Macro Regime
This skill should be used when the user asks to "classify the macro regime", "score expansion versus recession", or create a dated regime signal for capital market assumptions and SAA research.
Install
$ agentstack add skill-nutdnuy-self-driving-portfolio-skill-macro-regime ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo 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.
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Passed review? Show it. Paste this badge into your README, it links to the public security report.
Links to your public security report.[](https://agentstack.voostack.com/security/report/skill-nutdnuy-self-driving-portfolio-skill-macro-regime) Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
macro-regime
The 4-regime classifier from Ang/Azimbayev/Kim (2026) §3.2.
Methodology
Four indicator families are scored on a [-1, +1] scale (positive = "hot", negative = "cold"). Each family is a z-score of recent readings vs a rolling 5-year baseline.
| Family | FRED series | | --------------------- | ------------------------------------ | | Growth | INDPRO (industrial production, YoY) | | Inflation | CPIAUCSL (headline CPI YoY) | | Monetary policy | DFF − T10Y3M (real-curve proxy: fed funds minus 10y-3m spread) | | Financial conditions | NFCI (Chicago Fed; positive = tight) |
The regime score is a fixed-weight projection (paper §3.2):
expansion = +growth −0.5·inflation −0.5·monetary −1.0·fci
late_cycle = +growth +1.0·inflation +1.0·monetary +0.5·fci
recession = −growth −0.5·inflation +0.0·monetary +1.0·fci
recovery = +growth −1.0·inflation −1.0·monetary −0.5·fciScores are softmax-normalised across the four labels. The argmax is the regime label; the full vector is exposed downstream.
Scripts
scripts/fetch_macro.py— pull an authenticated FRED vintage when
FRED_API_KEY is set; otherwise apply the observation-date cutoff to latest-revised public CSV history and disclose that policy in the artifact.
scripts/classify_regime.py— runs the scoring, writesregime.json.
Output contract
schemas/regime.schema.json. The notes field is filled by the LLM in agent mode; a templated version is filled when run from CLI.
Run through pipeline/orchestrator.py for schema gating and governed output. Treat direct CLI output as an isolated, ungoverned stage result.
CLI
python skills/macro-regime/scripts/classify_regime.py \
--as-of 2026-05-08 --out outputs/demo01/regime.jsonSource & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: nutdnuy
- Source: nutdnuy/self-driving-portfolio-skill
- License: MIT
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.