Hyperexecute Skill logo

Hyperexecute Skill

Organization
LambdaTest
hyperexecute-skill

Operates HyperExecute end-to-end for TestMu AI/LambdaTest cloud test execution: analyze projects, create YAML, validate locally, run CLI jobs, debug failures, and wire CI. Use when the user mentions HyperExecute, hyperexecute.yaml, HyperExecute CLI, autosplit, matrix execution, LambdaTest grid, cloud test runs, CI test orchestration, or migrating Playwright/Cypress/Selenium/Pytest/WebdriverIO tests to HyperExecute.

Overview

PublisherLambdaTest
Repositoryagent-skills
Skill namehyperexecute-skill
Stars
377
Forks
74
Bundled files
10
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 10 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by LambdaTest on GitHub. Read the source before you install it.

Installation

Install the Hyperexecute Skill AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/LambdaTest/agent-skills/tree/main/hyperexecute-skill
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/LambdaTest/agent-skills.git /tmp/agent-skills
mkdir -p .claude/skills
cp -r /tmp/agent-skills/hyperexecute-skill .claude/skills/hyperexecute-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hyperexecute Skill in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Hyperexecute Skill on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Hyperexecute Skill is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

HyperExecute Operator

Quick Start

  1. Locate the HyperExecute CLI. If missing, ask before downloading it unless the user explicitly approved an autonomous HyperExecute session.
  2. Run hyperexecute analyze when the CLI is available; use local inspection only as fallback.
  3. Create or repair hyperexecute.yaml from the analyze output, project test commands, and templates in reference/.
  4. Run node scripts/doctor.js --config hyperexecute.yaml and node scripts/validate-config.js hyperexecute.yaml.
  5. Validate with the official CLI: ./hyperexecute --user "$LT_USERNAME" --key "$LT_ACCESS_KEY" --config hyperexecute.yaml --validate.
  6. Ask before a real cloud job unless the user has explicitly opted into an autonomous HyperExecute session.
  7. For failures, download logs/artifacts/reports and use reference/troubleshooting.md.

Operating Rules

  • Treat the official HyperExecute CLI as the source of truth for analyze, validation, execution, logs, reports, and artifacts.
  • Use LT_USERNAME and LT_ACCESS_KEY from local environment variables or CI secrets; never hardcode credentials in YAML or docs.
  • Use --job-secret-file only for extra job-scoped secrets, preferably outside the repo or ignored by .gitignore/.hyperexecuteignore.
  • Prefer template-driven YAML over generator scripts because test commands, paths, and payload boundaries are project-specific.
  • Run safe local checks automatically; run real HyperExecute cloud jobs only after confirmation unless the user opted into autonomous mode.
  • In autonomous mode, validate first, run, inspect output, download logs/artifacts when useful, and retry only for actionable config/environment fixes.

Workflow

  • First run: analyze project, author YAML, run helper checks, run CLI validate, then request confirmation for the cloud job.
  • Debug: reproduce the failing CLI command, add --verbose when useful, download logs/artifacts/reports, fix one cause at a time.
  • CI: use CI secrets, add a validation stage before execution, set CI=true for quieter logs, and keep downloaded artifacts available for failed jobs.
  • Performance: tune autosplit, concurrency, cache keys, retries, smart ordering, and matrix/hybrid scope after one successful run.

Helper Scripts

  • scripts/doctor.js: checks CLI readiness, credentials, config presence, and optional official validation.
  • scripts/validate-config.js: lightweight config linting for common mistakes before official CLI validation.
  • scripts/build-command.js: prints safe validate/run/debug/download commands using environment variable references.
  • scripts/summarize-artifacts.js: summarizes downloaded logs, reports, and artifacts for triage.

References

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Hyperexecute Skill AI skill do?

Operates HyperExecute end-to-end for TestMu AI/LambdaTest cloud test execution: analyze projects, create YAML, validate locally, run CLI jobs, debug failures, and wire CI. Use when the user mentions HyperExecute, hyperexecute.yaml, HyperExecute CLI, autosplit, matrix execution, LambdaTest grid, cloud test runs, CI test orchestration, or migrating Playwright/Cypress/Selenium/Pytest/WebdriverIO tests to HyperExecute.

Why use Hyperexecute Skill on TypingMind?

Because you install it once and use it with any model. Hyperexecute Skill is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Hyperexecute Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LambdaTest/agent-skills/tree/main/hyperexecute-skill. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Hyperexecute Skill?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Hyperexecute Skill?

As many as you like. As long as a model supports skills, you can use Hyperexecute Skill with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Hyperexecute Skill AI skill free?

Yes. It is published on GitHub by LambdaTest under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇