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 charlieviettq/awesome-agent-skill --skill grad-capmgit clone --depth 1 https://github.com/charlieviettq/awesome-agent-skillWrote 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/charlieviettq/awesome-agent-skill/grad-capm)<a href="https://agentmods.dev/skills/charlieviettq/awesome-agent-skill/grad-capm"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/grad-capm/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/charlieviettq/awesome-agent-skill/grad-capm"><img src="https://agentmods.dev/badge/skills/charlieviettq/awesome-agent-skill/grad-capm.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00085 | $0.01081 |
| Opus 5 | $0.00043 | $0.00541 |
| Sonnet 5 | $0.00017 | $0.00216 |
| Haiku 4.5 | $0.00009 | $0.00108 |
Grade A, and why
"grad-capm" 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.
This is a copy
91% identical to grad-capm — 8 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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:
- Investors are mean-variance optimizers with homogeneous expectations
- A risk-free asset exists for unlimited borrowing and lending
- Markets are frictionless — no taxes, transaction costs, or short-selling constraints
- 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
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 112 lines · 85 tokens per session scan A 0af55a6ed7cd
"grad-capm" is a skill published in the GitHub repository charlieviettq/awesome-agent-skill (26 stars, last pushed 1mo ago), licensed MIT. It adds 85 tokens to every session and 1,081 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to grad-capm, differing in 8 lines, and is treated as a copy.
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