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 BlueWaterCorp/RiskModels_API --skill portfolio-hedgegit clone --depth 1 https://github.com/BlueWaterCorp/RiskModels_APIWrote 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/bluewatercorp/riskmodels_api/portfolio-hedge)<a href="https://agentmods.dev/skills/bluewatercorp/riskmodels_api/portfolio-hedge"><img src="https://agentmods.dev/badge/skills/bluewatercorp/riskmodels_api/portfolio-hedge.svg" alt="Measured on agentmods" 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 | $0.00718 |
| Opus 5 | $0.00043 | $0.00359 |
| Sonnet 5 | $0.00017 | $0.00144 |
| Haiku 4.5 | $0.00009 | $0.00072 |
Grade B, and why
portfolio-hedge scanned grade B with 1 finding 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 3d 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.
Subtle steeringmediumPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
neutralize; never tell the user to place, trim, or rebalance a trade, and never How it starts
The opening of the file, as written. The whole thing — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Portfolio & position hedging (ERM3 cascade + Lstar)
Report the ETF hedge legs the decomposition implies for a position or portfolio. This skill wraps the hosted RiskModels MCP and reports tool outputs; it computes nothing itself.
What to call
riskmodels_hedge_position— one ticker: scale the L-level ETF hedge ratios to a dollar position.riskmodels_hedge_portfolio— a weighted book: hedge ratios at the chosen cascade level (L1/L2/L3), scaled by notionals and aggregated into ETF USD hedge legs.riskmodels_analyze_portfolio— holdings-weighted L1/L2/L3 hedge-level aggregate when the user wants the whole-book view across depths.riskmodels_get_lstar/riskmodels_batch_lstar— dispatch the simplest cascade level that clears the marginal-ER threshold for a name (or basket) and return the residual-return series after that hedge.
For an ETF or 13F filer named instead of pasted tickers, resolve real holdings first
(riskmodels_search_etfs → riskmodels_get_etf_holdings, or riskmodels_search_filers
→ riskmodels_get_filer_holdings) and treat the result as the portfolio. Never
fabricate or approximate holdings — if no tool covers the portfolio, say so and ask
the user to paste the positions.
How to present
- Name each ETF leg and frame it as what it would mechanically neutralize, e.g. "$0.62 of SPY per $1 of book neutralizes the market leg." A hedge ratio is a model output, like a beta — reporting it is reporting the math.
- Aggregate legs across positions into a single per-ETF USD figure for the book.
- If residual ER is high, note that the leftover is stock-specific and not hedgeable with sector/market ETFs.
- Negative hedge ratios are valid (orthogonalization); don't read a negative market leg as "short the market" — it often offsets beta embedded in the sector/subsector legs.
Boundary
You are an analyst, not an investment adviser. Report what each hedge leg would neutralize; never tell the user to place, trim, or rebalance a trade, and never assess whether the book is suitable for them. No options, swaps, or derivatives — ETF legs only. Always call the tools before quoting figures.
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.
- 3d ago First seen · 60 lines · 85 tokens per session scan B 91377aa708bb
portfolio-hedge is a skill published in the GitHub repository BlueWaterCorp/RiskModels_API (0 stars, last pushed 3d ago), licensed Apache-2.0. It adds 85 tokens to every session and 718 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-04.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
trading-risk-gate
Unified pre-trade safety gate: Ruin check (Law #1), ergodicity audit, and win-rate dominance validation. Absorbs: ergodicity-check, law-of-ruin, win-rate-dominance.
vectorbt
High-performance vectorized backtesting with parameter optimization, portfolio simulation, and rich performance metrics.