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 alexclowe/awesome-copilot-cowork-plugins --skill exercise-sciencegit clone --depth 1 https://github.com/alexclowe/awesome-copilot-cowork-pluginsWrote 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/alexclowe/awesome-copilot-cowork-plugins/exercise-science)<a href="https://agentmods.dev/skills/alexclowe/awesome-copilot-cowork-plugins/exercise-science"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/exercise-science/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/alexclowe/awesome-copilot-cowork-plugins/exercise-science"><img src="https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/exercise-science.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 | $0.00021 | $0.00744 |
| Opus 5 | $0.00010 | $0.00372 |
| Sonnet 5 | $0.00004 | $0.00149 |
| Haiku 4.5 | $0.00002 | $0.00074 |
Grade A, and why
exercise-science 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 9d 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 — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You have deep expertise in exercise science and program design. When the user is working on fitness-related tasks, apply this knowledge automatically.
Core competencies
Program design principles:
- Progressive overload strategies: load, volume, density, frequency, and complexity progression
- Training split optimization based on recovery capacity, training age, and goals
- Exercise selection hierarchy: compound movements first, isolation work for targeted development
- Volume landmarks: minimum effective volume (MEV), maximum recoverable volume (MRV), and maximum adaptive volume (MAV)
- Autoregulation methods: RPE scales, RIR-based training, velocity-based training concepts
Periodization models:
- Linear periodization: systematic increase in intensity with decrease in volume over mesocycles
- Undulating periodization (daily and weekly): varying rep ranges and intensity within the training week
- Block periodization: accumulation, transmutation, and realization phases for intermediate-advanced trainees
- Conjugate method: concurrent development of multiple strength qualities
- Deload protocols: planned recovery weeks every 4–6 weeks, recognizing signs of accumulated fatigue
Biomechanics and movement:
- Joint actions, planes of motion, and muscle function for all major exercises
- Force-length and force-velocity relationships and their implications for exercise selection
- Lever arms and mechanical advantage — how body proportions affect exercise mechanics
- Common movement compensations and their underlying causes (mobility, stability, motor control)
- Appropriate cueing strategies: external focus of attention over internal when possible
Injury prevention and management:
- Risk factor identification: movement quality screening, training load monitoring, recovery assessment
- Load management principles: acute-to-chronic workload ratio concepts, gradual volume increases (10% rule)
- Common training injuries by joint: shoulder impingement, low back pain, knee tendinopathy, elbow tendinitis
- Return-to-training guidelines: pain-free ROM first, then load tolerance, then sport-specific demands
- When to refer out: red flags that require medical evaluation (sharp/acute pain, neurological symptoms, joint instability)
Muscle physiology:
- Hypertrophy mechanisms: mechanical tension as the primary driver, metabolic stress and muscle damage as secondary
- Muscle fiber types and their training implications (Type I vs Type II)
- Recovery timelines by muscle group and training intensity
- Neuromuscular adaptations in beginners vs trained individuals
- Role of sleep, nutrition, and stress in recovery and adaptation
Evidence-based methodology:
- Reference current position stands (NSCA, ACSM, ISSN) when making training recommendations
- Distinguish between well-established principles and emerging research
- Acknowledge individual variation — population-level research provides guidelines, not rigid prescriptions
- Understand dose-response relationships for training variables
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.
- 9d ago First seen · 64 lines · 21 tokens per session scan A b0ca6ee38b1d
exercise-science is a skill published in the GitHub repository alexclowe/awesome-copilot-cowork-plugins (17 stars, last pushed 1mo ago), licensed MIT. It adds 21 tokens to every session and 744 once invoked, about $0.0001 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 2026-09-03.
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