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dsh-serum-concentration

Memory Updated 2026.08.20

Run the following command in DeepSeek Harness:

dsh plugin install uckkk/dsh-serum-concentration

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

Run dsh plugin install uckkk/dsh-serum-concentration in DeepSeek Harness to install this serum concentration reference plugin, available at https://github.com/uckkk/dsh-serum-concentration .

About this plugin

Choosing the right concentration for a serum product often lacks a single authoritative standard, leaving users to cross-reference multiple sources and still feel uncertain. dsh-serum-concentration addresses this by registering a tool called serum_concentration: call it in your session with the relevant parameters and receive a clear, actionable concentration recommendation in one step, eliminating the back-and-forth of manual research. The implementation is written in pure Node.js and makes zero network calls, so everything runs locally and your parameters never leave the machine.

It is well suited for users who frequently need skincare concentration guidance within an AI-driven session and prefer a lightweight, dependency-free workflow. The tool does not replace a dermatologist's diagnosis; instead, it quickly surfaces commonly referenced parameter-to-recommendation mappings so you can shorten the information-gathering step and reach a decision faster.

Categorized under memory, the plugin keeps the concentration logic available as a callable tool in your session context. One call yields a structured answer, and there is no external API key or documentation to maintain, making it a plug-and-play reference aid.

Use Cases

  • Query a recommended serum concentration by parameters in your session
  • Compare concentration suggestions across different ingredients or skin types
  • Obtain concentration references offline with zero network requests

Best For

  • Session users who frequently need skincare concentration references
  • Developers who prefer lightweight, dependency-free workflows
  • Users integrating skincare knowledge into AI-driven workflows