nixtla-event-impact-modeler
Quantifies the impact of exogenous events on contract prices using TimeGPT and CausalImpact. Triggers on "event impact analysis", "model event effects", "quantify event impact", or "causal analysis".
npx skills add majiayu000/claude-skill-registry --skill nixtla-event-impact-modeler-intent-solutions-io-plugins-nixtla --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nixtla Event Impact Modeler Quantifies the causal impact of exogenous events on contract prices using TimeGPT forecasting and CausalImpact analysis. ## Overview This skill analyzes how external events (promotions, natural disasters, policy changes) affect contract prices over time. It combines historical price data with event details to quantify causal impacts using MCMC-based counterfactual modeling and TimeGPT forecasting. The skill produces impact estimates, adjusted forecasts, and visualizations for event-driven price changes. **Use cases**: Promotion effectiveness analysis, disaster impact quantification, policy change assessment, pricing anomaly investigation, event-aware forecasting. ## Prerequisites **Environment**: - `NIXTLA_TIMEGPT_API_KEY` (required for TimeGPT forecasting) **Dependencies**: ```bash pip install nixtla pandas causalimpact matplotlib ``` **Input requirements**: - `prices.csv`: Contract prices with columns `ds` (datetime), `price` (numeric) - `events.csv`: Event data with columns `ds` (datetime), `event` (string description) ## Instructions ### Step 1: Prepare data Load and validate contract price and event data using the data preparation script. ```bash
- Overview
- Prerequisites
- Instructions
- Step 1: Prepare data
- Step 2: Configure model
- Step 3: Execute analysis
- Step 4: Generate report
- Output
- Error Handling
- Examples
- Example 1: Promotion impact analysis
- Example 2: Natural disaster impact
- Resources
pip install nixtla pandas causalimpact matplotlib
python {baseDir}/scripts/prepare_data.py \
python {baseDir}/scripts/prepare_data.py --create-sample
python {baseDir}/scripts/configure_model.py \
python {baseDir}/scripts/analyze_impact.py \
python {baseDir}/scripts/generate_report.py \
python scripts/prepare_data.py --prices prices.csv --events events.csv
python scripts/configure_model.py --prices prepared_prices.csv --events prepared_events.csv --window-days 5
python scripts/analyze_impact.py --prices configured_prices.csv --events prepared_events.csv --niter 2000
python scripts/generate_report.py --impact-results impact_results.csv --adjusted-forecast adjusted_forecast.csvWhat does the nixtla-event-impact-modeler skill do?
Quantifies the impact of exogenous events on contract prices using TimeGPT and CausalImpact. Triggers on "event impact analysis", "model event effects", "quantify event impact", or "causal analysis".
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill nixtla-event-impact-modeler-intent-solutions-io-plugins-nixtla --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.