researching-stocks
Workflow for multi-step financial research requiring multiple data sources. Use for company comparisons, due diligence, comprehensive analysis, or complex financial questions.
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Workflow for multi-step financial research requiring multiple data sources. Use for company comparisons, due diligence, comprehensive analysis, or complex financial questions.
This skill should be used when the user asks to "run TabularEditor.exe", "deploy a model via CLI", "use Tabular Editor command line", "set up CI/CD for Power BI", "automate model deployment", "run BPA from comm...
This skill should be used when the user asks about ".tmuo files", "Tabular Editor user options", "TE3 preferences", "Preferences.json", "UiPreferences.json", "Layouts.json", "workspace database settings", "depl...
This skill should be used when the user asks to "create a BPA rule", "write a Best Practice Analyzer rule", "improve a BPA expression", "fix expression for BPA", "analyze BPA annotations", "check model for best...
This skill should be used when the user asks about "Tabular Editor documentation", "TE docs", "how to do X in Tabular Editor", "Tabular Editor features", "TE3 features", "C# scripts in Tabular Editor", "DAX scr...
Generate an HTML dashboard from meeting analysis. Use when the user wants to create a visual summary, build a dashboard, generate a report, or visualize meeting insights.
Audit dictionary usage against the Dictionary Usage Doctrine. Produces a severity-grouped report with minimal refactor suggestions.
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 documentat...
Generate production-ready forecasting experiments with StatsForecast and TimeGPT. Use when setting up model benchmarking or cross-validation. Trigger with 'scaffold experiment' or 'compare models'.
Audits Nixtla library usage and recommends cost-effective routing strategies. Scans TimeGPT, StatsForecast, and MLForecast patterns, identifies cost optimization opportunities, generates comprehensive usage rep...
Analyze Nixtla baseline forecasting results (sMAPE/MASE on M4 or other benchmark datasets). Use when the user asks about baseline performance, model comparisons, or metric interpretation for Nixtla time-series...
Provides expert Nixtla forecasting using TimeGPT, StatsForecast, and MLForecast. Generates time series forecasts, analyzes trends, compares models, performs cross-validation, and recommends best practices. Acti...
Generate benchmarking pipelines to compare forecasting models and summarize accuracy/speed trade-offs. Use when evaluating TimeGPT vs StatsForecast/MLForecast/NeuralForecast on a dataset. Trigger with "benchmar...
Scaffolds production-ready forecasting experiments with Nixtla libraries. Creates configuration files, experiment harnesses, multi-model comparisons, and cross-validation workflows for StatsForecast, MLForecast...
Analyze causal impact of events on time series forecasts using TimeGPT. Use when quantifying promotion or disaster effects. Trigger with 'event impact analysis' or 'causal analysis'.
Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Tri...
Generate production-ready Jupyter notebooks showcasing Nixtla forecasting workflows for statsforecast, mlforecast, and TimeGPT. Use when creating demos, building examples, or showcasing forecasting capabilities...
Transform data sources to Nixtla schema (unique_id, ds, y) with column inference. Use when preparing data for forecasting. Trigger with 'map to Nixtla schema' or 'transform data'.
Detects anomalies in time series data using TimeGPT. Identifies outliers, level shifts, and trend breaks without model training. Use when identifying anomalies, outliers, or unusual patterns in time series. Tri...
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".
Analyze Nixtla baseline forecasting results (sMAPE/MASE on M4 or other benchmark datasets). Use when the user asks about baseline performance, model comparisons, or metric interpretation for Nixtla time-series...
Generate comprehensive markdown benchmark reports from forecast accuracy metrics with model comparisons, statistical analysis, and regression detection. Use when analyzing baseline performance, comparing foreca...
Validates time series forecast quality metrics by comparing current performance against historical benchmarks. Detects degradation in MASE and sMAPE metrics. Activates when user mentions "validate forecast", "c...
Research and summarize Nixtla ecosystem updates and time-series forecasting content from the web and GitHub. Use when gathering release notes, recent changes, or best-practice references. Trigger with "Nixtla u...
Le classement utilise les étoiles du dépôt GitHub indexé. Plusieurs skills peuvent partager ce nombre : ce n’est ni un nombre d’installations ni une note de qualité.