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 frankxai/claude-skills-library --skill gym-training-expertgit clone --depth 1 https://github.com/frankxai/claude-skills-libraryWrote 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/frankxai/claude-skills-library/gym-training-expert)<a href="https://agentmods.dev/skills/frankxai/claude-skills-library/gym-training-expert"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/gym-training-expert/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/frankxai/claude-skills-library/gym-training-expert"><img src="https://agentmods.dev/badge/skills/frankxai/claude-skills-library/gym-training-expert.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 | $0.00058 | $0.04064 |
| Opus 5 | $0.00029 | $0.02032 |
| Sonnet 5 | $0.00012 | $0.00813 |
| Haiku 4.5 | $0.00006 | $0.00406 |
Grade A, and why
gym-training-expert scanned grade A with 1 finding 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 12d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Leg curl (hamstrings) The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
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.
- 12d ago First seen · 642 lines · 58 tokens per session scan A 71d86b18f507
gym-training-expert is a skill published in the GitHub repository frankxai/claude-skills-library (40 stars, last pushed 4d ago), with no licence file. It adds 58 tokens to every session and 4,064 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
token-saver
Token optimization guide for Claude Code. This skill should be used when the user wants to reduce API costs, learn model routing strategies, or understand how to use free/cheaper tools (Ollama, Haiku) instead of burning expensive Claude tokens on routine tasks. Triggers on: save tokens, reduce costs, token tips, cheap…
scaffold-exercises
A template builder for coding exercises, organized into sections and exercises with problem, solution, and explanation folders. It creates the files and structure expected by the project’s lint checker.
habit-formation
Atomic habits, cue-routine-reward loops, habit stacking, accountability systems, and behavior change.
quick-win-session
Generates guided first-action flows that help users achieve a meaningful result within 60 seconds to boost retention. Use when user wants quick win onboarding, time-to-value optimization, or first success moments.
tipkit-generator
Generate TipKit infrastructure with inline/popover tips, rules, display frequency, and testing utilities. Use when adding contextual tips or feature discovery to an iOS/macOS app.
go
Execute the ADR-driven 4-phase development workflow (preflight, implementation, formatting, release). Use whenever the user says 'itp go'.