Agent Skills by alvarolaraff
Showing 32 vetted skills indexed across 1 GitHub repositories.
goals-tracker
Set and track training goals by distance, time, or elevation across periods.
tri-combined-load
Merge run, ride, and swim metrics into a single daily training-load value.
swim-volume
Aggregate weekly and monthly swim distance, time, and sessions from Strava SQLite.
ride-climbing
Compute VAM and watts per kilogram from Strava ride data.
matched-activities
Group Run activities by route similarity and report pace progression.
athlete-snapshot
Recompute physiological metrics from the local Strava database and write a timestamped snapshot.
tri-discipline-balance
Compute run, ride, and swim training distribution percentages from Strava activities.
weekly-log
Aggregate Strava activities by ISO week and sport into structured JSON.
training-load
Calculate CTL, ATL, and TSB from local Strava training data.
gear-mileage
Aggregate per-gear mileage from Strava activity history and determine retirement readiness.
run-race-predictor
Predict 5k, 10k, half-marathon, and marathon times using Riegel and VDOT methods.
memory-consolidate
Audit memory entries for age, vagueness, and data leaks.
readiness-today
Compute a daily training readiness verdict from TSB, ACWR, and 48-hour load.
session-analysis
Analyze a Strava training session into structured JSON with splits and HR drift.
ride-power-curve
Compute mean-max power curves from cycling rides with watts streams.
strava-coach
Query local Strava data to generate coaching insights and training plans.
polarization-check
Analyze 30 days of HR data to classify training zones and output a structured JSON verdict.
swim-css
Compute Critical Swim Speed from 400m and 200m time trials.
consistency
Analyze Strava activity data to compute training consistency metrics.
run-cadence-form
Analyze running cadence and stride-length trends from Strava SQLite data.
ride-tss-load
Compute TSS, NP, IF, and VI from Strava watts streams via Python CLI.
swim-swolf
Compute per-session SWOLF efficiency and analyze trends across recent pool swims.
strava-sync
Sync Strava activity data into a local SQLite cache via Python CLI.
overtraining-check
Compute ACWR, monotony, and strain from daily loads to detect overtraining risk.