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Agent Teams Command

Community
Mark393295827
agent-teams-command

Use when work has genuinely independent streams or distinct builder, evaluator, domain, and integration roles that require bounded multi-agent command scaled from 5 to 100+ agents.

Overview

PublisherMark393295827
Repositorythird-brain-v7-skills
Skill nameagent-teams-command
Stars
141
Forks
22
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by Mark393295827 on GitHub. Read the source before you install it.

Installation

Install the Agent Teams Command 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/Mark393295827/third-brain-v7-skills/tree/main/skills/agent-teams-command
  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/Mark393295827/third-brain-v7-skills.git /tmp/third-brain-v7-skills
mkdir -p .claude/skills
cp -r /tmp/third-brain-v7-skills/skills/agent-teams-command .claude/skills/agent-teams-command
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Teams Command 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 Agent Teams Command 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 Agent Teams Command 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.

Agent Teams Command — 安德智能体集群指挥系统 (V8.1)

<skill_contract> A mission with independently ownable workstreams, time SLA requirements, pre-flight task list, token budget, interfaces, permissions, and an integration owner. An isolated worker fleet (5 to 100+ agents), typed IPC ledger, pre-allocated token ledger, serial integration, cleanup, and evidence receipts. Pre-flight task list is 100% reconciled, integrated artifacts pass mission checks, token consumption is within TCLR boundaries, and cleanup gates pass. <non_goals>Parallelism without a task list, unbounded token burn, overlapping writers, or worker self-certification.</non_goals>

Strategic intent and final integration remain serial; owned execution runs in dynamically scaled parallel squadrons (5, 10, 50, 100+ agents). Use the Ender lens for commander intent and squad autonomy, Palantir for operational objects/actions, and von Neumann for executable process architecture. Detailed patterns: references/ender-palantir-command-patterns.md; examples: references/classic-campaigns.md.

Usage Template

Provide: mission, time SLA, task list candidate, workstreams, scaling tier (5/10/50/100+), dependencies, acceptance criteria, token budget (ETC limit), permissions, runtime capabilities, and integration owner.

Workflow

  1. Mission Intake & Scale Triage: Analyze mission complexity and select the smallest supported tier: Tier 1 (Squad: 5 agents), Tier 2 (Squadron: 10 agents), Tier 3 (Battle Group: 50 agents), or Tier 4 (Fleet: 100+ agents). If the runtime exposes fewer slots, cap the active fleet to that observed limit.
  2. Pre-flight Task List & Token Gate: Generate structured task list with explicit owner, territory, expected output, verification command, and token budget (ETC).
  3. Territory Mapping: Partition work into non-overlapping directories or git worktrees. Every worker gets strictly exclusive write boundaries.

<unknowns_gate>

If dependencies, write ownership, or verification commands are ambiguous, return NEEDS_INPUT or INSUFFICIENT_EVIDENCE. Do not launch parallel workers before pre-flight task list and token ledger are fully reconciled.

</unknowns_gate>

  1. Launch Workers: Dispatch workers in parallel across isolated worktrees. Each receives a typed context_manifest containing only its objective, dependencies, territory, inputs, output schema, budget, and verifier.
  2. Typed IPC & Command Board: Workers report state transitions (pending -> active -> review -> accepted -> closed) to shared command board.
  3. Independent Verification: Evaluator and reviewer agents verify worker outputs against objective criteria. No worker self-certification.
  4. Serial Integration & Join Gate: Integrator aggregates verified artifacts and receipts in dependency order. Do not merge private branch transcripts into the integration context. Run regression tests.
  5. Durable Write-back & Cleanup: Persist execution receipts, update system logs, and clean up temporary worktree branches.

For high-risk operations or cross-repo modifications, require independent review and human approval. Prepare a rollback plan before applying writes.

Audit total token consumption against the mission-declared TCLR: verified tasks resolved per 10,000 effective tokens consumed. Higher is better; do not invent a universal threshold. Verify all workstreams reached accepted or closed state, no file ownership conflicts occurred, and the declared regression suite passed.

<retry_policy>

max_attempts: 2. Stop on repeated failure signature or NO_PROGRESS. If a worker stalls or fails, reassign territory or downgrade to serial execution rather than launching unbudgeted retries.

</retry_policy>

<state_contract>

Persist {mission_id, run_id, status, attempt, budget, token_ledger, task_list, worker_fleet, context_manifest, command_board, evidence, unknowns, next_action} in durable storage.

</state_contract>

Failure Protocol

  • NEEDS_INPUT: Ambiguous mission scope or overlapping territory; prompt for clarification before launch.
  • INSUFFICIENT_EVIDENCE: Missing verification command or unverified worker output; reject merge.
  • BLOCKED_PERMISSION: Worker lacks required tool or file write permission; escalate to commander.
  • BLOCKED_DEPENDENCY: Upstream task failed; suspend dependent workers until resolved.
  • VERIFY_FAILED: Artifact fails independent evaluation; reject and trigger bounded repair.
  • NO_PROGRESS: Worker produces duplicate errors after 2 attempts; stop worker and flag for review.
  • BUDGET_STOP: Token consumption exceeds allocated ETC budget; trigger automatic halt. max_attempts: 2.

Output Contract

Return status, result (worker fleet summary, task resolution ledger, and verified artifacts), evidence (source receipts, lint receipts, and integration logs), unknowns, and next_action including approval or rollback when relevant.

Edge Cases

  • File Collision: Two workers attempt to modify the same file -> Integrator serializes execution and assigns one canonical owner.
  • Worker Runaway: Worker exceeds token budget without progress -> Heartbeat monitor triggers instant BUDGET_STOP kill.
  • Context Drift: Worker hallucinates outside its assigned territory -> Reviewer rejects output and logs territory violation.

Success Metrics

  • 100% of pre-flight tasks resolved with verifiable evidence.
  • Zero file write collisions across concurrent workers.
  • Total token expenditure remains within allocated ETC budget.
  • Wall-clock time reduced significantly compared to single-agent baseline.

Quality Gates

  • Pre-flight task list and token ledger reconciled before execution.
  • Exclusive write territories assigned to all active workers.
  • Independent reviewer verified all deliverables (no self-certification).
  • Integrator executed serial merge with passing regression suite.
  • High-risk operations received independent review, human approval, and rollback readiness.
  • Worktrees cleaned up and durable receipts written to system log.

</skill_contract>

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 Agent Teams Command AI skill do?

Use when work has genuinely independent streams or distinct builder, evaluator, domain, and integration roles that require bounded multi-agent command scaled from 5 to 100+ agents.

Why use Agent Teams Command on TypingMind?

Because you install it once and use it with any model. Agent Teams Command 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 Agent Teams Command in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Mark393295827/third-brain-v7-skills/tree/main/skills/agent-teams-command. 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 Agent Teams Command?

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 Agent Teams Command?

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

Is the Agent Teams Command AI skill free?

Yes. It is published on GitHub by Mark393295827 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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