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Dispatch Agents

Community
danielvm-git
dispatch-agents

Dispatch multiple subagents in parallel on independent tasks. No waiting between them — all run concurrently. Use when tasks are truly decoupled and speed matters. Distinct from delegate-task (concurrent here, no inter-task review gate).

Overview

Publisherdanielvm-git
Repositorybigpowers
Skill namedispatch-agents
Stars
206
Forks
18
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by danielvm-git on GitHub. Read the source before you install it.

Installation

Install the Dispatch Agents 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/danielvm-git/bigpowers.git /tmp/bigpowers
mkdir -p .claude/skills
cp -r /tmp/bigpowers/skills/dispatch-agents .claude/skills/dispatch-agents
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dispatch Agents 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 Dispatch Agents 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 Dispatch Agents 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.

story: e09s04

story: e45s38

story: e45s30

Dispatch Agents

HARD GATEHARD GATE — Agent work must be parallelizable and have explicit synchronization points. Do NOT dispatch work that has hidden dependencies between agents.

Run multiple subagents in parallel on independent tasks. Use when tasks are genuinely decoupled — no agent needs the output of another to start.

Distinct from delegate-task: This skill maximizes throughput via concurrency. There is no sequential review gate between tasks. Use delegate-task instead when a single task needs careful two-stage oversight before proceeding.

When to use

  • Tasks that can run simultaneously without shared state
  • Large plans that can be broken into parallel workstreams
  • Exploration: gather information from multiple parts of the codebase at once

When NOT to use

  • Task B depends on Task A's output
  • You need to review Task A before Task B can start safely
  • The tasks share a file and concurrent edits would conflict

Process

1. Confirm independence

Before dispatching, verify each task pair is truly independent:

  • No shared files being written
  • No shared state (DB migrations, config files)
  • No ordering dependency between outcomes

If any two tasks conflict, sequence them with delegate-task or execute-plan instead.

Subagent depth tiers (e45s30)

Map effort: frontmatter and story risk: to prompt depth — do not send minimal_decisive agents a full_maturity brief.

TierWhenBrief shapeToken budget
full_maturityeffort: heavy, risk: P0, security-sensitive diffsFull task_brief + CONVENTIONS excerpts + threat model if presentFull envelope
standardeffort: standard, risk: P1P2Standard task_brief fields belowDefault
minimal_decisiveeffort: light, risk: P3, read-only explorationgoal + verify + in_scope only≤15 lines

Record depth: <tier> in the Agent tool description when dispatching.

2. Write typed task briefs (Orca message protocol)

Before writing briefs, read specs/state.yaml if it exists — each agent gets only the decisions relevant to its task, nothing else.

Every inter-agent message uses a typed envelope — no freeform prose between waves:

typeWhenRequired fields
task_briefDispatchtask_id, goal, in_scope, out_of_bounds, verify, prior_decisions
checkpointMid-wave progresstask_id, status (running|blocked), comment (one line)
resultAgent returntask_id, exit (pass|fail), summary, verify_output
circuit_open3 consecutive failurestask_id, failures (3), escalate_to (user)

Example task_brief (each agent starts cold — brief size directly controls token cost and hallucination risk):

yaml
type: task_brief
task_id: agent-1
goal: [one sentence — what success looks like]
in_scope: [explicit file or module list]
out_of_bounds: [what NOT to touch]
verify: [runnable command]
prior_decisions: [relevant entries from specs/state.yaml — omit if none]

Emit checkpoint comments when an agent is slow or blocked — one line, no stack traces. Parent reads checkpoints before spawning follow-ups.

Do not include the full conversation, full file contents, or decisions unrelated to this agent's task.

3. Circuit breaker + iterative retrieval (max 3 cycles)

Track consecutive failures per task_id. On the 3rd consecutive result.exit: fail for the same task, emit type: circuit_open and stop dispatching that task — escalate to user with the three failure summaries. Reset counter on any pass.

After each wave completes:

  1. Dispatch — run parallel agents with typed task_brief envelopes.
  2. Evaluate — read result messages; list gaps vs goal; honor open circuits.
  3. Refine — tighten briefs or spawn follow-up agents (max 3 cycles total).

Stop when gaps empty, circuit opens, or cycle 3 reached — escalate to user. If agents go silent without returning, invoke diagnose-stall before spawning another wave.

4. Dispatch in parallel

Spawn all agents in a single message using multiple Agent tool calls. Each agent gets its own complete brief.

Agent 1: brief for task A
Agent 2: brief for task B
Agent 3: brief for task C

5. Collect and review results

When all agents return: review each result, run verify commands, check diffs for scope violations.

6. Integrate

Merge accepted results. Resolve conflicts manually; note in summary.

Report: which tasks succeeded, which need revision, overall verify status.

Verify

→ verify: test -f skills/dispatch-agents/SKILL.md && test -f scripts/lib/completeness-critic.sh

Frequently asked questions

What does the Dispatch Agents AI skill do?

Dispatch multiple subagents in parallel on independent tasks. No waiting between them — all run concurrently. Use when tasks are truly decoupled and speed matters. Distinct from delegate-task (concurrent here, no inter-task review gate).

Why use Dispatch Agents on TypingMind?

Because you install it once and use it with any model. Dispatch Agents 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 Dispatch Agents in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/danielvm-git/bigpowers/tree/main/skills/dispatch-agents. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dispatch Agents?

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 Dispatch Agents?

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

Is the Dispatch Agents AI skill free?

Yes. It is published on GitHub by danielvm-git 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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