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Agent skill

Grad Capm

by asgard-ai-platform in asgard-ai-platform/skills

Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs.

MITAuto-check passedSales & Support

Install Grad Capm

skills CLI
$ npx skills add asgard-ai-platform/skills --skill grad-capm -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install asgard-ai-platform/skills grad-capm --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/asgard-ai-platform/skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/grad-capm .claude/skills/grad-capm && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
grad-capm
GitHub stars
242
Token cost
~1.1k tokens
SKILL.md length
434 words
Files
4 (incl. scripts, references)
Skills in repo
207
Repo updated
First seen
Licence
MIT

At a glance

Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs.

  • Works in 4 steps: Identify Inputs → Compute Expected Return → Plot on Security Market Line → …
  • The user needs to calculate expected return on an asset
  • SKILL.md covers Overview, When to Use, When NOT to Use and Assumptions, plus 5 more sections
  • Runs Python scripts from its folder; calls python

What it does

Grad Capm is an agent skill from asgard-ai-platform/skills. Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for risk, or when they ask 'what return should I expect', 'what is the risk premium', or 'how does beta affect pricing'.

Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 6 other files, including scripts and reference files (for example `examples/sample_input.json`, `references/derivation.md` and `scripts/capm.py`).

It sits in Sales & Support, covering Pricing strategy. The repository describes itself as: 301 open-source coding agent skills across 22 domains — methodology, judgment & gotchas packaged as Claude Agent Skills for the Asgard AI Platform. The licence is MIT.

When your agent uses it

  • The user needs to calculate expected return on an asset
  • Interpret beta as systematic risk exposure
  • Evaluate whether an investment compensates for risk
  • They ask what return should I expect

Example prompts

  • “what return should I expect”
  • “what is the risk premium”
  • “how does beta affect pricing”
  • “/grad-capm”

Requirements

  • Python 3

Workflow steps

4 steps, taken from the step headings in SKILL.md.

  1. Identify Inputs
  2. Compute Expected Return
  3. Plot on Security Market Line
  4. Interpret and Decide

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Grad Capm loads about 1.1k tokens when it runs, and up to ~1.8k if it reads all its reference files. Until then it costs about 101 tokens; SKILL.md has 434 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~101
When it runs · the whole SKILL.md, loaded when a task matches
~1.1k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~1.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 434 words, ~1,131 tokens.

Download SKILL.mdSave it as .claude/skills/grad-capm/SKILL.md (or your agent's skills folder). This skill also uses 3 other files; get the full folder from GitHub.
name
grad-capm
description
Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Use this skill when the user needs to calculate expected return on an asset, interpret beta as systematic risk exposure, evaluate whether an investment compensates for risk, or when they ask 'what return should I expect', 'what is the risk premium', or 'how does beta affect pricing'.
metadata.category
WP-26 財務理論
metadata.tags
CAPM, beta, systematic-risk, expected-return, Sharpe, risk-premium

Capital Asset Pricing Model (CAPM)

Overview

CAPM (Sharpe, 1964; Lintner, 1965) establishes a linear relationship between systematic risk and expected return. The model states that the expected return on any asset equals the risk-free rate plus a premium for bearing market risk, scaled by the asset's beta.

When to Use

  • Estimating required rate of return for equity valuation
  • Calculating cost of equity in WACC
  • Comparing asset risk via beta
  • Evaluating portfolio performance against the Security Market Line (SML)

When NOT to Use

  • When the asset has significant exposure to size, value, or other factors beyond market risk
  • For illiquid or non-traded assets where beta estimation is unreliable
  • When market portfolio proxy is questionable (Roll's critique)

Assumptions

IRON LAW: CAPM only prices SYSTEMATIC risk — diversifiable (unsystematic)
risk earns NO premium. An asset's expected return depends solely on its
beta with the market portfolio.

Key assumptions:

  1. Investors are mean-variance optimizers with homogeneous expectations
  2. A risk-free asset exists for unlimited borrowing and lending
  3. Markets are frictionless — no taxes, transaction costs, or short-selling constraints
  4. All assets are infinitely divisible and publicly traded

Methodology

Step 1 — Identify Inputs
  • Risk-free rate (Rf): government bond yield matching investment horizon
  • Market return E(Rm): historical average or forward-looking estimate
  • Beta: regression of asset returns against market returns
Step 2 — Compute Expected Return

E(Ri) = Rf + Bi x (E(Rm) - Rf). See references/derivation.md for the derivation from mean-variance optimization.

Step 3 — Plot on Security Market Line

Assets above the SML are undervalued (positive alpha); below are overvalued (negative alpha).

Step 4 — Interpret and Decide
  • Beta > 1: amplifies market moves, higher risk-higher expected return
  • Beta < 1: dampens market moves, lower risk-lower expected return
  • Beta = 0: returns equal the risk-free rate
Show full SKILL.md (179 more words)Show less

Output Format

⚠️ Decimal vs percent: When passing values to or from the bundled script, all rates (risk_free, market_return, beta_contribution, expected_return, alpha) are decimals — 0.05 means 5%, NOT 5.0. The narrative report below renders them as percentages for humans, but never mix the two in the same JSON object.

markdown
## CAPM Analysis: [Asset / Portfolio]

### Inputs
| Parameter | Value | Source |
|-----------|-------|--------|
| Risk-free rate (Rf) | x% | [source] |
| Market return E(Rm) | x% | [source] |
| Beta | x.xx | [estimation method] |

### Expected Return
- E(Ri) = Rf + B x (E(Rm) - Rf) = x%

### SML Assessment
- Alpha = Actual return - Expected return = x%
- Interpretation: [undervalued / overvalued / fairly priced]

### Limitations in This Context
- [Note any assumption violations]

Gotchas

  • Beta is backward-looking; future beta may differ from historical estimates
  • Choice of market proxy matters enormously (Roll's critique, 1977)
  • CAPM assumes a single risk factor; empirical evidence supports multi-factor models
  • Risk-free rate selection (T-bill vs T-bond) affects results significantly
  • Beta estimation is sensitive to return frequency (daily vs monthly) and sample period
  • CAPM fails to explain the low-beta anomaly (low-beta stocks outperform predictions)

Scripts

ScriptDescriptionUsage
scripts/capm.pyCompute CAPM expected return and alphapython scripts/capm.py --help

Run python scripts/capm.py --verify to execute built-in sanity tests.

References

  • Sharpe, W. (1964). Capital asset prices. Journal of Finance, 19(3), 425-442.
  • Lintner, J. (1965). The valuation of risk assets. Review of Economics and Statistics, 47(1), 13-37.
  • Roll, R. (1977). A critique of the asset pricing theory's tests. Journal of Financial Economics, 4(2), 129-176.

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (scripts, references) in grad-capm of asgard-ai-platform/skills.

  • SKILL.md
  • examples/sample_input.json
  • references/derivation.md
  • scripts/capm.py

Open the folder on GitHubat commit 4e7f4f8

Compare with similar skills

Grad Capm next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Grad Capm compared with similar skills
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Grad Capm this skillasgard-ai-platform/skills242—~1.1kAutomated safety check: PassMIT
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Niche Opportunity Finderzanecole10/software-tailor-skills106—~4.2kAutomated safety check: PassNone
Pricing Strategyalirezarezvani/claude-skills28k1 repos~3.5kAutomated safety check: PassMIT
Software Pricing Calculatorzanecole10/software-tailor-skills106—~3.7kAutomated safety check: PassNone

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Categories

Questions about Grad Capm

What does Grad Capm do?

Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs. Grad Capm is an agent skill from asgard-ai-platform/skills. Apply the Capital Asset Pricing Model (CAPM) to estimate expected returns and assess risk-return tradeoffs.

When should I use Grad Capm?

Grad Capm fits situations like: the user needs to calculate expected return on an asset; interpret beta as systematic risk exposure; evaluate whether an investment compensates for risk; they ask what return should I expect.

How do I install Grad Capm in Claude Code?

Run `npx skills add asgard-ai-platform/skills --skill grad-capm -a claude-code`. Or copy the skill folder (grad-capm in asgard-ai-platform/skills) into .claude/skills/grad-capm in your project. Claude Code loads it when a task matches its description.

How do I install Grad Capm in Codex?

Run `npx skills add asgard-ai-platform/skills --skill grad-capm -a codex`. Or copy the skill folder (grad-capm in asgard-ai-platform/skills) into .agents/skills/grad-capm in your project. Codex loads it when a task matches its description.

Can I use Grad Capm in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add asgard-ai-platform/skills --skill grad-capm -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/grad-capm, .gemini/skills/grad-capm, .github/skills/grad-capm and .opencode/skills/grad-capm in your project.

What does Grad Capm need to run?

Going by SKILL.md and its folder, Grad Capm needs Python for the scripts in its folder and the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Grad Capm access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Grad Capm safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Grad Capm use?

Grad Capm is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Grad Capm use?

About 1.1k tokens (SKILL.md is roughly 4.5k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 627 tokens, read only when the agent opens those files.

What are the alternatives to Grad Capm?

Skills that share tags, products or a category with Grad Capm: Pricing Strategy (freekmurze/dotfiles, 1k stars), Profit Margin Calculator Amazon (nexscope-ai/eCommerce-Skills, 1.1k stars), Niche Opportunity Finder (zanecole10/software-tailor-skills, 106 stars) and Pricing Strategy (alirezarezvani/claude-skills, 28k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Grad Capm?

asgard-ai-platform (a GitHub organization) maintains it in asgard-ai-platform/skills, which has 242 GitHub stars. The repository holds 207 skills in this directory. The repository was last updated on June 6, 2026.

Source: asgard-ai-platform/skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.