Browse published AI skills maintained by nadavyigal. Compare skill descriptions and follow each source repository for setup instructions.
Indexed skills
24Repository stars across indexed skills
0Repository forks
0Average repository stars per indexed skill
0Repository stars may repeat across skills from the same repository.
Chat-based goal classification (habit/distance/speed/race) with constraint clarification. Use during onboarding or when user wants to update their running goals through conversation with weekly commitment disco...
Converts run telemetry into insights, recovery recommendations, and next-session nudges.
Converts run telemetry and user notes into structured reflections with confidence scores. Use after runs to capture qualitative feedback and generate next-step guidance with safety checks for pain or abnormal h...
Detects unsafe spikes in training load and emits SafetyFlags with conservative adjustments.
Recomputes upcoming workouts based on recent runs, feedback, and safety limits.
Classifies pre-run readiness (proceed/modify/skip) using recent load, recovery, and self-reported signals.
Detects unsafe training load spikes (>20-30% week-over-week) and emits safety flags. Use in nightly background jobs or when reviewing weekly training volume with conservative adjustment recommendations.
Entry point for Codex-discoverable skills used by the Run-Smart AI coach.
Generates route specifications with distance and elevation constraints. Use when user asks for route suggestions, wants to explore new running paths, or needs safer surface recommendations for upcoming workouts...
Recomputes upcoming workouts based on recent runs and user feedback. Use when recent performance deviates from plan, user provides negative feedback, or recovery signals indicate adjustment needed with determin...
Pre-run safety gate that evaluates readiness and recommends proceed/modify/skip decisions. Use before scheduled workouts to assess recovery status, injury signals, and training load based on sleep, soreness, an...
Generates pacing and fueling strategies for target races based on current fitness and course constraints.
Translates planned workouts into execution cues, purpose explanations, and common mistakes to avoid. Use before workouts when user asks "what should I do?" or "how do I run this session?" with beginner-friendly...
Converts telemetry and user notes into a structured reflection, confidence score, and next-step guidance.
Translates planned workouts into clear execution cues, purpose, mistakes to avoid, and substitutions.
Guides users through clarifying goals, constraints, and weekly commitments via chat.
Generates 14–21 day personalized running training plans with safe load progression. Use when user completes onboarding, requests a new plan, updates their running goals, or wants to export a training calendar.
Generates race-day pacing and fueling strategies with contingency plans. Use when user has an upcoming race, asks for race preparation advice, or wants a printable race-day plan with segment pacing and fueling...
Generates 14–21 day running plans tailored to onboarding data and recent performance.
Generates route specifications (distance, elevation, constraints) compatible with the app’s mapping layer.
Analyzes completed runs to provide effort assessment, recovery recommendations, and next-session guidance. Use immediately after user saves a run, asks "how did I do?", or requests recovery advice.
Reference catalog for all running coach AI skills, shared TypeScript contracts, safety guardrails, and telemetry conventions. Use when working with any running coach feature to understand data schemas, safety p...
Identifies missed training sessions and proposes plan reshuffles with motivational support. Use for weekly check-ins, when multiple sessions are skipped, or when user asks for help getting back on track.
Identifies missed sessions or inconsistency and proposes plan reshuffles with motivational nudges.
The default order uses GitHub repository stars from our indexed data. Stars belong to the source repository and may be shared by multiple skills. They indicate repository interest, not skill installs or an independent quality score.