Risk assessment

Skill artherahq/skills/skills/risk-assessment

Reusable Agent Skills for quantitative finance research, extracted from the Aria toolchain.

Install

npx -y skills add artherahq/skills --skill risk-assessment

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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What its author says it does

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Decompose and judge the risk of a portfolio, strategy, or single position from its actual history. Trigger for "我的组合风险大吗", "最大回撤会有多深", "如果市场跌20%我亏多少", "该不该降仓", "组合太集中了吗", "portfolio risk", "VaR", "stress test my holdings", or whenever the user (1) holds or proposes a set of positions and asks how risky it is, (2) asks what a market drop would do to them, (3) asks whether to reduce/hedge/diversify, or (4) receives a strategy from another skill and needs its risk characterized before acting. Fire even for casual phrasing ("这样拿着安全吗", "is this too much NVDA?"). Do NOT trigger for backtest trustworthiness questions (that is backtest-validation) or pure data lookups.

SKILL.md

3.8 KB, as published. Nobody here has run it

Risk Assessment

Risk questions deserve numbers a risk committee would accept — not vibes, and not fabricated precision. This skill decomposes where the risk actually comes from (volatility, concentration, correlation, tail shape) and states plainly what the data cannot show.

Principles

Only the supplied history speaks. Every figure is computed from the portfolio's own return window. No assumed correlation matrices, no invented scenarios. If the sample never contained a crisis, say that the numbers understate crisis risk — do not simulate one silently.
Concentration is the risk most users cannot see. Ten highly-correlated names are one position in disguise. Effective N and average pairwise correlation are reported next to VaR, always.
The tail is not normal. Historical VaR/CVaR and a Cornish–Fisher adjustment are shown together; when skew/kurtosis diverge from normal, the report says which number to trust less.
Beta shocks are labeled as linear approximations. A −20% market shock estimate via beta is a floor, not a ceiling — real crashes raise correlations. The report says so verbatim, and omits the shock table entirely when no benchmark is supplied.

Workflow

Assemble inputs: per-asset return history (wide CSV), portfolio weights, optional benchmark. If weights don't sum to 1, the harness re-normalizes by gross exposure and discloses it — confirm with the user that gross exposure is what they meant.
Run the profile: python scripts/risk_profile.py --returns returns.csv --weights weights.csv [--benchmark bench.csv] --json report.json With no data, demonstrate with --demo.
Report in this order: risk level → main risk source → the flag list → core metrics → concentration/diversification → worst historical windows → beta shock estimate (if available). Lead with the diagnosis, not the table.
Translate flags for the user (see references/methodology.md for the thresholds and their rationale). "diversification_illusion" matters more to a retail holder than the CVaR decimal.
Any recommendation (reduce, hedge, diversify) must be framed as a research observation with its trigger flag attached — never as individualized investment advice. High-risk verdicts require explicit user confirmation before any downstream skill acts on them.
Always surface the disclosure lines from the report output. They are part of the deliverable, not boilerplate to trim.

Guardrails

No fabricated stress scenarios, correlations, or forward-looking loss estimates beyond the labeled linear beta approximation.
No "safe", "guaranteed", or "risk-free" language, at any risk level.
Missing inputs degrade honestly (skipped + reason), never silently.
A "low" risk level describes the sample window, not the future — say so.
Position-reduction suggestions are observations tied to flags; execution decisions belong to the user.