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Lindy agent tuning through controlled experiments v1.0.0

Use this resource to optimise the speed, reliability and cost of a Lindy agent

New

  • 336 downloads
  • 242 likes
  • 239 saves

Free

Install with the CLI

In a terminal, from the folder you want:

npx --yes palatekit@latest install lindy-agent-performance-experiments

With no option, the skill goes to ~/.agents/skills/; add --agent claude-code (or codex, cursor…) to target an agent.

First time: connect the CLI with

npx --yes palatekit@latest login

The agent defines the experiment contract, saves a rollback point, changes one variable and compares results on the same evaluation set. It keeps the change only if the criteria set in advance are met.

Mostly picked by

Updated November 2, 2026

Card for the “Lindy agent tuning through controlled experiments” skill

Details

Works in
Install guides
Generates with
Your agent's model
Licence
MIT

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What you get

  • SKILL.md : six-step protocol, from rollback point to rollout
  • references/implementation.md : optimisation patterns for deterministic actions

Good fit for

  • Your Lindy agent is slow or uses too many credits
  • An agent gives inconsistent results from one run to another

Not for

    Tags

    • lindy
    • performance
    • optimisation
    • evaluation
    • cost

    About

    You say

    “My Lindy qualification agent is too slow: propose a controlled experiment to reduce latency.”

    Card for the “Lindy agent tuning through controlled experiments” skill

    Skill published by Jeremy Longshore <jeremy@intentsolutions.io> under the MIT license, delivered in its original version.

    Show more

    How to use it

    1. Install the skill in your agent's skills folder
    2. Ask your agent to optimise the speed, reliability and cost of a Lindy agent
    3. Review and adjust the result

    Content preview

    Files

    • LICENSE1.1 KB
    • NOTICE-PALATEKIT.md0.5 KB
    • references/implementation-guide.md4.2 KB
    • references/implementation.md3.2 KB
    • SKILL.md9.4 KB

    Origin and licence

    Listed unmodified

    Author
    Jeremy Longshore <jeremy@intentsolutions.io>
    Source
    github.com/jeremylongshore/tons-of-skills-marketplace/tree/ab85a061f7bcc039dac13ee193de7a4cd4388571/skills/.curated/lindy-performance-tuning
    Licence
    MIT

    Notice

    « lindy-performance-tuning » (Jeremy Longshore <jeremy@intentsolutions.io>, MIT) livré dans sa version d'origine : fiche en français et en anglais par PalateKit.

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