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 revo2wheels/intervalsicugptcoach-public --skill strengthgit clone --depth 1 https://github.com/revo2wheels/intervalsicugptcoach-publicWrote 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/revo2wheels/intervalsicugptcoach-public/strength)<a href="https://agentmods.dev/skills/revo2wheels/intervalsicugptcoach-public/strength"><img src="https://agentmods.dev/badge/skills/revo2wheels/intervalsicugptcoach-public/strength/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/revo2wheels/intervalsicugptcoach-public/strength"><img src="https://agentmods.dev/badge/skills/revo2wheels/intervalsicugptcoach-public/strength.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 | $0.00061 | $0.04878 |
| Opus 5.5 | $0.00024 | $0.01951 |
| Sonnet 5 | $0.00012 | $0.00976 |
| Haiku 4.5 | $0.00006 | $0.00488 |
Grade A, and why
montis-strength-training 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 2d 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 — 969 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Montis.icu Strength Training Skill
Purpose
This skill governs how Montis interprets, recommends, schedules, and describes strength training for endurance athletes.
It exists to make strength-training behaviour consistent across ChatGPT, MCP clients, and other Montis interfaces.
The skill defines:
- what Montis may and may not infer about strength training;
- how athlete-supplied exercises, sets, reps, loads, RPE, and RIR are handled;
- how strength work is integrated around cycling, running, swimming, recovery, and target events;
- how conservative strength progression may be proposed;
- when progression must be withheld;
- how strength prescriptions are handed to Workoutsv2 for calendar writing.
This skill is a knowledge and governance layer.
It is not a Strength Progression Engine and does not create a new physiological calculation.
1. Scope
Montis supports strength training as a complementary component of endurance training.
Current supported functions:
- recommend whether strength training is appropriate in the current endurance context;
- schedule strength sessions around the endurance plan;
- preserve athlete-supplied strength exercises and working loads;
- build a structured strength session from an athlete-supplied baseline;
- repeat an established strength session;
- propose conservative changes to load, reps, sets, or exercise selection when enough evidence is available;
- place strength prescriptions into a
WeightTrainingcalendar event through Workoutsv2.
Current unsupported functions:
- deterministic exercise-level strength adaptation modelling;
- automatic estimation of 1RM from incomplete data unless explicitly requested as an estimate;
- automatic progression based solely on elapsed calendar time;
- automatic progression based solely on CTL, ATL, TSB, ACWR, ESPE, ADE, or cycling performance;
- claims that Montis has measured strength adaptation when structured exercise history is unavailable;
- invented working weights;
- rehabilitation or injury-treatment prescription.
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.
- 2d ago First seen · 969 lines · 61 tokens per session scan A c7caed19890b
montis-strength-training is a skill published in the GitHub repository revo2wheels/intervalsicugptcoach-public (50 stars, last pushed today), licensed MIT. It adds 61 tokens to every session and 4,878 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-09-25.
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