Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add ColinEberhardt/claude-running-coach --skill training-plangit clone --depth 1 https://github.com/ColinEberhardt/claude-running-coachWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/colineberhardt/claude-running-coach/training-plan)<a href="https://agentmods.dev/skills/colineberhardt/claude-running-coach/training-plan"><img src="https://agentmods.dev/badge/skills/colineberhardt/claude-running-coach/training-plan/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/colineberhardt/claude-running-coach/training-plan"><img src="https://agentmods.dev/badge/skills/colineberhardt/claude-running-coach/training-plan.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00078 | $0.03410 |
| Opus 5 | $0.00039 | $0.01705 |
| Sonnet 5 | $0.00016 | $0.00682 |
| Haiku 4.5 | $0.00008 | $0.00341 |
Grade A, and why
training-plan scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 304 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Training Plan Creator
Overview
This skill creates personalized, evidence-based training plans for 5K, 10K, Half Marathon, and Marathon distances. Plans are delivered as structured markdown documents with clear progression, appropriate training intensities, and race-specific preparation.
Workflow
1. Gather Essential Information
Before creating a plan, collect these key details from the user:
Required:
- Race distance: 5K, 10K, Half Marathon, or Marathon
- Race date: When the race occurs
- Current weekly mileage: Average miles/km per week over the last 4 weeks
- Available training days: Number of days per week the athlete can train (typically 3-6)
Recommended:
- Target race time: Goal finish time or pace
- Recent race results: Previous race times to establish current fitness level
- Training background: Months/years of consistent running
- Injury history: Any recurring issues or limitations
- Other constraints: Schedule restrictions, terrain access, weather, life stress
Handling missing information:
- If target time is not provided, ask about recent race performance or typical training paces
- If training days are unclear, suggest 4-5 days as a reasonable starting point
- If race date creates a very short timeline (< 8 weeks for marathon), flag this concern
2. Calculate Training Paces
IMPORTANT: Use the calculate_paces.py script to generate training paces based on the target race time and distance. This ensures consistent and accurate pace calculations.
How to use the pace calculator:
The script is located at scripts/calculate_paces.py (relative to this skill's directory).
python3 scripts/calculate_paces.py --distance <km> --time <HH:MM:SS or MM:SS>
Examples:
# 5K in 20:00 (paces per mile by default)
python3 scripts/calculate_paces.py -d 5 -t 20:00
# 10K in 45:30
python3 scripts/calculate_paces.py -d 10 -t 45:30
# Half Marathon in 1:35:00
python3 scripts/calculate_paces.py -d 21.0975 -t 1:35:00
# Marathon in 3:30:00
python3 scripts/calculate_paces.py -d 42.195 -t 3:30:00
# For per-km paces instead of per-mile
python3 scripts/calculate_paces.py -d 10 -t 45:00 --per-km
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 304 lines · 78 tokens per session scan A 8ce7fb670040
training-plan is a skill published in the GitHub repository ColinEberhardt/claude-running-coach (23 stars, last pushed 3mo ago), licensed MIT. It adds 78 tokens to every session and 3,410 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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