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Parallel Agents

Checked
davila7·vlatest·agents·MIT

Davila7's parallel agents efficiently execute complex tasks by dividing them across multiple simultaneous AI workers for faster results.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add davila7/claude-code-templates --skill parallel-agents -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

davila7

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned davila7/claude-code-templates at 79182c51fb.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
79182c51fb
Manifest
cli-tool/components/skills/ai-research/parallel-agents/SKILL.md
Scanned
1 file, 160 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill enables AI agents to orchestrate multiple sub-agents that run concurrently to tackle complex, multi-step tasks. It breaks down a single high-level objective into parallel workflows, allowing different parts of a project to be executed simultaneously for faster results.

When to use it

  • You need to generate multiple independent code files or components at once rather than sequentially.
  • A task requires distinct research paths or data gathering steps that do not depend on each other's immediate output.
  • You want to reduce total execution time by avoiding linear bottlenecks in a large workflow.
  • The project involves diverse domains (e.g., frontend styling, backend logic, and documentation) that can be handled by specialized agents.

Key capabilities

  • Spawns multiple independent agent instances to work on different aspects of a prompt simultaneously.
  • Manages concurrent execution without requiring strict sequential dependencies between sub-tasks.
  • Aggregates outputs from parallel processes into a cohesive final result.
  • Optimizes throughput for tasks that naturally decompose into non-blocking steps.

Example prompts

  • "Create a full-stack todo app: have one agent build the React frontend, another set up the Node.js backend, and a third write the database schema."
  • "Analyze this dataset by having one agent summarize trends, a second visualize key metrics, and a third draft a report on anomalies."
  • "Refactor this codebase: assign one agent to update imports, another to fix linting errors, and a third to add unit tests."

Tips & gotchas

Ensure your tasks can logically be split into independent sub-problems; forcing parallelism on dependent steps may cause confusion or errors. Monitor output aggregation carefully, as the main orchestrator must correctly synthesize results from multiple concurrent sources.