Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add yennanliu/InvestSkill --skill stock-valuationgit clone --depth 1 https://github.com/yennanliu/InvestSkillWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/yennanliu/investskill/stock-valuation)<a href="https://agentmods.dev/skills/yennanliu/investskill/stock-valuation"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/stock-valuation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/yennanliu/investskill/stock-valuation"><img src="https://agentmods.dev/badge/skills/yennanliu/investskill/stock-valuation.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00019 | $0.06329 |
| Opus 5 | $0.00010 | $0.03164 |
| Sonnet 5 | $0.00004 | $0.01266 |
| Haiku 4.5 | $0.00002 | $0.00633 |
Grade A, and why
stock-valuation scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 619 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Stock Valuation
⚠️ Data Verification — Do This Before Any Analysis
Before running any analysis, always retrieve the latest market data for the ticker:
- Fetch current price — use web search or ask the user for the live price, 52-week range, and market cap. Never assume a price from training data.
- Confirm key figures — recent earnings, revenue, key ratios (P/E, P/S, etc.) as applicable to this skill.
- State your data source — note where the numbers came from (e.g., "Google Finance, June 19 2026") at the top of the output.
- Flag stale data explicitly — if live data is unavailable, display this warning before proceeding:
⚠️ Live data unavailable. The following analysis uses training-data estimates which may be significantly out of date. Verify all prices and metrics before making any decisions.
Never silently substitute training-data estimates for current prices. When in doubt, ask the user to paste the latest quote.
Derive a rigorous intrinsic value estimate using multiple independent valuation methodologies, then triangulate to a single probability-weighted target price. Never rely on a single method — cross-validation across DCF, comparable company analysis (CCA), EV/EBITDA, and residual income models builds conviction and exposes assumption fragility.
Overview
Valuation is an art grounded in financial science. Each method has strengths and weaknesses depending on the business type, stage of maturity, and data availability. This skill applies four to five valuation methods, then reconciles them into a football field chart to show the implied value range. Wherever there is consensus across methods, conviction is high. Where methods diverge significantly, that gap tells you something important about market expectations.
When to Use Each Method
Valuation Method Best For Avoid For
────────────────────────────────────────────────────────────────────────────────────
DCF (Free Cash Flow) Mature, FCF-positive businesses Pre-revenue, banks, REITs
Comparable Company (CCA) Any publicly traded company No good public comps
EV/EBITDA Multiple Capital-intensive industrials Asset-light, high-SBC tech
Price/Earnings (P/E) Stable earnings businesses Negative earnings
Price/Sales (P/S) Revenue-stage growth companies Mature high-margin businesses
EV/Revenue High-growth, low-margin SaaS Mature, cyclical businesses
Residual Income (RI) Financial companies, book-value Asset-light businesses
Dividend Discount (DDM) Dividend-paying value stocks Growth stocks, no dividend
Asset-Based NAV Real estate, holding companies Operating businesses
────────────────────────────────────────────────────────────────────────────────────
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 9d ago First seen · 619 lines · 19 tokens per session scan A f947f615d010
stock-valuation is a skill published in the GitHub repository yennanliu/InvestSkill (205 stars, last pushed yesterday), licensed MIT. It adds 19 tokens to every session and 6,329 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
dcf-model
DCF valuation: free cash flow projections, WACC, terminal value, sensitivity analysis.
initiating-coverage
Full equity research initiation: company research, financial model, valuation, charts, 30-50 page report.
comps-analysis
Comparable company analysis: operating metrics, valuation multiples, peer benchmarking.
ui-design
Design-quality reference for financial-research visual output: typography, color, composition, and avoiding generic AI aesthetics.
onboarding
First-time user onboarding to set up investment profile, watchlists, portfolio, and preferences.
chart-annotation
Draw price lines, trendlines, zones, and event markers directly on a stock's price chart — reach for it whenever you'd otherwise describe a level, pattern, or event in prose. Renders live on MarketView and as a clickable preview card in any other chat.