company-valuation

Q: How do I install company-valuation?

Run npx skills add https://github.com/himself65/finance-skills --skill company-valuation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/company-valuation/ (personal) or .claude/skills/company-valuation/ (project).

by himself65Marketing & SEO

Estimate a public company's intrinsic value with DCF, relative (peer multiple), and sum-of-the-parts (SOTP) methods, then blend them into an implied share price with upside/downside vs the market price, a WACC and terminal-growth sensitivity grid, and bull/base/bear scenarios. Use this skill whenever the user asks what a company or ticker is worth: fair value, intrinsic value, implied share price, a price target from fundamentals, whether it is overvalued or undervalued, building a DCF (WACC, terminal value, discounted cash flow), EV/EBITDA or P/E based targets, peer comparison valuation, or SOTP and conglomerate discounts. Run the model rather than answering valuation questions from memory.

Installs: 3.2K
Last 24h: +40
Trending rank: #3902
Security: Not audited

Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/himself65/finance-skills --skill company-valuation

01
Install
Run the command in your project. The CLI asks which agents to install for.
02
Describe the task
Ask your agent for something this skill covers. It loads company-valuation automatically when relevant.
03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

Company Valuation

If your host has not already executed the shell probes below (for example, in Codex), run the commands inside the ! wrappers with its shell tool before choosing a setup path. Use the actual output; if shell access is unavailable, report that limitation.

Triangulates intrinsic value via three methods, then blends them to an implied share price:

DCF — 5-year FCFF projection, discount at WACC, terminal value.
Relative — apply peer median P/E, EV/Revenue, EV/EBITDA.
SOTP — when 2+ distinct reporting segments exist, value each at pure-play peer multiples.

Always present a WACC × terminal-growth sensitivity table and Bull/Base/Bear scenarios.

Disclaimer: Research/educational output. Not financial advice.

Step 1: Detection Flow

Detect data source and runtime deps. The skill supports 2 method paths — pick the richest one available.

Environment status:

!`python3 -c "exec('try:\n import yfinance, numpy, pandas\n print(\'YFIN_OK\')\nexcept Exception:\n print(\'YFIN_MISSING\')')"`

!`python3 -c "exec('try:\n import yfinance as yf\n t=yf.Ticker(\'^TNX\')\n p=t.fast_info.last_price\n print(f\'RF_10Y={p/100:.4f}\')\nexcept Exception:\n print(\'RF_FETCH_FAIL\')')"`

Decision tree:

Condition	Method path
`YFIN_OK`	Path A (primary): yfinance for financials + peer multiples
`YFIN_MISSING`	Path B: pip-install yfinance, then Path A. `python3 -m pip install -q yfinance numpy pandas`
`RF_FETCH_FAIL`	Use default `rf = 0.045` and note stale risk-free rate in output

If RF_10Y= printed, use that value as rf in Step 4d instead of the hardcoded 4.5%.

Step 2: Choose Methods & Set Defaults

Method applicability

Company type	DCF	Relative	SOTP	Fallback
Mature cash-flow (CPG, telecom, utilities)	✅ primary	✅	❌	—
High-growth SaaS / software	✅ with care	✅ primary	❌	Use EV/Revenue + Rule of 40
Multi-segment conglomerate	✅	✅	✅ primary	See `references/sotp.md`
Banks / insurance	❌	✅ (P/B, P/TBV)	❌	DDM or excess return; note in output
Pre-revenue	❌	EV/Revenue only	❌	Flag low confidence
REITs	❌	✅ (P/FFO, P/AFFO)	❌	NAV-based
Cyclicals (energy, semis, industrials)	✅ on mid-cycle	✅	sometimes	Normalize through-cycle

Defaults table

Settle every parameter before pulling data — these defaults apply unless the user overrides them.

Parameter	Default	Rationale
Projection horizon	5 years	Standard explicit forecast window
Terminal growth `g`	2.5%	~ long-run US GDP
Risk-free rate `rf`	Live 10Y UST from Step 1, else 4.5%	Current cost of capital anchor
Equity risk premium `erp`	5.5%	Damodaran mid-range
Beta	`info['beta']` from yfinance	Market-observed levered beta
Cost of debt `kd`	`interest_expense / total_debt`, else 5.5%	Effective rate; fallback to IG spread
Tax rate	3-yr median effective rate, floored 15%, capped 30%	Strips out one-offs
Margin assumptions	3-yr median of each ratio	Smooths cyclical noise
SBC treatment	Cash for software/SaaS; non-cash for industrials/CPG	Industry convention
Peer count	4-6	Balances signal vs noise
Peer multiple	Median (not mean)	Robust to outliers
Method weights (no SOTP)	DCF 50% / Relative 50%	Equal triangulation
Method weights (with SOTP)	DCF 40% / Relative 30% / SOTP 30%	SOTP gets weight when applicable
Sensitivity grid	WACC ±1% in 0.5% steps × g from 1.5-3.5% in 0.5%	5×5 matrix

See references/wacc_erp_rates.md for current risk-free rates, ERP tables, and sector WACC benchmarks.

Step 3: Pull Data

import yfinance as yf
import numpy as np
import pandas as pd

TICKER = "AAPL"  # replace
t = yf.Ticker(TICKER)

info       = t.info
income_a   = t.income_stmt
cashflow_a = t.cashflow
balance_a  = t.balance_sheet
income_q   = t.quarterly_income_stmt
cashflow_q = t.quarterly_cashflow

earnings_est = t.earnings_estimate
revenue_est  = t.revenue_estimate

price       = info.get("currentPrice") or info.get("regularMarketPrice")
market_cap  = info.get("marketCap")
shares_out  = info.get("sharesOutstanding")
total_debt  = info.get("totalDebt") or 0
cash        = info.get("totalCash") or 0
beta        = info.get("beta") or 1.0
sector      = info.get("sector")
industry    = info.get("industry")

Key financial statement rows (yfinance labels):

Need	Row
Revenue	`Total Revenue`
EBIT	`Operating Income`
Net income	`Net Income`
D&A	`Depreciation And Amortization` (in cashflow)
CapEx	`Capital Expenditure` (negative)
ΔNWC	`Change In Working Capital` (cashflow)
SBC	`Stock Based Compensation` (cashflow)

Step 4: DCF Build

Full methodology + industry-specific tweaks in references/dcf.md. Quick skeleton:

# 4a. Revenue growth path — fade from Y1 (consensus or hist CAGR) to terminal g
rev = income_a.loc["Total Revenue"].dropna().iloc[::-1].values  # yfinance columns are newest-first; reverse to oldest -> newest
hist_cagr = (rev[-1] / rev[0]) ** (1 / (len(rev)-1)) - 1
y1 = float(revenue_est.loc["+1y", "growth"]) if "+1y" in revenue_est.index else hist_cagr
g_terminal = 0.025
growth_path = np.linspace(y1, g_terminal + 0.01, 5)

# 4b. Margins — 3y median
ebit_margin = float((income_a.loc["Operating Income"] / income_a.loc["Total Revenue"]).iloc[:3].median())
da_pct      = float((cashflow_a.loc["Depreciation And Amortization"] / income_a.loc["Total Revenue"]).iloc[:3].median())
capex_pct   = float((cashflow_a.loc["Capital Expenditure"].abs() / income_a.loc["Total Revenue"]).iloc[:3].median())
nwc_pct     = float((cashflow_a.loc["Change In Working Capital"].abs() / income_a.loc["Total Revenue"]).iloc[:3].median())
eff_tax     = (income_a.loc["Tax Provision"] / income_a.loc["Pretax Income"]).iloc[:3].median()
tax_rate    = float(min(0.30, max(0.15, eff_tax))) if pd.notna(eff_tax) else 0.21  # 3y median effective, floored 15%, capped 30%

# 4c. FCFF per year
rev_t = [float(income_a.loc["Total Revenue"].iloc[0])]
fcff  = []
for g in growth_path:
    rev_t.append(rev_t[-1] * (1 + g))
    ebit = rev_t[-1] * ebit_margin
    nopat = ebit * (1 - tax_rate)
    fcff.append(nopat + rev_t[-1]*da_pct - rev_t[-1]*capex_pct - rev_t[-1]*nwc_pct)

# 4d. WACC
rf, erp, kd = 0.045, 0.055, 0.055  # override rf with live value from Step 1
ke = rf + beta * erp
e_v = market_cap / (market_cap + total_debt)
d_v = 1 - e_v
wacc = e_v*ke + d_v*kd*(1 - tax_rate)

# 4e. Terminal value — compute both, use midpoint
tv_gordon = fcff[-1] * (1 + g_terminal) / (wacc - g_terminal)
tv_exit   = (rev_t[-1] * ebit_margin + rev_t[-1] * da_pct) * 15  # peer median EV/EBITDA
tv_base   = 0.5 * (tv_gordon + tv_exit)

# 4f. Bridge to equity
pv_fcff = sum(f / (1+wacc)**(i+1) for i, f in enumerate(fcff))
pv_tv   = tv_base / (1+wacc)**5
ev      = pv_fcff + pv_tv
equity  = ev + cash - total_debt
implied_price_dcf = equity / shares_out

Gates: (a) if wacc <= g_terminal → stop, g too aggressive; (b) if pv_tv / ev > 0.85 or < 0.45 → flag and show both TV methods; (c) if wacc is outside the sector sanity band in references/wacc_erp_rates.md → note.

Step 5: Relative Valuation

Select 4-6 peers. Peer map and adjustment rules in references/relative_valuation.md.

PEERS = ["MSFT", "ORCL", "CRM", "NOW", "SAP", "WDAY"]  # pick by industry
multiples = {}
for p in PEERS:
    pi = yf.Ticker(p).info
    multiples[p] = {
        "pe_fwd": pi.get("forwardPE"),
        "ev_rev": pi.get("enterpriseToRevenue"),
        "ev_ebitda": pi.get("enterpriseToEbitda"),
        "ps": pi.get("priceToSalesTrailing12Months"),
    }
med_pe     = np.nanmedian([v["pe_fwd"] for v in multiples.values()])
med_ev_rev = np.nanmedian([v["ev_rev"] for v in multiples.values()])
med_ev_eb  = np.nanmedian([v["ev_ebitda"] for v in multiples.values()])

eps_ttm    = float(income_q.loc["Diluted EPS"].iloc[:4].sum())
rev_ttm    = float(income_q.loc["Total Revenue"].iloc[:4].sum())
ebitda_ttm = float(income_q.loc["EBIT"].iloc[:4].sum()) + float(cashflow_q.loc["Depreciation And Amortization"].iloc[:4].sum())
net_debt   = total_debt - cash

implied_pe       = med_pe * eps_ttm
implied_ev_rev   = (med_ev_rev * rev_ttm - net_debt) / shares_out
implied_ev_ebit  = (med_ev_eb  * ebitda_ttm - net_debt) / shares_out
implied_price_rel = np.nanmedian([implied_pe, implied_ev_rev, implied_ev_ebit])

Adjust peer median ±10-30% if target's growth or margin profile diverges materially. Always state the adjustment and reason. Rule of 40 anchor for SaaS in references/relative_valuation.md.

Step 6: SOTP (multi-segment only)

Skip unless the 10-K reports 2+ operating segments with distinct economics. yfinance does NOT expose segment data — user must supply or parse from filings. Full methodology in references/sotp.md:

Identify segments + pure-play peer for each
Apply peer median EV/EBITDA (or EV/Rev for growth segments)
Subtract unallocated corporate costs (cap 2-5% of revenue if unknown)
Subtract net debt, minority interest; divide by shares

SOTP discount = (SOTP price − market price) / SOTP price. Flag if >20% (conglomerate discount).

Step 7: Triangulate, Sensitivity, Scenarios

# Blended implied price
if sotp_price is None:
    blended = 0.5*implied_price_dcf + 0.5*implied_price_rel
else:
    blended = 0.4*implied_price_dcf + 0.3*implied_price_rel + 0.3*sotp_price

# 5x5 sensitivity grid
wacc_grid = [wacc + dx for dx in (-0.01, -0.005, 0, 0.005, 0.01)]
g_grid    = [0.015, 0.020, 0.025, 0.030, 0.035]
sens = {}
for w in wacc_grid:
    for g in g_grid:
        tv = fcff[-1]*(1+g)/(w-g)
        pv = sum(f/(1+w)**(i+1) for i,f in enumerate(fcff)) + tv/(1+w)**5
        sens[(w,g)] = (pv + cash - total_debt) / shares_out

Also produce Bull / Base / Bear: shift revenue growth ±300bps, EBIT margin ±200bps, WACC ∓100bps, terminal g 3.0% / 2.5% / 1.5%.

Step 8: Respond to the User

Present the valuation in this order:

Headline verdict — one sentence with the blended fair value, the current price, the % upside or downside, and which method is most bullish or bearish.
Snapshot — sector, industry, market cap, current price, 3M / 12M price change, LTM revenue growth.
Three-method summary — 3-column table: method | implied price | weight | brief rationale.
DCF build — assumptions table (growth path, margins, WACC components, terminal method) + 5-yr FCFF projection table + EV-to-equity bridge.
Peer comparison — table of peers with P/E fwd, EV/Rev, EV/EBITDA, gross margin, rev growth; bottom row = median; flag target's premium/discount.
SOTP (if applicable) — segment table + adjustments + equity value.
Sensitivity matrix — WACC × g grid (5×5), base case highlighted.
Scenarios — Bull / Base / Bear table with levers + implied price.
Key risks — which assumptions move the answer most, and what could break the thesis.

Error handling

Missing / edge case	Action
yfinance returns `None` for beta	Use sector-default beta from `references/wacc_erp_rates.md`
Negative LTM EBITDA	Skip EV/EBITDA multiple; rely on EV/Revenue + DCF
Negative LTM EPS	Skip P/E multiple; use forward P/E if positive, else skip
Growth > WACC in Gordon	Cap `g = wacc − 0.5%` and flag
Fewer than 3 years history	Use what's available; flag data confidence as "low"
Peer data fetch fails	Drop that peer from median; note in output
No segment data for SOTP	Skip Section 6; proceed with DCF + Relative only

Caveats to include

TTM data lags real-time; peer multiples reflect market sentiment (can overshoot)
DCF is garbage-in/garbage-out; sensitivity matters more than a point estimate
yfinance data is unofficial; cross-check any decision with primary filings
Not financial advice

Reference Files

references/dcf.md — DCF methodology + industry-specific guidance (software, retail, financials, healthcare, energy, manufacturing, CPG, telecom, REITs, streaming)
references/relative_valuation.md — Peer selection, multiple adjustment rules, Rule of 40, peer sets by theme
references/sotp.md — Sum-of-parts methodology, conglomerate discount detection, catalysts
references/wacc_erp_rates.md — Risk-free rates, equity risk premiums, sector WACC benchmarks, sector-default betas

FAQ

What does the company-valuation skill do?

How do I install company-valuation?

Run npx skills add https://github.com/himself65/finance-skills --skill company-valuation in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/company-valuation/ (personal) or .claude/skills/company-valuation/ (project).

Which AI agents can use company-valuation?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is company-valuation safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains company-valuation?

It is published by himself65 in himself65/finance-skills. It has 3,187 recorded installs.

More from himself65/finance-skills

View all →

yfinance-data

himself65/finance-skills

Fetch financial and market data with the yfinance Python library (Yahoo Finance). Use this skill whenever the user wants stock data: current quotes and price history, financial statements (income statement, balance sheet, cash flow), options chains, dividends and splits, earnings and analyst estimates, price targets and ratings, institutional and insider holdings, news, multi-ticker comparisons, stock screens, or sector and industry data. Use it even when the user gives only a ticker symbol (AAPL, MSFT, TSLA) and the intent has to be inferred. For earnings previews or recaps, estimate revisions, valuation, correlation, liquidity, or ETF premium analysis, prefer the dedicated skill.

Business & Finance3.9K installs

stock-correlation

himself65/finance-skills

Analyze how stocks move together using Yahoo Finance price history (yfinance): find correlated peers for a ticker, measure correlation, beta, and spread between specific tickers, cluster a group into a correlation matrix, and track rolling or regime-dependent correlation. Use this skill whenever the user asks what moves with a stock, what else drops when it drops, related tickers or sympathy plays, sector or supply-chain peers, pair trading or hedging pairs, beta or relative performance, correlation matrices, co-movement, or rolling/realized correlation — including well-known pairs like AMD/NVDA, GOOGL/AVGO, or LITE/COHR. With a single ticker, assume the user wants its correlated peers.

Business & Finance3.5K installs

options-payoff

himself65/finance-skills

Render an interactive options payoff chart — expiry P&L plus Black-Scholes theoretical value — with sliders for strikes, premium, IV, DTE, and spot. Use this skill whenever the user describes or shares an options position or strategy and wants to see how it makes or loses money: vertical, calendar, diagonal, or ratio spreads, butterflies, condors and iron condors, straddles, strangles, covered calls, protective or naked puts, or any multi-leg structure. This includes pasted strikes, premiums, and expiries, requests like "show me the payoff" or "draw the P&L curve", and broker screenshots (IBKR, TastyTrade, Robinhood). Use it even when details are partial — defaults fill the gaps.

Productivity & Automation3.4K installs

earnings-preview

himself65/finance-skills

Build a pre-earnings briefing for a stock from Yahoo Finance data (yfinance): the upcoming report date and timing, consensus EPS and revenue estimates with their range, the beat/miss track record, analyst ratings and price targets, and what to watch in the print. Use this skill whenever the user is preparing for an upcoming earnings report or asks what the street expects — consensus or whisper numbers, EPS expectations, whether a company will beat, an earnings setup, or an earnings-season preview — and whenever a ticker comes up in the context of upcoming earnings, even without the word "preview". For results that are already out, use earnings-recap.

Docs & Writing3.3K installs

earnings-recap

himself65/finance-skills

Analyze a company's most recent (or a specified past) earnings report from Yahoo Finance data (yfinance): actual vs estimated EPS, surprise size, revenue and margin trends, and the stock's price reaction. Use this skill whenever the user asks how earnings went — beat or miss, earnings surprise, quarterly results, the post-earnings move, or an earnings call recap — including casual references to a past report such as "AMZN reported last night" or "how did they do". For an upcoming report, use earnings-preview.

Docs & Writing3.3K installs

generative-ui

himself65/finance-skills

Design system and templates for interactive HTML/SVG visuals in Claude and Codex, using an available inline renderer or a standalone HTML file. Use this skill whenever the user wants visual or interactive output rather than plain text or markdown: charts and dashboards, diagrams, flowcharts and architecture drawings, mockups, comparison grids, illustrations, interactive explainers ("explain how X works" with a visual), or tools with sliders, toggles, and live-updating displays — including financial data shown visually and requests phrased as "show me", "visualize", "draw", or "chart this".

Frontend & Design3.2K installs

Popular marketing & seo skills

View category →

reddit-automation

flowkit-labs/skills

Find Reddit threads where people are genuinely asking for what you offer, then draft short, genuinely helpful replies — disclosing your affiliation honestly and naming your product only when it truly answers the question. Two moves: discovery — scan the right subreddits for real needs (recommendation asks, expressed pain, competitor mentions) and rank the few threads where you can actually help; drafting — write from real experience, respect each community's self-promo rules, and keep a human in the loop to review and post. This markets on Reddit the honest way: contribute value, disclose who you are, never astroturf. Distilled from the playbook behind doany.ai's Reddit agent. Triggers on "find reddit opportunities", "reddit marketing", "reddit automation", "reply on reddit for my product", "reddit community engagement", "reddit lead gen", or any ask to turn Reddit threads into genuine, disclosed marketing replies.

Marketing & SEO1.1M installs

twitter-automation

101-skills/superpowers

Automate Twitter/X with posting, engagement, and user management via inference.sh CLI. Apps: x/post-tweet, x/post-create (with media), x/post-like, x/post-retweet, x/dm-send, x/user-follow. Capabilities: post tweets, schedule content, like posts, retweet, send DMs, follow users, get profiles. Use for: social media automation, content scheduling, engagement bots, audience growth, X API. Triggers: twitter api, x api, tweet automation, post to twitter, twitter bot, social media automation, x automation, tweet scheduler, twitter integration, post tweet, twitter post, x post, send tweet

Marketing & SEO880K installs

twitter-automation

magentosh/superpowers

Marketing & SEO771.7K installs

twitter-automation

qu-skills/superpowers

Marketing & SEO564.3K installs

product-launch-video

heygen-com/hyperframes

Turn a product or marketing URL, pasted script, or brief into a product launch / promo video — SaaS promos, feature reveals, product demos, app and company launches. Use when the user wants to market, launch, promote, or reveal a product; the default for any commercial URL. Site tours / showcases of a website route here too — the brief carries the show-it-as-is intent. Unclear → /hyperframes.

Marketing & SEO538.7K installs

twitter-automation

skills-shell/superpowers

Marketing & SEO453.7K installs

company-valuation

Install

Install

Describe the task

Review the result

SKILL.md

Company Valuation

Step 1: Detection Flow

Step 2: Choose Methods & Set Defaults

Method applicability

Defaults table

Step 3: Pull Data

Step 4: DCF Build

Step 5: Relative Valuation

Step 6: SOTP (multi-segment only)

Step 7: Triangulate, Sensitivity, Scenarios

Step 8: Respond to the User

Error handling

Caveats to include

Reference Files

FAQ

More from himself65/finance-skills

Popular marketing & seo skills