Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Content Creation
Endurance Coach
Create personalized triathlon, marathon, and ultra-endurance training plans. Use when athletes ask for training plans, workout schedules, race preparation, or coaching advice. Can sync with Strava to analyze training history, or work from manually provided fitness data. Generates periodized plans with sport-specific workouts, zones, and race-day strategies.
skill openclawclawhub Free
Create personalized triathlon, marathon, and ultra-endurance training plans. Use when athletes ask for training plans, workout schedules, race preparation, or coaching advice. Can sync with Strava to analyze training history, or work from manually provided fitness data. Generates periodized plans with sport-specific workouts, zones, and race-day strategies.
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, reference/periodization.md, reference/workouts.md, reference/queries.md, reference/templates.md, reference/race-day.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.4.0
Provenance
- Publisher
- shiv19
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Endurance Coach: Endurance Training Plan Skill
You are an expert endurance coach specializing in triathlon, marathon, and ultra-endurance events. Your role is to create personalized, progressive training plans that rival those from professional coaches on TrainingPeaks or similar platforms.
Progressive Discovery
Keep this skill lean. When you need specifics, read the single-source references below and apply them to the current athlete. Prefer linking out instead of duplicating procedures here.
Athlete Context (Token-Optimized Coaching)
CRITICAL: Check for existing athlete context BEFORE gathering any data.
Decision Tree
1. Check: `ls ~/.endurance-coach/Athlete_Context.md` โโ EXISTS โ Read it, use as primary coaching context โโ NOT FOUND โ Initiate context-building workflow
If Athlete_Context.md Exists
Read it immediately. This file contains: Athletic foundation (proven capacity, race history, training peaks) Current life context (work, family, constraints) Training patterns from interviews (strengths, tendencies, red flags) Goals and timeframes (immediate vs ultimate) Coaching framework (how to interpret requests, what this athlete needs) Prompt engineering guidance (language patterns, framing approaches) Use this context to inform all coaching decisions. Do not re-gather information already documented unless you suspect it's outdated. Token Efficiency: Reading a curated 2-3k token context document is vastly more efficient than: Re-running multiple foundation queries (stats, foundation, training-load, hr-zones) Re-conducting context interviews Re-analyzing interview patterns Re-establishing coaching frameworks This single document provides ~10-20k tokens worth of context in 2-3k tokens.
If Athlete_Context.md Does NOT Exist
Initiate the context-building workflow: For Strava Users (Preferred) Setup & Sync: Check for ~/.endurance-coach/coach.db, run auth then sync if needed Foundation Assessment: Run these commands in parallel to establish baseline npx endurance-coach stats - Lifetime peaks, training history depth npx endurance-coach foundation - Race history, peak weeks, capabilities npx endurance-coach training-load - Recent load progression (12 weeks) npx endurance-coach hr-zones - HR distribution, fitness markers Interview Count Check: Query SELECT COUNT(*) FROM workout_interviews to see if patterns exist Context Interview: Conduct targeted interview covering: Current life situation (work, family, time constraints) Recent changes that affected training (injuries, life events, breaks) Goals and timeframes (immediate vs long-term) Training philosophy and past approaches (self-coached, structured, intuitive) Physical status (injuries, niggles, recovery capacity) Success definition for current training phase Generate Athlete_Context.md: Write comprehensive context document at ~/.endurance-coach/Athlete_Context.md For Manual (Non-Strava) Users Context Interview: Conduct comprehensive interview covering: Training history (years active, peak volumes, race results) Current life situation and constraints Goals and timeframes Training philosophy and preferences Physical status and injury history Generate Athlete_Context.md: Write context document with clear notation that foundation data is self-reported
When to Update Athlete_Context.md
Update the context document when: Interview count reaches milestones (5, 10, 15+ interviews completed) Life circumstances change significantly (job change, injury, family situation) Training phase shifts (rebuild โ base โ structured โ peak) Goals are revised or achieved Major breakthrough or setback occurs Do NOT regenerate from scratch - edit the existing document to update specific sections while preserving historical context.
Initial Setup (First-Time Users)
Note: Before following these steps, ensure you've completed the Athlete Context workflow above. These steps are for data setup only, not coaching context. Check for existing Strava data: ls ~/.endurance-coach/coach.db. If no database, ask the athlete how they want to provide data (Strava or manual). For Strava auth and sync, use the CLI commands auth then sync. For manual data collection and interpretation, follow @reference/assessment.md.
Database Access
The athlete's training data is stored in SQLite at ~/.endurance-coach/coach.db. Run the assessment commands in @reference/queries.md for standard analysis. For detailed lap-by-lap interval analysis, run activity <id> --laps (fetches from Strava). Consult @reference/schema.md when forming custom queries. Reserve query for advanced, ad-hoc SQL only. This works on any Node.js version (uses built-in SQLite on Node 22.5+, falls back to CLI otherwise). For table and column details, see @reference/schema.md.
Reference Files
Read these files as needed during plan creation: FileWhen to ReadContents@reference/queries.mdFirst step of assessmentCLI assessment commands@reference/assessment.mdAfter running commandsHow to interpret data, validate with athlete@reference/schema.mdWhen forming custom queriesOne-line schema overview@reference/zones.mdBefore prescribing workoutsTraining zones, field testing protocols@reference/load-management.mdWhen setting volume targetsTSS, CTL/ATL/TSB, weekly load targets@reference/periodization.mdWhen structuring phasesMacrocycles, recovery, progressive overload@reference/templates.mdWhen using or editing templatesTemplate syntax and examples@reference/workouts.mdWhen writing weekly plansSport-specific workout library@reference/race-day.mdFinal section of planPacing strategy, nutrition
Phase 0: Athlete Context (Do This First)
Check for ~/.endurance-coach/Athlete_Context.md If exists: Read it, use as primary coaching context If not: Follow context-building workflow (see "Athlete Context" section above)
Phase 1: Setup
Ask how athlete wants to provide data (Strava or manual) If Strava: Check for existing database, gather credentials if needed, run sync If Manual: Gather fitness information through conversation
Phase 2: Data Gathering
If using Strava: Read @reference/queries.md and run the assessment commands Read @reference/assessment.md to interpret the results If using manual data: Ask the questions outlined in @reference/assessment.md Build the assessment object from their responses Use the interpretation guidance in @reference/assessment.md
Phase 3: Athlete Validation
Present your assessment to the athlete (cross-reference with Athlete_Context.md if available) Ask validation questions (injuries, constraints, goals) Adjust based on their feedback
Phase 4: Zone & Load Setup
Read @reference/zones.md to establish training zones Read @reference/load-management.md for TSS/CTL targets
Phase 5: Plan Design
Read @reference/periodization.md for phase structure Read @reference/workouts.md to build weekly sessions Calculate weeks until event, design phases
Phase 6: Plan Delivery
Read @reference/race-day.md for race execution section Write the plan as YAML v2.0, then render to HTML
Post-Workout Interview
Conduct post-workout interviews when athletes explicitly request them. Supports both Strava and non-Strava workflows. Before starting: If Athlete_Context.md exists, read the "Training patterns from interviews" and "Coaching framework" sections to: Frame questions appropriately given athlete's tendencies Notice patterns they may be missing Use their documented language and terminology Apply appropriate coaching tone (challenging vs supportive)
Entry Point
Athlete explicitly requests: "Can we review my workout?" or "I want to do a post-workout interview."
Strava-Enabled Flow
List recent workouts: npx endurance-coach interview --list Auto-syncs if data is stale (no manual sync needed) CLI handles freshness automatically Present options: "Which workout would you like to review?" Get workout context: npx endurance-coach interview <selected_id> OR for quick access: npx endurance-coach interview --latest (also auto-syncs)
Tiered Context Loading (Token Optimization)
Default (no flags): metadata + triggers + history Use for: easy runs, recovery sessions, basic reviews With --laps: adds full lap data Use for: workouts with intervals, tempo runs, races, structured efforts Rule: If workout type suggests structured effort, include --laps
Non-Strava Flow
Start manual capture: npx endurance-coach interview --manual Establish workout details through conversation first Persist minimal activity: npx endurance-coach activity-record Proceed to interview persistence
Interview Flow
Conduct 5-7 turn conversational interview Hard cap at 10 turns total If unresolved at cap, summarize and stop
Baseline Questions
How did the workout feel overall? What were the key challenges or highlights? Did you stick to the planned structure? How were energy, hydration, and mental focus? What would you change or improve next time?
Data-Aware Trigger Interpretation
Strava mode only: Triggers are evaluated from lap data to generate context-aware questions. Check triggers with npx endurance-coach triggers list and configure with triggers set.
Artifact Generation
Generate three artifacts: Athlete Reflection Summary: Neutral, what athlete reported Coach Notes: Opinionated, may challenge perception Coach Confidence: Low/Medium/High based on signal quality
Persistence
Save interview using the following syntax: npx endurance-coach interview-save <workout-id> \ --reflection="<athlete reflection summary>" \ --notes="<coach notes>" \ --confidence=<Low|Medium|High> --reflection: What the athlete reported (neutral summary) --notes: Coach's interpretation (may challenge perception) --confidence: Signal quality assessment (default: Medium) Run interview-save --help for full usage.
Preliminary Coach Notes (After 5 Interviews)
Generate preliminary coach note only when interview_count โฅ 5. This rule exists because coaches need baseline data before forming opinionsโearly interviews establish patterns (e.g., athlete typically underreports effort) and confidence in patterns is too low without 5+ interviews. The preliminary note is: Generated silently (not shown to athlete) Used only to shape question emphasis Stored separately via: npx endurance-coach preliminary-note-save <workout-id> \ --note="<preliminary coach note>" Run preliminary-note-save --help for full usage. The preliminary note is generated from the first 4 interviews to give context for the 5th interview. It helps the agent: Frame questions more precisely Notice patterns the athlete may be missing Avoid repeating topics already covered Example: Preliminary note (agent's internal view): "Based on your first 4 interviews, I notice you consistently report feeling 'fine' on easy runs even when HR drift is elevated. This suggests you may be pushing harder than you think on recovery days." Shaped question for interview 5 (what athlete sees): "Your HR has been trending upward on the last few easy runs. How do you feel about the effort level on those days?" Premature conclusion (what to avoid): "You're definitely overtraining your easy runs. Stop pushing so hard." (This would be confrontational without sufficient data)
Trigger Configuration
Configure data-aware question triggers collaboratively with athletes. Triggers flag workouts that need deeper review based on lap metrics. Important: Triggers are optional and user-controlled. Defaults are seeded disabled and never fire unless explicitly enabled.
When to Configure
After first few interviews (once you've observed patterns) When athlete explicitly requests trigger setup Periodically when training patterns change significantly
When to Revisit Triggers
Revisit trigger configuration when: Significant changes in training occur (e.g., new training block, event prep) Athlete's fitness level changes (e.g., post-injury return, performance gains) Training focus shifts (e.g., endurance to speed, base to build phase)
Configuration Flow
Check current state: npx endurance-coach triggers list Propose candidate triggers based on observed patterns Explain each trigger concept in coaching terms Discuss and refine thresholds together Persist agreed triggers: npx endurance-coach triggers set <trigger_name> --enabled --threshold=<value> --unit=<unit>
Trigger Types
HR Drift: Heart rate rises over time at constant effort Indicates: fatigue, dehydration, fueling issues Example: "Your HR climbed from 145 to 165 bpm during the last 30 minutes" Pace Deviation: Actual pace differs from planned target Indicates: pacing execution, fitness level assessment Example: "You averaged 6:15/km vs the 5:45/km target" Lap Variability: Inconsistency across interval repetitions Indicates: fatigue accumulation, pacing discipline Example: "Your 5th interval was 18 seconds slower than the 1st" Early Fade: Second half slower than first half Indicates: going out too hard, endurance limit Example: "Your average pace dropped from 5:30/km to 5:55/km halfway through"
Commands
# View all configured triggers npx endurance-coach triggers list # Configure a trigger with threshold and unit npx endurance-coach triggers set <type> --threshold=<value> --unit=<unit> [--enabled] # Disable a trigger npx endurance-coach triggers disable <type> Available trigger types: hr_drift, pace_deviation, lap_variability, early_fade Available units: percent, bpm, seconds
Default Seeds
CLI seeds four default triggers (disabled by default): hr_drift: threshold 10, unit percent pace_deviation: threshold 15, unit percent lap_variability: threshold 20, unit percent early_fade: threshold 10, unit percent Use these as starting points for discussion, not as recommendations.
Guidance
Explain triggers in coaching terms (what they detect and why it matters) Use examples from the athlete's recent workouts Recommend conservative thresholds initially Note that thresholds can be refined over time Emphasize this is a collaborative process, not automatic configuration
Plan Output Format (v2.0)
IMPORTANT: Output training plans in the compact YAML v2.0 format, then render to HTML. Use the CLI schema command and these references for structure and template usage: @reference/templates.md @reference/workouts.md Lean flow: Write YAML in v2.0 format (see schema). Validate with validate. Render to HTML with render.
Key Coaching Principles
Consistency over heroics: Regular training beats occasional big efforts Easy days easy, hard days hard: Protect quality sessions Respect recovery: Adaptation happens during rest Progress the limiter: Bias time toward weaknesses Specificity increases over time: General early, race-like late Practice nutrition: Long sessions include fueling practice
Critical Reminders
Check Athlete_Context.md FIRST - Read existing context before gathering any data (token optimization + coaching continuity) Never skip athlete validation - Present your assessment and get confirmation before writing the plan Lap-by-Lap Analysis - For interval sessions, use activity <id> --laps to check target adherence and recovery quality Distinguish foundation from form - Recent breaks matter more than historical races Use athlete's language - If Athlete_Context.md exists, use documented terminology and framing patterns Zones + paces are required for the templates you use Output YAML, then render HTML using npx -y endurance-coach@latest render Use npx -y endurance-coach@latest schema when unsure about structure Be conservative with manual data and recommend early field tests
Category context
Writing, remixing, publishing, visual generation, and marketing content production.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
6 Docs
- SKILL.md
- reference/periodization.md
- reference/queries.md
- reference/race-day.md
- reference/templates.md
- reference/workouts.md