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Native Agent Swarms

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aAAaqwq
native-agent-swarms

Coordinate small teams of specialized Codex agents with bounded parallelism, explicit ownership, native messaging, and evidence-based synthesis. Use when two or more independent investigations, reviews, or implementation streams can run concurrently without sharing writable files, or when the user asks for swarm, team, parallel agent, or multi-reviewer execution.

Overview

PublisheraAAaqwq
RepositoryAGI-Super-Team
Skill namenative-agent-swarms
Stars
98
Forks
23
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

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

Installation

Install the Native Agent Swarms 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.
  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/aAAaqwq/AGI-Super-Team.git /tmp/AGI-Super-Team
mkdir -p .claude/skills
cp -r /tmp/AGI-Super-Team/plugins/agi-super-team-codex/skills/native-agent-swarms .claude/skills/native-agent-swarms
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Native Agent Swarms 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 Native Agent Swarms 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 Native Agent Swarms 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.

Native Agent Swarms

Use Codex-native collaboration instead of Claude TeamCreate, TaskCreate, SendMessage, tmux, or shared team-state conventions. Keep orchestration in the parent agent and use specialists as leaf workers.

Gate delegation

Delegate only when the user or applicable project instructions authorize subagents. Delegation never expands the user's requested scope or permission to change external state.

Before spawning, confirm:

  • At least two work streams are genuinely independent.
  • Each worker has a bounded objective and sufficient starting evidence.
  • Writable files have exactly one owner.
  • The expected speed or quality gain exceeds coordination overhead.
  • Available thread capacity can accommodate the team; prefer 2-3 workers and never exceed the configured limit.

Stay sequential when tasks share state, one result determines the next, or workers would edit the same files.

Compose the team

Select the narrowest installed custom agents. Typical choices include planner, architect, code-reviewer, security-reviewer, quality-engineer, hypothesis-debugger, performance-reviewer, database-reviewer, accessibility-reviewer, and language reviewers.

Use team-coordinator only to advise on decomposition. The parent agent remains responsible for spawning, monitoring, resolving conflicts, and final verification. Keep nesting depth at one unless the user explicitly asks for recursive delegation and the runtime configuration safely allows it.

Write dispatch contracts

Every worker task must include:

markdown
Objective: one concrete result.
Scope: exact subsystem, files, or hypothesis.
Ownership: writable files; all other files are read-only.
Starting evidence: errors, requirements, paths, and relevant constraints.
Acceptance criteria: observable completion conditions.
Safety: no commit, push, deploy, external messages, secret access, or destructive commands.
Output: findings or changes, evidence, commands run, gaps, and a concise handoff.

Use spawn_agent for independent work. Use direct send_message only for relevant evidence or interface updates, followup_task for a new bounded assignment after a worker becomes idle, wait_agent for progress, interrupt_agent only when work is obsolete or unsafe, and list_agents to audit active capacity.

Coordinate execution

  1. Record the task-to-agent and file-ownership matrix.
  2. Spawn independent tasks close together so they run concurrently.
  3. Continue useful parent-only work while agents run.
  4. Relay only evidence that materially changes another worker's task.
  5. Stop fan-out when results converge, capacity is saturated, or coordination cost rises.
  6. Never let two implementation agents modify the same file or mutable external resource.

For specialized patterns, read only the relevant reference:

Synthesize and verify

The parent agent must inspect worker evidence rather than concatenate summaries.

  • Deduplicate overlapping findings and retain the strongest evidence.
  • Resolve disagreements by checking source files, tests, or command output.
  • Review all shared-worktree changes for overlap and unintended edits.
  • Run integration-level verification after individual checks pass.
  • Report which agents ran, their scopes, final evidence, and unresolved gaps.

Do not claim success merely because all workers returned. Completion requires integrated verification proportional to risk.

Source patterns adapted from wshobson/agents commit 767d969a73ce6608d10ac713e52be9ac7f061ab9 (MIT), rewritten for native Codex collaboration and permission boundaries.

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 Native Agent Swarms AI skill do?

Coordinate small teams of specialized Codex agents with bounded parallelism, explicit ownership, native messaging, and evidence-based synthesis. Use when two or more independent investigations, reviews, or implementation streams can run concurrently without sharing writable files, or when the user asks for swarm, team, parallel agent, or multi-reviewer execution.

Why use Native Agent Swarms on TypingMind?

Because you install it once and use it with any model. Native Agent Swarms 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 Native Agent Swarms in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aAAaqwq/AGI-Super-Team/tree/main/plugins/agi-super-team-codex/skills/native-agent-swarms. 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 Native Agent Swarms?

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 Native Agent Swarms?

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

Is the Native Agent Swarms AI skill free?

Yes. It is published on GitHub by aAAaqwq 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.

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