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 gauss314/skills --skill portfoliogit clone --depth 1 https://github.com/gauss314/skillsWrote 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/gauss314/skills/portfolio)<a href="https://agentmods.dev/skills/gauss314/skills/portfolio"><img src="https://agentmods.dev/badge/skills/gauss314/skills/portfolio/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/gauss314/skills/portfolio"><img src="https://agentmods.dev/badge/skills/gauss314/skills/portfolio.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 | $0.00085 | $0.02819 |
| Opus 5 | $0.00043 | $0.01409 |
| Sonnet 5 | $0.00017 | $0.00564 |
| Haiku 4.5 | $0.00009 | $0.00282 |
Grade A, and why
portfolio 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 10d 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 — 241 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portfolio — Optimización Cuantitativa de Portafolios
Este skill implementa 3 enfoques de optimización de portafolios desde el material del curso
(notebook Clase_08_teoria_2025_portafolio.ipynb y PDF Portafolios 2025 Ucema.pdf):
- Markowitz / Media-Varianza — Optimización convexa vía
scipy.optimize- simulación Monte Carlo + frontera eficiente + CML.
- Black-Litterman — Combinación bayesiana de retornos de equilibrio de mercado (CAPM inverso) con views del inversor, incluyendo matriz de incertidumbre Ω (método Idzorek).
- HRP / HERC / NCO — Construcción jerárquica de portafolios mediante clustering (single/complete/average/ward), risk parity y NCO con restricciones.
Todos los scripts usan solo numpy, pandas y scipy. Sin dependencias pesadas.
Este skill es autónomo: funciona sin skills/backtesting.
Para ratios de performance post-optimización (Sharpe, Sortino, VaR, drawdowns, etc.)
consultar el skill hermana:
skills/backtesting.
Part of the Gauss314 Skills Repository.
File Map
skills/portfolio/
├── SKILL.md ← Este archivo
├── references/
│ ├── PORTFOLIO_THEORY.md ← MPT, Markowitz, frontera eficiente (ES)
│ ├── BLACK_LITTERMAN.md ← BL: prior, views, posterior, omega (ES)
│ ├── HIERARCHICAL.md ← HRP, HERC, NCO, clustering (ES)
│ └── RISK_MEASURES.md ← VaR, CVaR, MAD, MSV, DR, MDD (ES)
├── assets/
│ ├── sample_prices.csv ← Precios multi-activo para ejemplos
│ ├── sample_returns.csv ← Retornos multi-activo
│ ├── sample_mcaps.json ← Market caps para Black-Litterman
│ └── defaults.json ← Parámetros default
├── scripts/
│ ├── __init__.py
│ ├── portfolio.py ← Core: Markowitz, Sharpe, Monte Carlo, frontera
│ ├── black_litterman.py ← BL: prior, posterior, omega, views
│ ├── hierarchical.py ← HRP/HERC/NCO: clustering, risk parity, constraints
│ ├── risk_measures.py ← VaR, CVaR, MAD, MSV, MDD, DR
│ ├── covariance.py ← Covarianza: hist, ledoit-wolf, oas, ewma
│ └── cli.py ← CLI unificada (12 modos)
└── tests/
├── __init__.py
└── test_portfolio.py ← Tests + validación contra notebook
What ships with it
21 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.
- assets/defaults.json 124 B
- assets/sample_classes.csv 260 B
- assets/sample_constraints.csv 228 B
- assets/sample_mcaps.json 419 B
- assets/sample_prices_16.csv 74 KB
- assets/sample_prices.csv 7.3 KB
- assets/sample_returns_16.csv 85 KB
- assets/sample_returns.csv 23 KB
- references/BLACK_LITTERMAN.md 2.7 KB
- references/HIERARCHICAL.md 3.2 KB
- references/PORTFOLIO_THEORY.md 3.3 KB
- references/RISK_MEASURES.md 2.1 KB
- scripts/__init__.py 0 B runs code
- scripts/black_litterman.py 8.6 KB runs code
- scripts/cli.py 13 KB runs code
- scripts/covariance.py 4.4 KB runs code
- scripts/hierarchical.py 12 KB runs code
- scripts/portfolio.py 8.5 KB runs code
- scripts/risk_measures.py 4.0 KB runs code
- tests/__init__.py 0 B runs code
- tests/test_portfolio.py 11 KB runs code
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.
- 10d ago First seen · 241 lines · 85 tokens per session scan A 0ad591366259
portfolio is a skill published in the GitHub repository gauss314/skills (237 stars, last pushed 2mo ago), licensed MIT. It adds 85 tokens to every session and 2,819 once invoked, about $0.0004 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.
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