mirror of https://github.com/NousResearch/hermes-agent.git synced 2026-05-11 03:31:55 +00:00

feat(optional-skills): port Anthropic financial-services skills as optional finance bundle (#21180 )

Adds 7 optional skills under optional-skills/finance/ adapted from
anthropics/financial-services (Apache-2.0):

  excel-author        — openpyxl conventions: blue/black/green cells,
                        formulas over hardcodes, named ranges, balance
                        checks, sensitivity tables. Ships recalc.py.
  pptx-author         — python-pptx for model-backed decks (pitch,
                        IC memo, earnings note) that bind every number
                        to a source workbook cell.
  dcf-model           — institutional DCF (49KB skill): projections,
                        WACC, terminal value, Bear/Base/Bull scenarios,
                        5x5 sensitivity tables. Ships validate_dcf.py.
  comps-analysis      — comparable company analysis: operating metrics,
                        multiples, statistical benchmarking.
  lbo-model           — leveraged buyout: S&U, debt schedule, cash
                        sweep, exit multiple, IRR/MOIC sensitivity.
  3-statement-model   — fully-integrated IS/BS/CF with balance-check
                        plugs. Ships references/ for formatting,
                        formulas, SEC filings.
  merger-model        — accretion/dilution analysis for M&A.

All seven are optional (not active by default). Users install via
'hermes skills install official/finance/<skill>'.

Hermesification:
- Stripped every Office JS / Office Add-in / mcp__office__*
  branch — skills assume headless openpyxl only.
- Replaced Cowork MCP data-source instructions with 'MCP first (via
  native-mcp), fall back to web_search/web_extract against SEC EDGAR
  and user-provided data'.
- Swapped Claude tool references (Bash, Read, Write, Edit, mcp__*)
  for Hermes-native equivalents and Python library calls.
- Canonical Hermes frontmatter (name/description/version/author/
  license/metadata.hermes.{tags,related_skills}).
- Descriptions tightened to 187-238 chars, trigger-first.
- Attribution preserved: author field credits 'Anthropic (adapted by
  Nous Research)', license: Apache-2.0, each SKILL.md links back to
  the upstream source directory.

Verification:
- All 7 discovered by OptionalSkillSource with source_id='official'
- Bundle fetch includes support files (scripts, references, troubleshooting)
- related_skills cross-refs all resolve within the bundle
- No Claude product / Cowork / Office JS / /mnt/skills leakage
  remains in body text (bounded mentions only in attribution blocks)

Source: https://github.com/anthropics/financial-services (Apache-2.0)

2026-05-07 04:58:39 -07:00

5.3 KiB

Raw Blame History

name

description

version

author

license

metadata

merger-model

Build accretion/dilution (merger) models in Excel — pro-forma P&L, synergies, financing mix, EPS impact. Pairs with excel-author. Use for M&A pitches, board materials, or deal evaluation.

1.0.0

Anthropic (adapted by Nous Research)

Apache-2.0

hermes

Environment

This skill assumes headless openpyxl — you are producing an .xlsx file on disk. Follow the excel-author skill's conventions for cell coloring, formulas, named ranges, and sensitivity tables. Recalculate before delivery: python /path/to/excel-author/scripts/recalc.py ./out/model.xlsx.

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.

Workflow

Step 1: Gather Inputs

Acquirer:

Company name, current share price, shares outstanding
LTM and NTM EPS (GAAP and adjusted)
P/E multiple
Pre-tax cost of debt, tax rate
Cash on balance sheet, existing debt

Target:

Company name, current share price, shares outstanding (if public)
LTM and NTM EPS or net income
Enterprise value or equity value

Deal Terms:

Offer price per share (or premium to current)
Consideration mix: % cash vs. % stock
New debt raised to fund cash portion
Expected synergies (revenue and cost) and phase-in timeline
Transaction fees and financing costs
Expected close date

Step 2: Purchase Price Analysis

Item	Value
Offer price per share
Premium to current
Equity value
Plus: net debt assumed
Enterprise value
EV / EBITDA implied
P/E implied

Step 3: Sources & Uses

Sources	$	Uses	$
New debt		Equity purchase price
Cash on hand		Refinance target debt
New equity issued		Transaction fees
		Financing fees
Total		Total

Step 4: Pro Forma EPS (Accretion / Dilution)

Calculate year-by-year (Year 1-3):

	Standalone	Pro Forma	Accretion/(Dilution)
Acquirer net income
Target net income
Synergies (after tax)
Foregone interest on cash (after tax)
New debt interest (after tax)
Intangible amortization (after tax)
Pro forma net income
Pro forma shares
Pro forma EPS
Accretion / (Dilution) %

Step 5: Sensitivity Analysis

Accretion/Dilution vs. Synergies and Offer Premium:

	$0M syn	$25M syn	$50M syn	$75M syn	$100M syn
15% premium
20% premium
25% premium
30% premium

Accretion/Dilution vs. Cash/Stock Mix:

	100% cash	75/25	50/50	25/75	100% stock
Year 1
Year 2

Step 6: Breakeven Synergies

Calculate the minimum synergies needed for the deal to be EPS-neutral in Year 1.

Step 7: Output

Excel workbook with:
- Assumptions tab
- Sources & uses
- Pro forma income statement
- Accretion/dilution summary
- Sensitivity tables
- Breakeven analysis
One-page merger consequences summary for pitch book

Important Notes

Always show both GAAP and adjusted (cash) EPS where relevant
Stock deals: use acquirer's current price for exchange ratio, note dilution from new shares
Include purchase price allocation — goodwill and intangible amortization matter for GAAP EPS
Synergy phase-in is critical — Year 1 is often only 25-50% of run-rate synergies
Don't forget foregone interest income on cash used and new interest expense on debt raised
Tax rate on synergies and interest adjustments should match the acquirer's marginal rate

Data sources — MCP first, web fallback

Many passages below say "use the S&P Kensho MCP / Daloopa MCP / FactSet MCP". Those are commercial financial-data MCPs from the original Cowork plugin context. In Hermes:

If you have any structured financial-data MCP configured (Hermes supports MCP — see native-mcp skill), prefer it for point-in-time comps, precedent transactions, and filings.
Otherwise, fall back to:
- web_search / web_extract against SEC EDGAR (https://www.sec.gov/cgi-bin/browse-edgar) for US filings
- Company IR pages for press releases, earnings decks
- browser_navigate for interactive data portals
- User-provided data (explicitly ask when the context doesn't have it)
Never fabricate. If a multiple, precedent, or filing number can't be sourced, flag the cell as [UNSOURCED] and surface it to the user.

Attribution

This skill is adapted from Anthropic's Claude for Financial Services plugin suite (Apache-2.0). The Office-JS / Cowork live-Excel paths have been removed; this version targets headless openpyxl via the excel-author skill's conventions. Original: https://github.com/anthropics/financial-services

5.3 KiB Raw Blame History