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.png
This 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.json
Output 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)
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
Scenario 3: "Is the situation getting better or worse?"
Run analysis
Focus on:
Trend from TGA peak to latest (is TGA releasing?)
Reserves recovery from trough
SOFR premium vs baseline
Present trend assessment with clear directional language
Optionally show week-over-week changes
Output Presentation Best Practices
Lead with conclusion
State status (EASING/TIGHTENING) upfront
Show key metrics concisely
:
TGA: $941B (-$17B from peak)
Reserves: $2,863B (+$15B from trough)
SOFR Premium: 4 bps (vs 19 bps peak)
Visualize
Always include chart for complex cases
Contextualize
Reference historical episodes when relevant
Avoid jargon overload
Explain "stealth tightening" simply if user seems unfamiliar
Advanced Usage
Custom Baseline
When analyzing a specific episode, set an appropriate pre-shutdown baseline:
python scripts/analyze_shutdown.py
\
--start-date
2025
-10-01
\
--baseline-date
2025
-09-24
\
--end-date
2025
-11-07
The baseline should be ~1 week before shutdown starts (to capture "normal" conditions).
Monitoring Routine
For ongoing tracking:
Weekly check
(Wednesdays/Thursdays):
Run analysis
Note status changes
Update user if significant shift
Event-triggered checks
:
Shutdown announcement → Start tracking
SOFR premium spikes (>15 bps) → Generate alert
Fed intervention (SRF usage) → Document
Shutdown resolution → Final analysis
Limitations and Caveats
Weekly data frequency
TGA/reserves only update weekly, limiting real-time precision
Month/quarter-end effects
SOFR naturally spikes at period-ends (unrelated to shutdowns)
Other liquidity factors
QT, regulatory changes, seasonal patterns also affect reserves
Attribution challenge
Hard to isolate shutdown effect from concurrent events
No predictive power
This skill describes current conditions, doesn't forecast
Troubleshooting
No recent data?
Check if today is before next Wednesday data release
Most recent weekly data is typically ~1 week lagged
SOFR premium calculation fails?
Verify both EFFR and SOFR have data for the date range
SOFR introduced April 2018; unavailable before
Chart rendering issues?
Ensure matplotlib is installed
Check date range has sufficient data points (need >2 weekly observations)
References
See bundled documentation:
references/historical_cases.md
- Detailed analysis of 2013, 2018-19, 2025 shutdowns
references/data_sources.md
- FRED API technical reference
External resources:
Original PDF report (user-provided) for full theoretical framework
NY Fed SOFR page:
https://www.newyorkfed.org/markets/reference-rates/sofr
FRED data:
https://fred.stlouisfed.org/