exercise-science

exercise-science is a skill for Claude Code, Codex from alexclowe/awesome-copilot-cowork-plugins. It costs 21 tokens per session (744 once invoked), scanned A, original, MIT.

Exercise-science guidance for designing training programmes, including progression, recovery, biomechanics, and periodisation. Periodisation means organising training into phases over time.

In plain words
What is it for?
Use it to choose exercises, plan training splits and phases, set progression methods, and organise deloads and recovery.
Why use it?
It helps turn training goals into structured programmes and adjust workload when progress or recovery changes.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to choose exercises, plan training splits and phases, set progression methods, and organise deloads and recovery.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/alexclowe/awesome-copilot-cowork-plugins/exercise-science
Install

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.

Any agent
npx skills add alexclowe/awesome-copilot-cowork-plugins --skill exercise-science
Clone the repo
git clone --depth 1 https://github.com/alexclowe/awesome-copilot-cowork-plugins

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for exercise-science

README.md
[![agentmods](https://agentmods.dev/badge/skills/alexclowe/awesome-copilot-cowork-plugins/exercise-science/github.svg)](https://agentmods.dev/skills/alexclowe/awesome-copilot-cowork-plugins/exercise-science)
Your own site
<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.

agentmods 80×15 button for exercise-science

Your own site · 80×15
<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>
Per session 21 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 744 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 9d ago against content hash b0ca6ee38b1d, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

personal-trainer/skills/exercise-science/SKILL.md · 64 lines

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

Read the full file on GitHub · 64 lines

Changes

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.

  1. 9d ago First seen · 64 lines · 21 tokens per session scan A b0ca6ee38b1d

Subscribe to this mod's changes

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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