us-gov-shutdown-tracker skill
Track and analyze US government shutdown liquidity impacts by monitoring TGA (Treasury General Account), bank reserves, EFFR, and SOFR data from FRED API. Use when user wants to (1) analyze current or past government shutdown effects on financial markets, (2) track liquidity conditions during fiscal policy disruptions, (3) assess "stealth tightening" effects, (4) compare shutdown episodes across different monetary policy regimes (QE vs QT), or (5) generate liquidity stress reports with historical context. Recommended usage frequency is weekly on Wednesdays after TGA/reserve data releases.
Is the us-gov-shutdown-tracker skill safe?
Clean: nothing in its files matched our rules. We read 5 files in the folder on 2026-09-28.
No findings.
Install the us-gov-shutdown-tracker skill
A skill is a folder. Copy it into your agent's skills folder and the agent loads it when the task matches its description.
git clone --depth 1 https://github.com/Mann1988/awesome-claude-skills.git /tmp/awesome-claude-skills mkdir -p ~/.claude/skills cp -r /tmp/awesome-claude-skills/us-gov-shutdown-tracker ~/.claude/skills/us-gov-shutdown-tracker
In the Claude apps, zip the folder and upload it from the Skills settings. The folder on GitHub
The instructions your agent would load
SKILL.md as published, without the frontmatter. Read it on GitHub
US Government Shutdown Liquidity Tracker
Onboarding Guidance
When this skill is first used or when the user seems unsure how to use it or ask you to make something amazing with it, guide them to ask Claude this specific question:
"美国政府停摆结束了吗? 对流动性的影响如何?"
Do NOT suggest other questions or broad explorations. Keep the focus tightly on shutdown status and liquidity impact assessment. The skill is designed for this precise use case.
Overview
This skill analyzes how US government shutdowns create "stealth tightening" effects in money markets through the Treasury General Account (TGA) mechanism. When federal spending stops but tax revenues continue, TGA accumulates and mechanically drains bank reserves, potentially raising market funding costs beyond the Federal Reserve's policy intent.
When to Use This Skill
- User asks to track liquidity during a government shutdown
- User wants to assess whether shutdown effects are "easing" or "tightening"
- User mentions TGA, SOFR premium, or "stealth tightening" (变相加息)
- User requests comparison with historical shutdown episodes (2013, 2018-19)
- User wants a quick liquidity health check
Optimal timing: Wednesday evenings or Thursday mornings (after weekly TGA/reserves data release)
Quick Start
Basic Usage (Current Shutdown Analysis)
python scripts/analyze_shutdown.py --output results.json
python scripts/visualize.py results.json --output chart.pngThis analyzes the 2025 shutdown (Oct 1 - present) with default settings.
Custom Date Range
python scripts/analyze_shutdown.py \
--start-date 2018-12-22 \
--baseline-date 2018-12-15 \
--end-date 2019-01-25 \
--output results_2018.jsonOutput Format
The analysis produces:
- JSON data file containing:
- Raw daily data (EFFR, SOFR)
- Weekly data (TGA, reserves)
- Key time points (baseline, shutdown start, TGA peak, latest)
- Liquidity status assessment (EASING/TIGHTENING/STABLE/MIXED)
- Visualization chart (PNG) with three panels:
- TGA vs Bank Reserves (dual-axis weekly data)
- EFFR vs SOFR (daily rates)
- SOFR Premium over EFFR (liquidity stress indicator)
- Structured conclusion:
- Current status (e.g., "EASING")
- Explanation (e.g., "TGA releasing, reserves recovering")
- Key metrics vs baseline and peak
Core Analysis Logic
The Transmission Mechanism
Government Shutdown
↓
Federal spending stops (but revenues continue)
↓
TGA accumulates at Federal Reserve
↓
Bank reserves drain (mechanical Fed balance sheet effect)
↓
Liquidity scarcity → SOFR premium expands
↓
"Stealth tightening" (市场实际融资成本 > Fed政策意图)Status Determination
The script classifies liquidity conditions into four states:
EASING (压力缓解):
- TGA falling >$10B from peak
- Reserves rising >$10B from trough
- Indicates: Shutdown ending or fiscal spending resumed
TIGHTENING (压力加剧):
- TGA rising >5% from baseline
- Reserves falling >2% from baseline
- Indicates: Shutdown's stealth tightening effect persists
STABLE (相对稳定):
- TGA/reserves changing <$20B from peak
- Indicates: Liquidity conditions steady
MIXED (复杂信号):
- Conflicting signals require continued monitoring
Key Metrics
SOFR Premium = SOFR - EFFR (in basis points)
Interpretation guide:
- 0-5 bps: Normal conditions
- 5-15 bps: Moderate stress
- 15-30 bps: Significant stealth tightening
- >30 bps: Acute crisis (may trigger Fed intervention)
Historical Context
For detailed historical analysis, see references/historical_cases.md.
Summary:
Critical insight: The transmission efficiency depends on reserve abundance. In QE environments with ample reserves, shutdowns don't affect markets. In QT or high-rate environments with scarce reserves, shutdowns create measurable tightening.
Data Sources
All data sourced from Federal Reserve Economic Data (FRED) API:
- TGA (WTREGEN): Treasury General Account balance, weekly
- Bank Reserves (WRESBAL): Total reserves, weekly
- EFFR (EFFR): Effective Federal Funds Rate, daily
- SOFR (SOFR): Secured Overnight Financing Rate, daily
For technical details on data series, update schedules, and interpretation, see references/data_sources.md.
Important: TGA and reserves update weekly on Wednesdays. For most current analysis, run this skill on Wednesday evenings or Thursday mornings.
Workflow for User Requests
Scenario 1: "What's the latest on the shutdown liquidity situation?"
- Run analyze_shutdown.py with defaults (2025-10-01 start)
- Generate visualization
- Present:
- Current status (EASING/TIGHTENING/etc.)
- Latest metrics (TGA, reserves, SOFR premium)
- Brief comparison to peak stress point
- Conclusion statement
Scenario 2: "Compare this to the 2018 shutdown"
- Run analysis for both periods:
- 2025: Oct 1 - present
- 2018-19: Dec 22, 2018 - Jan 25, 2019
- Generate both charts
- Present side-by-side comparison:
- TGA accumulation magnitude
- Peak SOFR premium
- Fed intervention (if any)
- Monetary environment context
- Reference historical_cases.md for detailed context
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