section-11
Evidence-based endurance cycling coaching protocol. Use when analyzing training data, reviewing sessions, planning workouts, answering training questions, or giving cycling coaching advice. Always fetch athlete JSON data before responding to any training question.
Best use case
section-11 is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Evidence-based endurance cycling coaching protocol. Use when analyzing training data, reviewing sessions, planning workouts, answering training questions, or giving cycling coaching advice. Always fetch athlete JSON data before responding to any training question.
Teams using section-11 should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/section11/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How section-11 Compares
| Feature / Agent | section-11 | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Evidence-based endurance cycling coaching protocol. Use when analyzing training data, reviewing sessions, planning workouts, answering training questions, or giving cycling coaching advice. Always fetch athlete JSON data before responding to any training question.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Section 11 — AI Coaching Protocol
## First Use Setup
On first use:
1. **Check for DOSSIER.md** in the workspace
- If not found, fetch template from: https://raw.githubusercontent.com/CrankAddict/section-11/main/DOSSIER_TEMPLATE.md
- Ask the athlete to fill in their data (zones, goals, schedule, etc.)
- Save as DOSSIER.md in the workspace
2. **Set up JSON data source**
- Athlete creates a private GitHub repo for training data
- Set up automated sync from Intervals.icu to `latest.json`
- Save the raw URL in DOSSIER.md under "Data Source"
- See: https://github.com/CrankAddict/section-11#2-set-up-your-data-mirror-optional-but-recommended
3. **Configure heartbeat settings**
- Fetch template from: https://raw.githubusercontent.com/CrankAddict/section-11/refs/heads/main/openclaw/HEARTBEAT_TEMPLATE.md
- Ask athlete for their specific values:
- Location for weather checks (city/area)
- Timezone
- Valid outdoor riding hours
- Weather thresholds (min temp, max wind, max rain %)
- Preferred notification hours
- Save as HEARTBEAT.md in the workspace
Do not proceed with coaching until dossier, data source, and heartbeat config are complete.
## Protocol
Fetch and follow: https://raw.githubusercontent.com/CrankAddict/section-11/main/SECTION_11.md
## Data Hierarchy
1. JSON data (always fetch first from athlete's data URL)
2. Protocol rules (SECTION_11.md)
3. Athlete dossier (DOSSIER.md)
4. Heartbeat config (HEARTBEAT.md)
## Required Actions
- Fetch latest.json before any training question
- No virtual math — use only fetched values
- Follow Section 11 B validation checklist
- Cite frameworks per protocol
## Heartbeat Operation
On each heartbeat, follow the checks and scheduling rules defined in your HEARTBEAT.md:
- Daily: training/wellness observations, weather (only if conditions are good)
- Weekly: background analysis
- Self-schedule next heartbeat with randomized timing within notification hoursRelated Skills
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