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SKILL verified MIT Self-run

Asset Class Cma

skill-nutdnuy-self-driving-portfolio-skill-asset-class-cma · by nutdnuy

This skill should be used when the user asks to "build capital market assumptions", "estimate asset-class returns", or create regime-conditioned return, volatility, and confidence inputs for portfolio construction.

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Install

$ agentstack add skill-nutdnuy-self-driving-portfolio-skill-asset-class-cma

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • ✓ Prompt-injection patterns
  • ✓ Secret / credential exfiltration
  • ✓ Dangerous shell & filesystem operations
  • ✓ Untrusted network calls
  • ✓ Known-malicious package signatures

What it can access

  • ✓ Network access No
  • ✓ Filesystem access No
  • ✓ Shell / process execution No
  • ✓ Environment & secrets No
  • ✓ Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

✓ Security review passed
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● 26d ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

asset-class-cma

Given a list of tickers and the regime classification, produce a CMA per ticker.

Methodology

  1. Pull adjusted close prices via yfinance (default 10y daily lookback) and

reject every row after the shared as-of date.

  1. Compute geometric mean return and annualised volatility from log-returns.
  2. Apply a regime-conditional tilt to expected return:

| Asset class proxy | expansion | late_cycle | recession | recovery | | --- | --- | --- | --- | --- | | Equities (SPY, EFA, EEM, VNQ) | +1.5% | −0.5% | −3.0% | +2.0% | | Treasuries (IEF, BIL) | −0.5% | −0.5% | +1.5% | +0.5% | | Credit (LQD) | 0.0% | −1.0% | −1.5% | +1.0% | | Inflation-linked (TIP, GLD) | +0.5% | +1.5% | +0.0% | −0.5% |

Tilts are scaled by the regime's softmax probability so that a low-confidence call applies a smaller tilt.

  1. Confidence (0–1) = sample-size factor × vol-stability factor

× regime-confidence factor.

Stop when any IPS ticker is unavailable or has fewer than 60 usable returns. Never shrink the mandate universe silently.

CLI

python skills/asset-class-cma/scripts/build_cma.py \
       --regime outputs/demo01/regime.json \
       --tickers SPY,EFA,EEM,IEF,LQD,TIP,GLD,VNQ,BIL \
       --as-of 2026-05-08 \
       --out outputs/demo01/cmas.json

Run through pipeline/orchestrator.py for schema gating and governed output.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.

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