stock-valuation

stock-valuation is a skill for Claude Code from yennanliu/InvestSkill. It costs 19 tokens per session (7,287 once invoked), scanned A, original, MIT.

A stock-analysis guide that estimates what a company's shares may be worth using several methods, including cash-flow forecasts and comparisons with similar companies.

In plain words
What is it for?
Use it to assess a stock's possible fair value, compare it with competitors, and produce a combined price estimate from current financial data.
Why use it?
It helps compare different valuation approaches instead of relying on one estimate or outdated market data.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Part of the us-stock-analysis plugin — 38 skills shipped together

Good fit Use it to assess a stock's possible fair value, compare it with competitors, and produce a combined price estimate from current financial data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/yennanliu/investskill/stock-valuation
Install

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.

Any agent
npx skills add yennanliu/InvestSkill --skill stock-valuation
Clone the repo
git clone --depth 1 https://github.com/yennanliu/InvestSkill

Made for: Claude Code.

Or install us-stock-analysis, the plugin that ships this one along with the rest of its 38 skills.

Wrote 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.

agentmods badge for stock-valuation

README.md
[![agentmods](https://agentmods.dev/badge/skills/yennanliu/investskill/stock-valuation/github.svg)](https://agentmods.dev/skills/yennanliu/investskill/stock-valuation)
Your own site
<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.

agentmods 80×15 button for stock-valuation

Your own site · 80×15
<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>
Per session 19 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,287 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00019 $0.07287
Opus 5.5 $0.00008 $0.02915
Sonnet 5.5 $0.00004 $0.01457
Haiku 4.5 $0.00002 $0.00729

Measured today against content hash 3f975bbab6ab, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 today.

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.

plugins/us-stock-analysis/skills/stock-valuation/SKILL.md · 658 lines

How it starts

The opening of the file, as written. The whole thing — 658 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:

  1. 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.
  2. Confirm key figures — TTM and latest fiscal-year revenue, EBIT, FCF, net debt, diluted share count, and the inputs to WACC (risk-free rate, beta, equity risk premium, cost of debt) — each with its source and date.
  3. State your data source — fill in the Data & Sources header (next section) so the origin, as-of date, retrieval path, and confidence of every figure are explicit at the top of the output.
  4. 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.


📋 Data & Sources Header — Open Every Output With It

The first thing in the output is this provenance block, filled in — never left as placeholders. It is the standard documented on the Data & Accuracy page and the first thing result-validator looks for:

Data & Sources
  As of:      <date the figures represent, e.g. 2026-06-30>
  Source:     <primary docs — SEC EDGAR 10-K/10-Q, company IR, FRED, exchange data …>
  Retrieval:  <pasted by user | web/tool retrieval | model memory>
  Confidence: <HIGH | MEDIUM | LOW>
  • Retrieval: model memory must be paired with Confidence: LOW — memory is a placeholder until confirmed against a primary source.
  • Mixed sources: list each with its own as-of date rather than blending them.
  • Sources disagree on the same figure (a closing price, a deal term): prefer the primary source (SEC filing, company release, exchange data) over an aggregator, then the most recent dated one. Show both values with their sources and dates, say which you used and why, and never average them.
  • Data the user pasted is reported as pasted by user; do not upgrade its confidence beyond what the user's own source supports.

Read the full file on GitHub · 658 lines

Changes

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.

  1. today Changed · +17 lines 3f975bbab6ab
  2. 12d ago Changed · +1 lines 33c6f9f4f0d3
  3. 28d ago Changed · -2 lines 4517bb050b0f
  4. 29d ago Changed · +23 lines a76c8f2c5027
  5. 1mo ago First seen · 619 lines · 19 tokens per session scan A f947f615d010

Subscribe to this mod's changes

stock-valuation is a skill published in the GitHub repository yennanliu/InvestSkill (255 stars, last pushed today), licensed MIT. It adds 19 tokens to every session and 7,287 once invoked, about $0.0001 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

dcf-model

Build a DCF valuation in Excel: FCF projections, WACC, terminal value, scenarios, sensitivity grids, reverse DCF. Triggers on build a DCF, what is it worth, intrinsic value, fair value, price target from cash flows.

ginlix-ai/LangAlpha · 56 tokens

comps-analysis

Comparable company analysis: peer set, operating metrics, valuation multiples, statistics and an implied value. Triggers on comps, trading comparables, how does it trade against peers, peer benchmarking, what multiple should it get.

ginlix-ai/LangAlpha · 48 tokens

company-profile

One-page company profile slide, four quadrants of overview, financial summary, share price chart and key facts. Also the compact profile paragraph when it has to sit inside a memo, a deck or a chat reply. Triggers on one-page profile, tear sheet, company snapshot, profile slide, quick profile of [company].

ginlix-ai/LangAlpha · 68 tokens

check-deck

QC an investment deck (a .pptx) before it circulates: number consistency, chart and narrative alignment, source coverage, language, then a circulation verdict. Triggers on check this deck, deck QC, review my presentation, is this ready to send, proofread the pitch book.

ginlix-ai/LangAlpha · 63 tokens

check-model

Audit a model somebody already built, and report on it without editing it: structure, formulas, integrity identities, source tie-out and reasonableness, ending in a routed issue log. Triggers on check my formulas, QA this spreadsheet, audit model, model review, something is off in my model, why does my balance sheet…

ginlix-ai/LangAlpha · 72 tokens

ui-design

Design-quality reference for financial-research visual output: typography, color, composition, and avoiding generic AI aesthetics.

ginlix-ai/LangAlpha · 24 tokens