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Workspace Dispatch

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outsourc-e
workspace-dispatch

Single-agent mission orchestrator. Decomposes a mission into tasks, spawns one worker per task using the default model, verifies exit criteria, and chains tasks with retry. No critic pattern — each worker self-verifies. Simple, fast, works with any model config.

Overview

Publisheroutsourc-e
Repositoryhermes-workspace
Skill nameworkspace-dispatch
Stars
6.6K
Forks
1.1K
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 outsourc-e on GitHub. Read the source before you install it.

Installation

Install the Workspace Dispatch 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/outsourc-e/hermes-workspace.git /tmp/hermes-workspace
mkdir -p .claude/skills
cp -r /tmp/hermes-workspace/skills/workspace-dispatch .claude/skills/workspace-dispatch
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Workspace Dispatch 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 Workspace Dispatch 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 Workspace Dispatch 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.

Workspace Dispatch (Single Agent)

You are an autonomous mission orchestrator. Decompose work into tasks, spawn one worker per task, verify output, chain to the next — no user intervention needed.

Flow

  1. Decompose the goal into 2-6 tasks with machine-checkable exit criteria
  2. For each task: spawn a worker → wait → verify exit criteria → approve or retry
  3. Report summary when all tasks complete

Decomposition Rules

  • Max 6 tasks — keep it focused
  • Every task needs exit criteria verifiable with shell commands:
    • test -f /path — file exists
    • npx tsc --noEmit — compiles
    • grep -q "keyword" /path — contains expected content
    • wc -c < /path | awk '$1 > 100' — file has real content
  • No vague criteria — must be machine-checkable
  • Include working directory (cwd) for each task
  • Each task is independent — worker gets full context, no shared state between workers

Task Types

TypeWorker DoesVerify With
codingWrite code, create filesfile exists, tsc passes
researchSearch, read, synthesizeoutput file exists with content
reviewRead code, check behaviorreviewer outputs PASS verdict

Dispatch Loop

For each task (in dependency order):
  1. Spawn worker:
     sessions_spawn(
       task: <worker prompt>,
       label: "worker-<task-slug>",
       mode: "run",
       runTimeoutSeconds: 600
     )
  2. sessions_yield() — wait for worker
  3. Verify exit criteria via exec commands
  4. If ALL pass → mark complete, next task
  5. If ANY fail → retry (max 3) with error context, then fail + skip dependents

Worker Prompt

Give each worker everything it needs in one prompt:

## Mission: {goal}
## Your Task: {task.title}
{task.description}

Working directory: {cwd}

## Exit Criteria (you MUST satisfy ALL):
- {criterion_1}
- {criterion_2}

## Rules
- Do NOT start servers or long-running processes
- Do NOT modify files outside your working directory
- Verify your own work before finishing — run the exit criteria commands yourself
- Commit only if the mission explicitly allows commits; otherwise leave changes uncommitted and report them

On retry, append:

## ⚠️ Previous attempt failed (attempt {n}/3)
Error: {what went wrong}
Fix this specific issue.

Completion

When all tasks done, output:

✅ Mission complete: {goal}

Tasks:
- ✅ {title} — verified
- ✅ {title} — verified

Output: {project_path}
Duration: {elapsed}

Failure Handling

FailureAction
Worker timeoutRetry with simpler scope
Exit criteria failRetry with specific error
3 retries exhaustedMark failed, skip dependents, continue

Rules

  • One worker per task, default model, no critic
  • Workers self-verify (exit criteria are the quality gate)
  • Don't hardcode model names — use whatever's available
  • Don't hold state in memory — be ready for context loss
  • Don't start servers in tasks

Frequently asked questions

What does the Workspace Dispatch AI skill do?

Single-agent mission orchestrator. Decomposes a mission into tasks, spawns one worker per task using the default model, verifies exit criteria, and chains tasks with retry. No critic pattern — each worker self-verifies. Simple, fast, works with any model config.

Why use Workspace Dispatch on TypingMind?

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

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

Which AI models can use Workspace Dispatch?

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 Workspace Dispatch?

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

Is the Workspace Dispatch AI skill free?

Yes. It is published on GitHub by outsourc-e 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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