Nixtla Explain Analyst

by intent-solutions-io

skill

Analyze and explain TimeGPT forecast results in plain English. Generates

Skill Details

Repository Files

1 file in this skill directory


name: nixtla-explain-analyst description: 'Analyze and explain TimeGPT forecast results in plain English. Generates executive summaries with driver analysis.

Use when stakeholders need forecast explanations, board presentations, or compliance documentation.

Trigger with "explain forecast", "why is the forecast", "forecast narrative".

' allowed-tools: Read,Glob,Grep version: 1.0.0 author: Jeremy Longshore jeremy@intentsolutions.io license: MIT

Nixtla Explain Analyst

Overview

Generate plain-English explanations of TimeGPT forecasts for non-technical stakeholders, with a short executive narrative plus driver-oriented analysis.

Prerequisites

  • A forecast output artifact to explain (CSV/JSON/markdown), or a path under the plugin workspace containing results.
  • If using TimeGPT outputs: access to the TimeGPT run metadata used to generate the forecast.

Instructions

  1. Locate the forecast results file(s) and any run metadata (model, horizon, frequency, training window).
  2. Summarize forecast context: what is being forecast, horizon, and any known events/holidays/regressors.
  3. Explain forecast shape using: baseline level, trend direction, seasonal pattern (if present), and uncertainty.
  4. Provide driver analysis at the level supported by available data (do not fabricate causal drivers).
  5. Produce a stakeholder-ready narrative plus a short technical appendix (assumptions + limitations).

Output

  • Executive: 1-page summary for C-level
  • Technical: Detailed analysis for data science
  • Compliance: SOX/Basel III documentation

Error Handling

  • If only point forecasts are available, state that uncertainty intervals are unavailable and recommend generating prediction intervals.
  • If inputs are missing (no horizon/freq), request the minimum details required to interpret results.
  • If drivers/regressors are not provided, restrict “drivers” to observable components (trend/seasonality/outliers).

Examples

User: “Explain the Q4 forecast for the board presentation.”

Response structure:

  • Executive summary (3–6 bullets)
  • What changed vs last quarter (trend/seasonality)
  • Key risks and uncertainty
  • Assumptions and limitations (what was/wasn’t modeled)

Resources

  • Plugin docs and outputs under 005-plugins/nixtla-forecast-explainer/

Related Skills

Attack Tree Construction

Build comprehensive attack trees to visualize threat paths. Use when mapping attack scenarios, identifying defense gaps, or communicating security risks to stakeholders.

skill

Grafana Dashboards

Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces.

skill

Matplotlib

Foundational plotting library. Create line plots, scatter, bar, histograms, heatmaps, 3D, subplots, export PNG/PDF/SVG, for scientific visualization and publication figures.

skill

Scientific Visualization

Create publication figures with matplotlib/seaborn/plotly. Multi-panel layouts, error bars, significance markers, colorblind-safe, export PDF/EPS/TIFF, for journal-ready scientific plots.

skill

Seaborn

Statistical visualization. Scatter, box, violin, heatmaps, pair plots, regression, correlation matrices, KDE, faceted plots, for exploratory analysis and publication figures.

skill

Shap

Model interpretability and explainability using SHAP (SHapley Additive exPlanations). Use this skill when explaining machine learning model predictions, computing feature importance, generating SHAP plots (waterfall, beeswarm, bar, scatter, force, heatmap), debugging models, analyzing model bias or fairness, comparing models, or implementing explainable AI. Works with tree-based models (XGBoost, LightGBM, Random Forest), deep learning (TensorFlow, PyTorch), linear models, and any black-box model

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Query Writing

For writing and executing SQL queries - from simple single-table queries to complex multi-table JOINs and aggregations

skill

Pydeseq2

Differential gene expression analysis (Python DESeq2). Identify DE genes from bulk RNA-seq counts, Wald tests, FDR correction, volcano/MA plots, for RNA-seq analysis.

skill

Scientific Visualization

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

skill

Skill Information

Category:Skill
License:MIT
Version:1.0.0
Allowed Tools:Read,Glob,Grep
Last Updated:12/22/2025