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dsh-one-rep-max

Client Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-one-rep-max

Paste the following prompt into your AI chat to install this plugin:

Run dsh plugin install uckkk/dsh-one-rep-max in DeepSeek Harness to install this plugin; the full source code is available at https://github.com/uckkk/dsh-one-rep-max .

About this plugin

In strength training, the one-rep max (1RM) is the go-to metric for absolute strength, yet testing it head-on means lifting maximal loads, which carries real injury risk and demands long recovery. dsh-one-rep-max offers a lighter alternative: provide a known working weight and the number of reps completed, and a well-established regression formula returns a precise 1RM estimate without putting your body through another maximal effort.\n\nThe entire tool is implemented in pure Node.js and runs entirely on-device. No network requests are made, there are no external package downloads beyond the initial install, and the calculation is instantaneous. Once the plugin is registered as a client tool, it is immediately callable in-session with zero additional configuration or environment setup.\n\nIt is well suited for fitness enthusiasts, strength athletes, and coaches who want quick 1RM estimates embedded in their training logs or periodization plans, as well as for developers building automated workflows that need that number as a reference point. The absence of any network dependency keeps it fully functional in air-gapped, intranet, or privacy-sensitive environments where external calls are restricted or undesirable.

Use Cases

  • Quickly estimate 1RM after a workout session without a separate max test
  • Track weekly 1RM progression trends within a periodization block
  • Embed 1RM calculation logic into automated training log scripts

Best For

  • Fitness enthusiasts who want to avoid dedicated 1RM test days
  • Strength athletes and coaches needing dependency-free offline calculations
  • Developers embedding 1RM logic into automated training workflows