Skip to main content
Run any Skill in Manus
with one click
fnauman
GitHub creator profile

fnauman

Repository-level view of 6 collected skills across 1 GitHub repositories.

skills collected
6
repositories
1
updated
2026-05-13
repository map

Where the skills live

Top repositories by collected skill count, with their share of this creator catalog and occupation spread.

repository explorer

Repositories and representative skills

report-generation
executive-secretaries-and-executive-administrative-assistants

Convert ts-agents workflow outputs, run manifests, plots, JSON, CSV files, and short Markdown reports into stakeholder-facing Markdown, Quarto, HTML, or PDF reports. Use when the user asks for a report, executive summary, research appendix, or reusable analysis deliverable from existing ts-agents artifacts.

2026-05-13
time-series-activity-recognition
data-scientists-152051

End-to-end workflow for labeled-stream activity recognition: prepare or download data, run window-size selection, evaluate a windowed classifier, and produce plots + a short report. Use when you need a reproducible CLI workflow artifact or evaluation bundle.

2026-04-20
time-series-forecasting
data-scientists-152051

Forecast/predict future values of a time series, choose reasonable baselines, and compare forecasting methods on arbitrary series loaded from ts-agents data.

2026-04-05
time-series-classification
data-scientists-152051

Supervised time series classification: choose and run classifiers (KNN/DTW, ROCKET variants, HIVE-COTE), compare models, and report accuracy. Use when the user asks to classify/categorize time series, build a classifier, or compare time series classification algorithms.

2026-04-01
time-series-decomposition
data-scientists-152051

Decompose a time series into trend/seasonal/residual components (STL, MSTL, Holt-Winters). Use when the user asks about trend, seasonality, detrending, or wants residuals for anomaly detection/forecasting.

2026-04-01
time-series-diagnostics
data-scientists-152051

Quick EDA and diagnostics for a time series: descriptive stats, autocorrelation, and periodicity. Use when the user asks "what does this series look like?", "is there seasonality?", "what's the period?", or before choosing decomposition/forecasting parameters.

2026-04-01
Showing 1 of 1 repositories
All repositories loaded