not fully specified.

The workflow produces updated price targets and rating recommendations but includes no explicit investment advice boundary.

No formal validation checks


Morning Note

Draft concise morning meeting notes summarizing overnight developments, trade ideas, and key events for coverage stocks. Tight, opinionated, actionable — designed for the 7am morning meeting format.

Summary: An equity research workflow for producing one-page morning meeting notes. It covers scanning overnight developments (earnings, news, market context), formatting a tight opinionated note with top call and trade ideas, and providing quick earnings reactions with rating and price target actions.

morning meeting
what happened overnight
trade idea
morning call prep
daily note

The workflow does not require reading files from the filesystem.

The workflow outputs a markdown text or Word document for morning meeting distribution.

The workflow requires web search for overnight developments, earnings releases, news, market data, and analyst actions.

Market data and consensus estimates may come from paid sources such as Bloomberg or FactSet, but no credential contract is specified.

Overnight news and earnings

The workflow requires scanning for overnight earnings, news, M&A, and analyst actions across the coverage universe.

Web sources for company news, earnings releases, and analyst actions.

Market data and futures

Overnight futures, pre-market moves, sector ETF performance, and commodity or currency moves are required for market context.

Market data from Bloomberg, FactSet, or web sources for futures and ETF performance.

Consensus estimates

Consensus estimates are used for earnings beat/miss comparison but may not always be available.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

External data and credential needs vary

Overnight developments, market data, and consensus estimates may require paid data sources, while credential requirements and provider availability are not fully specified.

The workflow produces trade ideas with rating and price target recommendations but includes no explicit investment advice boundary.

No formal validation checks

The directory includes no validation scripts or formal quality control beyond informal guidance notes.


Sector Overview

Create comprehensive industry and sector landscape reports covering market dynamics, competitive positioning, key players, and thematic trends. Use for client requests, sector initiations, thematic research, or internal knowledge building.

Summary: An equity research workflow for producing sector and industry landscape reports. It covers market sizing and growth, industry structure, competitive landscape with company profiles and valuation, investment implications, and chart-rich deliverables in Word, PowerPoint, and Excel formats.

industry report
market landscape
sector analysis
industry deep dive
thematic research

The workflow outputs a Word document or PowerPoint with an Excel appendix.

The workflow requires web search for market sizing data, company profiles, valuation multiples, M&A transactions, and regulatory developments.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

Market sizing data may come from paid research firms, and valuation data may require Bloomberg or FactSet, but no credential contract is specified.

Market sizing data

TAM, historical growth, forecast growth, and market segmentation data are required for the market overview.

Market size data from research firms, industry reports, or web sources.

Company financials and valuation

Revenue, growth, EBITDA margin, market share, P/E, EV/EBITDA, and EV/Revenue are required for the competitive landscape and valuation context.

Company financials and valuation multiples from Bloomberg, FactSet, or web sources.

M&A transaction data

Recent M&A transaction multiples are referenced for valuation context but may not always be available.

M&A transaction multiples from web sources or paid databases.

External data and credential needs vary

Market sizing data may come from paid research firms, and valuation and M&A data may require Bloomberg or FactSet, while credential requirements and provider availability are not fully specified.

The workflow produces investment implications and risk/reward recommendations but includes no explicit investment advice boundary.

No formal validation checks


Thesis Tracker

Maintain and update investment theses for portfolio positions and watchlist names. Track key data points, catalysts, and thesis milestones over time. Use when updating a thesis with new information, reviewing position rationale, or checking if a thesis is still intact.

Summary: An equity research workflow for maintaining and updating investment theses. It covers defining thesis statements with pillars, risks, catalysts, and target prices, maintaining a running scorecard with conviction levels, tracking disconfirming evidence, and producing thesis summaries for portfolio review or risk committee.

update thesis
thesis check
is my thesis still intact
add data point
review my positions

The workflow loads existing thesis data from structured files to update them.

The workflow stores thesis data in a structured format and outputs markdown or Word documents.

The workflow requires web search for new data points, earnings results, management changes, and competitor moves.

Market data and company information may come from paid sources such as Bloomberg or FactSet, but no credential contract is specified.

Company news and earnings

The workflow requires new data points from earnings results, management changes, competitor moves, and company developments.

Web sources for company news, earnings releases, and corporate events.

Market data

Market data may be needed for target price and valuation tracking but is not explicitly required.

Market data from Bloomberg, FactSet, or web sources for price and valuation tracking.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

External data and credential needs vary

New data points, earnings results, and company information may require paid data sources, while credential requirements and provider availability are not fully specified.

The workflow produces investment thesis summaries with conviction levels and position actions but includes no explicit investment advice boundary.

No formal validation checks


3-Statement Model

Complete and populate integrated 3-statement financial model templates (Income Statement, Balance Sheet, Cash Flow Statement) with proper linkages, formulas, and validation checks.

Summary: A 3-statement financial modeling workflow for completing integrated Income Statement, Balance Sheet, and Cash Flow Statement templates. It covers template structure analysis, formula integrity, cross-statement validation, scenario analysis, credit metrics, SEC filings data extraction, and staged user review at each statement.

three statement model
integrated financial model
is bs cf model

Python

Python 3 for standalone workbook generation

Office JS is an alternative when the skill runs inside Excel.

Required for the standalone openpyxl path.

openpyxl

Referenced in instructions but not declared in a manifest

Required only for standalone Python workbook generation.

Reads, writes, and populates Excel workbook templates.

recalc.py

The instructions make recalculation mandatory before delivery.

The helper is referenced but not included in the captured directory.

SEC EDGAR

Optional, for public company data extraction

Used only when the template requires SEC filings data.

Public filings source for historical financial data.

The workflow reads existing template structures and financial input data.

The workflow populates and writes Excel workbook templates.

The SEC filings reference instructs retrieving data from SEC EDGAR.

The standalone path requires running Python and recalc.py before delivery.

The workflow supports Python/openpyxl and Office JS execution paths.

No credential contract is defined; SEC EDGAR is public but optional data providers may need credentials.

User-provided financial data

Used when the template provides data directly or the user has preferred inputs.

Historical financials, assumptions, and template structure supplied by the user.

SEC EDGAR filings

Used only when the template specifically requires SEC filings data.

Public regulatory filings for historical financial data.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

Runs local code and writes workbooks

Installation should preserve an explicit approval boundary before Python, Office JS, or recalc.py runs.

openpyxl is referenced in the instructions but no requirements.txt or equivalent manifest is included in the captured directory.

Required recalculation helper is outside the snapshot

The instructions require recalc.py before delivery, but that file is not present in the captured directory.

External data extraction needs independent review

SEC EDGAR retrieval is referenced for public company data; credential requirements and provider availability are not fully specified.

The workflow contains strong model checks but no explicit investment advice boundary.


Audit Spreadsheet

Audit a spreadsheet for formula accuracy, errors, and common mistakes. Scopes to a selected range, a single sheet, or the entire model including financial-model integrity checks like BS balance, cash tie-out, and logic sanity.

Summary: A financial analysis workflow for auditing Excel spreadsheets. It covers formula-level checks (errors, hardcodes, inconsistent formulas, circular references), model-integrity checks (BS balance, cash tie-out, roll-forwards), and model-type-specific bugs for DCF, LBO, merger, and 3-statement models. Outputs a severity-ranked findings table.

audit this sheet
check my formulas
find formula errors
QA this spreadsheet
sanity check this
debug model
model check
model review

Excel spreadsheet

The workflow requires a user-provided spreadsheet file to audit.

An Excel workbook file is required as input for auditing.

The workflow reads spreadsheet files to audit formulas, check model integrity, and inspect cell values.

The workflow explicitly states it should not change anything without asking — report first, fix on request.

No network access or web search is required by the workflow.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

No external credentials or paid data sources are required for spreadsheet auditing.

User-provided spreadsheet

The workflow operates entirely on a user-provided Excel file.

The user provides the spreadsheet file to be audited.

No requirements.txt or equivalent manifest is included in the captured directory.

No financial advice boundary

The workflow audits financial models but includes no explicit investment advice disclaimer.


Clean Spreadsheet Data

Clean up messy spreadsheet data — trim whitespace, fix inconsistent casing, convert numbers-stored-as-text, standardize dates, remove duplicates, and flag mixed-type columns. Use when data is messy, inconsistent, or needs prep before analysis.

Summary: A financial analysis workflow for cleaning messy Excel spreadsheet data. It covers whitespace trimming, casing normalization, number-as-text conversion, date standardization, deduplication, encoding repair, and error flagging. Prefers transparent helper-column formulas over hardcoded values and requires user confirmation for destructive operations.

clean this data
clean up this sheet
normalize this data
fix formatting
dedupe
standardize this column

openpyxl

Python library for standalone .xlsx file processing

openpyxl is referenced for standalone .xlsx files but may not be installed in the user environment.

Python openpyxl library for reading and writing .xlsx files outside Excel.

The workflow reads spreadsheet data to profile columns, detect issues, and inspect cell values.

The workflow writes helper-column formulas and may overwrite in-place with computed values when explicitly requested.

The workflow references Python/openpyxl for standalone .xlsx files and Office JS for Excel add-in environments, but no code files are included in the captured directory.

No external credentials or paid data sources are required for spreadsheet data cleaning.

User-provided spreadsheet

The workflow operates entirely on a user-provided Excel file or range.

The user provides the spreadsheet data to be cleaned.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

No requirements.txt or equivalent manifest is included in the captured directory, though openpyxl is referenced in the instructions.

No financial advice boundary

The workflow prepares data for financial analysis but includes no explicit investment advice disclaimer.


Competitive Landscape Mapping

Framework for building competitive landscape decks — market positioning, competitor deep-dives, comparative analysis, strategic synthesis. Use when the user asks for a competitive landscape, competitor analysis, peer comparison, market positioning assessment, strategic review, or investment memo deck.

Summary: A financial analysis workflow for building competitive landscape decks. It covers industry-defining metrics, market context, industry economics, target company profiles, competitor mapping, positioning visualization, competitor deep-dives, comparative analysis, strategic context, and synthesis with moat assessment and optional bull/base/bear scenarios. Includes reference files for 2x2 matrix frameworks and table schemas.

who are the competitors to
benchmark against peers
build a market map
competitive landscape
competitor analysis
peer comparison
market positioning assessment
strategic review

PowerPoint or .pptx generator

PowerPoint add-in or .pptx file generation capability

The workflow builds slides directly in PowerPoint or generates .pptx files.

Works in PowerPoint add-in (Office JS) or chat (generates .pptx file).

The workflow reads uploaded Excel/CSV files with competitor data and may read existing .pptx files.

The workflow generates .pptx files or builds slides directly into an open PowerPoint deck.

The workflow references external sources (10-Ks, earnings calls, sell-side research, industry reports) for competitor data, but data may also come from user-provided files. Network access is implied but not explicitly required.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

Sell-side research and industry reports from McKinsey, Gartner may require paid subscriptions, but no credential contract is specified.

User-provided spreadsheet

The workflow accepts uploaded Excel/CSV with competitor data but data may also come from external sources.

User may upload Excel/CSV with competitor metrics.

SEC filings

10-Ks and annual reports are listed as the highest-quality source but may not be needed if user provides data.

10-Ks and annual reports for audited competitor metrics.

Market data and research

Sell-side research, industry reports, and market sizing data may be needed but are not explicitly required.

Earnings calls, sell-side research, industry reports (McKinsey, Gartner), and news for competitor data.

External data and credential needs vary

Competitor metrics, market sizing, and M&A data may require external sources including 10-Ks, sell-side research, and industry reports from McKinsey or Gartner. Credential requirements and provider availability are not fully specified.

Financial advice boundary is absent

The workflow produces bull/base/bear investment scenarios but includes no explicit investment advice disclaimer.


Comparable Company Analysis

Build institutional-grade comparable company analyses with operating metrics, valuation multiples, and statistical benchmarking in Excel/spreadsheet format. Perfect for public company valuation, benchmarking performance, pricing IPOs, and supporting investment committee presentations.

Summary: A financial analysis workflow for building institutional-grade comparable company analyses (comps) in Excel. It covers document structure, operating statistics (revenue, growth, margins, EBITDA), valuation multiples (EV/Revenue, EV/EBITDA, P/E), statistical benchmarking with quartiles, industry-specific metric selection, data source hierarchy (MCP first), formula transparency, and quality control with sanity checks and output checklists.

comparable company analysis
comps
peer analysis
trading comps
public comps
valuation multiples

daloopa sec-edgar user-provided-data web-market-data bloomberg factset

openpyxl

Python library for standalone .xlsx file generation

openpyxl is referenced for standalone .xlsx files but may not be installed in the user environment.

Python openpyxl library for generating Excel files with formulas and formatting.

The workflow reads uploaded template files and may read existing Excel files for data.

The workflow generates Excel/spreadsheet files with formulas, formatting, and cell comments.

The workflow references MCP data sources (S&P Kensho, FactSet, Daloopa) and Bloomberg Terminal, SEC EDGAR for financial data. Network access is implied for MCP sources but not explicitly required if user provides data.

The workflow references Office JS and Python/openpyxl for Excel generation but no code files are included in the captured directory.

MCP data sources (S&P Kensho, FactSet, Daloopa) and Bloomberg Terminal may require paid subscriptions, but no credential contract is specified.

MCP data sources

S&P Kensho MCP, FactSet MCP, or Daloopa MCP are preferred but may not be available in the user environment.

MCP data sources provide verified, institutional-grade data with proper citations.

SEC filings

SEC EDGAR filings are listed as a fallback data source when MCPs are unavailable.

10-K and 10-Q filings for audited financial data.

User-provided data

User may provide template files or data, but the workflow also pulls from external sources.

User-provided template files and competitor data.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

External data and credential needs vary

MCP data sources (S&P Kensho, FactSet, Daloopa), Bloomberg Terminal, and SEC EDGAR are referenced for financial data. Credential requirements and provider availability are not fully specified.

Referenced example file is not included

The instructions reference examples/comps_example.xlsx but the file is not present in the captured directory.

Financial advice boundary is absent

The workflow produces valuation analysis for investment decisions but includes no explicit investment advice disclaimer.


DCF Model

Build an Excel discounted cash flow valuation with WACC, scenario analysis, formula checks, and source notes.

Summary: A detailed DCF modeling workflow for public company valuation. It covers financial data collection, cash flow forecasts, WACC, terminal value, sensitivity tables, workbook checks, and staged user review.

discounted cash flow model
intrinsic value model
company valuation model

dcf-valuation wacc-analysis cash-flow-forecasting sensitivity-analysis excel-financial-modeling

daloopa sec-edgar user-provided-data web-market-data

Python

Office JS is an alternative when the skill runs inside Excel.

Required for the standalone openpyxl path.

openpyxl

Required only for standalone Python workbook generation.

Reads, writes, and validates Excel workbooks.

requests

The manifest lists requests, while exact usage depends on data sourcing.

Supports HTTP data retrieval in the Python path.

recalc.py

The instructions make recalculation mandatory before delivery.

The helper is referenced but not included in the captured directory.

Financial data provider

Optional MCP provider, web source, or user data

The chosen workflow determines which provider or credentials are needed.

Examples include Daloopa, SEC filings, and web market data.

The workflow reads financial inputs and generated workbooks.

The workflow creates Excel models and may write validation JSON.

The instructions allow MCP and web data retrieval.

The delivery checklist requires Python commands.

The directory includes a Python validator and instructs model generation.

Optional MCP providers may require credentials, but the directory does not define a credential contract.

User-provided financial data

Used when structured sources are unavailable or the user has preferred inputs.

Historical financials and estimates supplied by the user.

SEC EDGAR filings

Primary filings for historical financial data.

MCP financial data providers

Used only when a compatible provider is configured.

Web market data

Used for current prices, beta, debt, cash, or related inputs when needed.

Current values need date and source checks.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

External data and credential needs vary

MCP and web sources are allowed, while credential requirements and provider availability are not fully specified.

Runs local code and writes workbooks

Installation should preserve an explicit approval boundary before Python or Office JS runs.


Deck Refresh

Updates a presentation with new numbers — quarterly refreshes, earnings updates, comp rolls, rebased market data. Use whenever the user asks to update the deck with new figures, refresh comps, roll forward, or swap values across an existing deck without rebuilding it.

Summary: A financial analysis workflow for updating numbers across existing presentation decks. It covers four phases: getting new data from user mappings or uploaded files, reading every slide to find all number variants, presenting a change plan for approval, and executing with formatting preservation. Handles scale variants, precision variants, unit style variants, embedded numbers in charts and footnotes, and flags derived numbers that may become stale.

update the deck
refresh the comps
roll this forward
swap in the new earnings
deck update
presentation refresh

PowerPoint or .pptx editor

PowerPoint add-in or .pptx file editing capability

The workflow edits slides directly in PowerPoint or regenerates and writes back .pptx files.

Works in PowerPoint add-in (Office JS) or chat (regenerate and write back .pptx file).

The workflow reads the existing deck file and any uploaded Excel files with new data.

The workflow edits the deck in-place (add-in) or regenerates affected slides and writes the result back (chat).

No network access is required or referenced. All data comes from user-provided mappings or uploaded files.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

No external credentials or paid data sources are referenced.

User-provided mapping

The workflow requires user-provided old-to-new value mappings, either pasted, uploaded as Excel, or stated as new values.

User provides old-to-new value mappings via paste, uploaded Excel, or direct statement.

Financial advice boundary is absent

The workflow updates financial figures in presentation decks but includes no explicit investment advice disclaimer.


IB Deck Checker

Investment banking presentation quality checker. Reviews a pitch deck or client-ready presentation for number consistency, data-narrative alignment, language polish against IB standards, and visual and formatting QC. Use whenever the user asks to review, check, QC, proof, or do a final pass on a deck or client materials.

Summary: A financial analysis workflow for quality-checking investment banking presentations. It covers four QC dimensions: number consistency (with an automated Python extraction script that normalizes units and flags conflicts), data-narrative alignment (verifying claims against supporting data), language polish (IB terminology standards with replacement patterns), and visual formatting QC. Output is a structured report with severity classification (critical, important, minor). Includes reference files for IB terminology and report format.

what am I missing before I send this out

Python 3

Python 3 runtime for executing extract_numbers.py

The workflow runs scripts/extract_numbers.py via shell to extract and check numbers.

Python 3 with standard library only (argparse, json, re, sys, collections, dataclasses, pathlib, typing). No third-party packages required.

The workflow reads the presentation file (.pptx) and writes extracted text to a temporary file for the number-checking script.

The workflow writes extracted deck text to a temporary file (e.g. /tmp/deck_content.md) for the extraction script to process.

No network access is required. The workflow operates on the uploaded or open deck file only.

The workflow runs a Python script (scripts/extract_numbers.py) via shell to extract and check numbers from the deck content.

A Python script (scripts/extract_numbers.py, 306 lines) is included in the captured directory and executed as part of the workflow.

User-provided presentation

The workflow requires a presentation file (.pptx) to review.

User provides the pitch deck or client-ready presentation for QC review.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

Included Python script requires execution

The skill includes a 306-line Python script (scripts/extract_numbers.py) that is executed as part of the workflow. The script uses only Python standard library modules but must be reviewed before installation.

No requirements.txt or equivalent manifest is included in the captured directory, though Python 3 is required to run the extraction script.

Financial advice boundary is absent

The workflow checks IB presentation quality but includes no explicit investment advice disclaimer.


LBO Model

Completes LBO (Leveraged Buyout) model templates in Excel for private equity transactions, deal materials, or investment committee presentations. Fills in formulas, validates calculations, and ensures professional formatting standards that adapt to any template structure.

Summary: A financial analysis workflow for building institutional-grade LBO models in Excel. It covers template analysis, section-by-section formula filling (Sources & Uses, Operating Model, Debt Schedule, Returns Analysis, Sensitivity Tables), formula color conventions (blue/black/purple/green), fill color palette, circular reference handling with beginning balances, debt paydown waterfalls, IRR/MOIC returns with correct signs, odd-dimension sensitivity tables, section-by-section user checkpoints, and a comprehensive verification checklist.

lbo
leveraged buyout model
pe model
buyout model
sources and uses
deal model

openpyxl

openpyxl is referenced for standalone .xlsx files but may not be installed in the user environment.

Python openpyxl library for generating Excel files with formulas and formatting.

recalc.py

Formula validation script for recalculation

A recalc.py script is referenced at /mnt/skills/public/xlsx/recalc.py for formula validation but is not included in the captured directory.

External formula validation script that must return success with zero errors.

The workflow reads template files (e.g. examples/LBO_Model.xlsx) and user-provided templates.

The workflow generates Excel model files with formulas, formatting, and cell color conventions.

No network access is required or referenced. All data comes from user-provided assumptions and templates.

The workflow references running a Python recalc script (recalc.py) for formula validation after model completion.

The workflow references Python/openpyxl for standalone .xlsx generation and a recalc.py script for formula validation. No code files are included in the captured directory.

No external credentials or paid data sources are referenced.

User-provided template

The workflow requires a template file (user-provided or standard LBO_Model.xlsx) and user assumptions.

User provides an LBO model template file and transaction assumptions.

No requirements.txt or equivalent manifest is included in the captured directory, though Python/openpyxl and a recalc.py script are referenced.

Referenced template and validation script are not included

The instructions reference examples/LBO_Model.xlsx as a standard template and /mnt/skills/public/xlsx/recalc.py for formula validation, but neither file is present in the captured directory.

Financial advice boundary is absent

The workflow produces LBO models for investment committee presentations but includes no explicit investment advice disclaimer.


Buyer List

Build and organize a universe of potential acquirers for sell-side M&A processes. Identifies strategic and financial buyers, assesses fit, and prioritizes outreach. Use when preparing for a sell-side mandate, building a buyer universe, or evaluating potential partners.

Summary: An investment banking workflow for building buyer universes in sell-side M&A. It covers six steps: understanding the target, identifying strategic buyers (direct competitors, adjacent players, vertical integrators, platform builders), identifying financial sponsors (platform investors, add-on buyers, growth equity), tiered prioritization, contact mapping for Tier 1, and producing an Excel workbook with buyer tabs, contact mapping, and summary statistics.

buyer universe
potential acquirers
who would buy this
strategic buyers
financial sponsors
buyer screening

No filesystem read access is required. The workflow operates on user-provided company information and market knowledge.

The workflow outputs an Excel workbook with strategic buyers, financial sponsors, contact mapping, and summary statistics.

No network access is required or referenced. The workflow relies on user-provided information and the agent knowledge base.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

User-provided company information

The workflow requires company description, sector, financials, key assets, expected valuation, and seller preferences.

User provides target company information and sell-side process parameters.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

No requirements.txt or equivalent manifest is included in the captured directory.

Financial advice boundary is absent

The workflow builds buyer universes for sell-side M&A processes but includes no explicit investment advice disclaimer.


CIM Builder

Structure and draft a Confidential Information Memorandum for sell-side M&A processes. Organizes company information into a professional, investor-ready document with consistent formatting and narrative flow. Use when preparing sell-side materials, drafting a CIM, or organizing company data for a sale process.

Summary: An investment banking workflow for building Confidential Information Memoranda in sell-side M&A. It covers four steps: gathering source materials, structuring the CIM across 8 sections (Executive Summary, Company Overview, Industry Overview, Growth Opportunities, Customers & Sales, Operations, Financial Overview, Appendix), applying drafting guidelines (professional tone, data-driven claims, 40-60 pages), and producing a Word document with Excel appendix and embedded charts.

cim
confidential information memorandum
offering memorandum
info memo
draft cim
sell-side materials

No filesystem read access is required. The workflow operates on user-provided source materials.

The workflow outputs a Word document (.docx) with professional formatting and a separate Excel appendix with detailed financials.

No network access is required or referenced. All data comes from user-provided source materials.

User-provided source materials

The workflow requires management presentations, historical financials, budget/forecast, customer data, org chart, and prior presentations.

User provides company source materials including financials, management presentations, and customer data.

Financial advice boundary is absent

The workflow produces sell-side M&A materials that set valuation expectations but includes no explicit investment advice disclaimer.


Data Pack Builder

Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies.

Summary: An investment banking workflow for building standardized financial data packs from CIMs, SEC filings, web search, and MCP servers. It covers six phases: document processing and data extraction, data normalization (restructuring charges, SBC, acquisition costs, legal settlements, related party adjustments), Excel workbook building (8-tab structure), scenario building (management/base/downside cases), quality control (5 check categories), and final delivery. Includes industry-specific adaptations for SaaS, manufacturing, real estate, and healthcare.

data pack
financial data pack
due diligence data pack
ic materials
standardize financial reporting

xlsx skill

External xlsx skill for all Excel file creation and manipulation

The xlsx skill is referenced as required for Excel file manipulation but is not included in the captured directory.

The xlsx skill provides Excel file creation, formatting, and formula writing capabilities used throughout the workflow.

The workflow reads source materials including uploaded documents, SEC filings, and data from MCP servers.

The workflow outputs Excel workbooks with standardized financial data packs.

The workflow references web search for public filings and MCP server data access as input sources.

No shell commands or scripts are included in the captured directory, though the xlsx skill referenced may use Python/openpyxl.

The workflow references using the xlsx skill for Excel file creation and includes Python code examples for row reference tracking and formula writing.

No external credentials are required, though MCP server access may require configuration depending on the data source.

User-provided documents

The workflow accepts uploaded documents including CIMs, offering memorandums, and SEC filings as primary input sources.

User provides source documents such as CIMs, offering memorandums, and historical financials.

Web search

Web search for public filings is an optional data source.

Web search can be used to access public SEC filings and market data.

MCP server data

MCP server data access is an optional data source.

MCP servers can provide structured financial data from external providers.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

No requirements.txt or equivalent manifest is included in the captured directory, though the xlsx skill and Python/openpyxl are referenced.

Referenced xlsx skill is not included

The workflow explicitly requires the xlsx skill for all Excel file creation and manipulation, but this skill is not present in the captured directory.

Financial advice boundary is absent

The workflow produces investment committee materials but includes no explicit investment advice disclaimer.


Deal Tracker

Track multiple live deals with milestones, deadlines, action items, and status updates. Maintains a deal pipeline view and surfaces upcoming deadlines and overdue items. Use when managing a book of business, tracking process milestones, or preparing for weekly deal reviews.

Summary: An investment banking workflow for tracking M&A deal pipelines. It covers five steps: deal setup (name, client, type, role, size, stage, team, key dates), milestone tracking (18 milestones from engagement to close with status), action items (running list with owner, due date, priority), weekly deal review (per-deal status and pipeline summary), and producing an Excel workbook with pipeline overview, milestone tracker tabs, action item list, and weekly review summary.

deal status
where are we on
process update
deal pipeline
weekly deal review

No filesystem read access is required. The workflow operates on user-provided deal information.

The workflow outputs an Excel workbook with pipeline overview, per-deal milestone tracker tabs, action item master list, and weekly review summary, plus an optional Markdown summary.

No network access is required or referenced. The workflow relies on user-provided deal information.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

No external credentials or paid data sources are referenced.

User-provided deal information

The workflow requires deal name, client, type, role, size, stage, team, and key dates for each deal.

User provides deal setup information and ongoing milestone updates.

Financial advice boundary is absent

The workflow tracks M&A deal pipelines but includes no explicit investment advice disclaimer.


Merger Model

Build accretion/dilution analysis for M&A transactions. Models pro forma EPS impact, synergy sensitivities, and purchase price allocation. Use when evaluating a potential acquisition, preparing merger consequences analysis for a pitch, or advising on deal terms.

Summary: An investment banking workflow for building M&A accretion/dilution models. It covers seven steps: gathering acquirer/target/deal term inputs, purchase price analysis (premium, EV/EBITDA, P/E implied), sources & uses, pro forma EPS calculation (year 1-3 with synergies, foregone interest, new debt interest, intangible amortization), sensitivity analysis (synergies vs premium, cash/stock mix), breakeven synergies, and producing an Excel workbook with one-page merger consequences summary.

accretion dilution
ma model
pro forma eps
merger consequences
deal impact analysis

No filesystem read access is required. The workflow operates on user-provided deal inputs.

The workflow outputs an Excel workbook with assumptions, sources & uses, pro forma income statement, accretion/dilution summary, sensitivity tables, and breakeven analysis, plus a one-page merger consequences summary.

No network access is required or referenced. All data comes from user-provided deal inputs.

User-provided deal inputs

The workflow requires acquirer data (share price, shares outstanding, EPS, P/E, cost of debt, tax rate, cash, debt), target data (share price, shares, EPS, EV), and deal terms (offer price, consideration mix, synergies, fees, close date).

User provides acquirer, target, and deal term information for the merger analysis.

Financial advice boundary is absent

The workflow produces M&A deal impact analysis for pitch books but includes no explicit investment advice disclaimer.


Pitch Deck

Populate investment banking pitch deck templates with data from source files. Use when user provides a PowerPoint template to fill in, has source data (Excel/CSV) to populate into slides, mentions populating or filling a pitch deck template, or needs to transfer data into existing slide layouts. Not for creating presentations from scratch.

Summary: An investment banking workflow for populating pitch deck PowerPoint templates with financial data. It covers five phases: data extraction (backup, identify sources, validate numbers, standardize units), content mapping (analyze template, map data to sections), template population (remove placeholder boxes, populate content, apply formatting, create table objects), validate-fix-repeat (visual validation via LibreOffice, 9-point checklist, 3-cycle fix protocol), and final verification. Includes four reference files for formatting standards, slide templates, XML patterns, and calculation standards.

populate pitch deck
fill pitch deck template
pitch book
presentation template
slide population

LibreOffice (soffice)

LibreOffice is used for visual validation by converting PPTX to PDF/images

The workflow uses soffice --headless --convert-to pdf for visual validation of populated slides.

LibreOffice is used for validation only, not for editing. Alternative: libreoffice --headless command.

pdftoppm

pdftoppm converts PDF slides to JPEG images for visual validation

The workflow uses pdftoppm -jpeg -r 150 to convert PDF to slide images for validation.

pdftoppm is part of poppler-utils and is used for visual validation only.

The workflow reads source materials including Excel, CSV, PDF reports, Word documents, and PowerPoint templates.

The workflow modifies PowerPoint files directly, including creating backups and populating template slides with data.

No network access is required. The workflow references web sources as optional input but does not require network access for the core workflow.

The workflow uses shell commands for validation: soffice --headless --convert-to pdf and pdftoppm for converting slides to images for visual validation.

The workflow involves direct XML editing of PowerPoint files and references XML patterns for tables, shapes, and arrows.

No external credentials or paid data sources are required.

User-provided PowerPoint template

The workflow requires a PowerPoint template file to populate with data.

User provides the PowerPoint template to be populated.

User-provided source data

The workflow requires source data in Excel, CSV, PDF, or Word format to populate into the template.

User provides source data files (Excel, CSV, PDF reports, Word documents) for populating the template.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

No requirements.txt or equivalent manifest is included in the captured directory, though soffice (LibreOffice) and pdftoppm are referenced for validation.

Financial advice boundary is absent

The workflow produces IB pitch deck materials but includes no explicit investment advice disclaimer.


Process Letter

Draft process letters and bid instructions for sell-side M&A processes. Covers initial indication of interest (IOI) instructions, final bid procedures, and management meeting logistics.

Summary: An investment banking workflow for drafting sell-side M&A process letters. It covers five steps: determining letter type (initial, IOI, final bid, management meeting), initial process letter/IOI instructions (6 sections including IOI requirements with valuation, consideration, financing, diligence, timeline, and strategic rationale), final bid/second round letter (8 additional requirements including SPA markup, committed financing, exclusivity, regulatory, and evaluation criteria), management meeting invitation (logistics, attendees, agenda, ground rules, materials, follow-up), and producing a Word document with track changes for client review.

bid instructions
ioi letter
bid procedures
final round letter
management meeting invite

No filesystem read access is required. The workflow operates on user-provided deal information.

The workflow outputs a Word document (.docx) with professional letter formatting, firm letterhead placeholder, and a track changes version for client review.

No network access is required or referenced.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

No external credentials or paid data sources are referenced.

User-provided deal information

The workflow requires deal code name, seller objectives, process timeline, key dates, IOI/final bid requirements, and management meeting logistics.

User provides deal parameters and process requirements for the letter drafting.

Financial advice boundary is absent

The workflow drafts M&A process letters but includes no explicit investment advice disclaimer.


Strip Profile

Create professional investment banking strip profiles (company profiles) for pitch books, deal materials, and client presentations. Generates 1-4 information-dense slides with quadrant layouts, charts, and tables.

Summary: An investment banking workflow for creating professional company strip profiles. It covers three steps: clarifying requirements (single vs multi-slide, focus areas), research and planning (SEC filings, Bloomberg, FactSet, CapIQ for financials, valuation, growth, ownership, segments), and slide-by-slide creation with PptxGenJS (4:3 format, quadrant layouts, charts, tables, mandatory visual review via soffice/pdftoppm, per-slide user approval gates). Includes detailed formatting standards, font sizes, chart code examples, and a quality checklist at IB standard.

company profile
strip profile slides
ib company profile
pitch book profile

sec-edgar bloomberg factset web-market-data user-provided-data

PptxGenJS

JavaScript library for programmatic PowerPoint file generation

The workflow uses PptxGenJS for all PowerPoint generation including slides, charts, tables, shapes, and text formatting.

PptxGenJS is a JavaScript library for creating PowerPoint files programmatically.

LibreOffice (soffice)

LibreOffice is used for visual validation by converting PPTX to PDF

The workflow uses soffice --headless --convert-to pdf for visual validation of each slide.

LibreOffice is used for validation only, not for editing.

pdftoppm

The workflow uses pdftoppm -jpeg -r 150 to convert PDF to slide images for visual review.

pdftoppm is part of poppler-utils and is used for visual validation only.

No filesystem read access is required. The workflow operates on researched data from web sources and market data providers.

The workflow generates PowerPoint files using PptxGenJS and converts them to PDF/images for visual validation.

The workflow requires web search for company brand colors and references data sources including BamSEC, SEC EDGAR, Bloomberg, FactSet, and CapIQ for market data and consensus estimates.

The workflow uses shell commands for visual validation: soffice --headless --convert-to pdf and pdftoppm for converting slides to images for visual review.

The workflow uses PptxGenJS (JavaScript library) for programmatic PowerPoint generation, including charts, tables, shapes, and text formatting.

The workflow references Bloomberg, FactSet, and CapIQ as market data sources, which require paid subscriptions.

SEC EDGAR / BamSEC

Company filings (Item 1. Business, MD&A) are the primary data source for company information.

SEC EDGAR and BamSEC for company filings and business descriptions.

Bloomberg / FactSet / CapIQ

Market data (price, shares, market cap, net debt, EV, ownership) and consensus estimates (NTM revenue, EBITDA, EPS) from these providers.

Paid market data terminals for financial metrics and consensus estimates.

Web search

Web search is required for company brand colors and recent press releases from last 90 days.

Web search for brand colors, press releases, M&A activity, and guidance changes.

P0 records automated snapshot review only. Installation was not tested.

No human review evidence is recorded for this snapshot.

No requirements.txt or equivalent manifest is included in the captured directory, though PptxGenJS, soffice, and pdftoppm are referenced.

Referenced example file is not included

The SKILL.md references examples/Nike_Strip_Profile_Example.pptx for layout inspiration, but this file is not included in the captured directory.

Financial advice boundary is absent

The workflow produces IB pitch book materials but includes no explicit investment advice disclaimer.


Teaser

Draft anonymous one-page company teasers for sell-side M&A processes. Creates a compelling summary without revealing the company identity, designed to gauge buyer interest before NDA execution.

Summary: An investment banking workflow for drafting anonymous one-page company teasers for sell-side M&A processes. It covers four steps: gathering inputs (company description, sector, financial metrics, geography, selling points, anonymization guidance, target buyer audience), teaser structure (header with deal code name, company description, investment highlights, financial summary table, transaction overview), anonymization check (no company name, brand names, specific cities, named customers, identifiable imagery), and output (Word, PDF, optional PowerPoint). Designed to generate buyer interest before NDA execution.

blind teaser
anonymous profile
one-pager for process
draft teaser for sell-side

No filesystem read access is required. The workflow operates on user-provided company information.

The workflow outputs a Word document (.docx), a PDF version for distribution, and an optional PowerPoint version (single slide).

No network access is required or referenced.

No shell commands or scripts are included or referenced in the captured directory.

No code files or code execution instructions are present in the captured directory.

No external credentials or paid data sources are referenced.

User-provided company information

The workflow requires company description, sector, key financial metrics, geographic footprint, key selling points, anonymization guidance, and target buyer audience.

User provides all company information for the teaser drafting.

Financial advice boundary is absent

The workflow drafts M&A teaser materials but includes no explicit investment advice disclaimer.