Delegating Work logo

Delegating Work

CommunityPopular
mvschwarz
delegating-work

Use when you have a task and must decide WHO does it — yourself, a spawned subagent, or another agent in the topology that already holds the context. The rule: how much would a blank slate have to read in to do this correctly? A lot → route to a context-holding agent; a little → subagent; unsure → a real agent or yourself. NOT the mechanics of fanning out (that's dispatching-parallel-agents / subagent-driven-development) — this is the who/whether decision, and the failure mode that makes teams abandon subagents.

Overview

Publishermvschwarz
Repositoryopenrig
Skill namedelegating-work
Stars
5K
Forks
366
Bundled files
Instructions only
LicenseApache-2.0
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 mvschwarz on GitHub. Read the source before you install it.

Installation

Install the Delegating Work 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/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/delegating-work
  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/mvschwarz/openrig.git /tmp/openrig
mkdir -p .claude/skills
cp -r /tmp/openrig/packages/daemon/assets/plugins/openrig-core/skills/delegating-work .claude/skills/delegating-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Delegating Work 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 Delegating Work 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 Delegating Work 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.

Delegating work — who should do this task?

The core question: context performance

Getting work done well is a context problem. The agent best suited to a task is the one who already holds the context that task requires. Before you spawn a subagent or grind it out yourself, ask who that is.

The decision

Ask: how much would a blank slate have to read in before it could do this correctly?

  • A lot to read in → route to a real agent in the topology that already holds it. This is what the role structure is FOR — reviewers, QA, implementers who have been on a project over time. An agent that already understands the work produces a better, safer result than any amount of briefing.
  • A little — a prompt plus light grounding is enough → spawn a subagent.
  • Genuinely unsure → err toward a real agent, or do it yourself.

Subagents are underused — reach for them far more

For self-contained work that is fully specifiable in the prompt, spawn a subagent instead of spending your own context: tracing, research, grounding passes, summarizing, scanning log files, searching, fetching, web search, mechanical extraction with citations. This is the common shape and it is under-used.

The failure mode — the part that matters most

Subagents start as blank slates, and you cannot anticipate everything they need to know. Give one a task where there is a lot to understand and it will have blind spots it does not know it has — it will complete the task and return a false conclusion, not from weakness but because it lacked the context to know what "correct" even looked like.

This is the unknown-unknowns problem, and it is the single most common subagent failure: a poorly-contextualized summary is worse than no summary, because it arrives looking like an answer. Overreliance on subagents for context-heavy work causes more problems than it solves — and a team burned by it stops using subagents at all, which is the wrong correction. The fix is not "avoid subagents"; it is "match the task to the context it needs."

The practical test

Before delegating to a blank slate, ask: would a wrong-but-plausible answer here be detectable? If the requester cannot check the result against something they already know — a citation they can open, a count they can re-run — the blind-spot risk is unacceptable, and that task belongs with an agent that holds the context.

Worked examples

  • GOOD (→ subagent): extract how four apps implement a UI mechanism, with file:line citations and an explicit instruction to report facts, not recommendations. Self-contained; the requester can verify the citations.
  • BAD to delegate blind (→ routed to the context-holder): an independent security re-sweep of a rewritten git branch, where knowing which commit range must stay byte-identical, and that "lightly obfuscated real values" is the thing to hunt, takes a paragraph to explain and one sentence to get wrong. It went to the agent already holding the context.

It generalizes

This is really "route work to whoever holds the context," with subagents as the option for work that needs none. The audience is every agent — implementers and QA seats make this call as much as orchestrators do.

Hand over intent, not instructions

Whoever does the work, give them the goal, why it matters, the context you hold and where to find more, then let them decide how. When you have the requester's own words, pass them on instead of compressing them into a list of steps. A capable agent with the goal builds better than one painting inside a narrow spec, which gets exactly what was written and not what was wanted. Keep exact instructions for the few things that must be exact, and say why.

Once you've decided

  • Decided on subagents for 2+ independent tasks → dispatching-parallel-agents (the fan-out mechanics).
  • Subagents executing a plan's independent tasks → subagent-driven-development.
  • Routing to a real agent you will direct over multiple rounds → directing-partner-agents.
  • The right context-holder is the human → human-in-the-loop.

Frequently asked questions

What does the Delegating Work AI skill do?

Use when you have a task and must decide WHO does it — yourself, a spawned subagent, or another agent in the topology that already holds the context. The rule: how much would a blank slate have to read in to do this correctly? A lot → route to a context-holding agent; a little → subagent; unsure → a real agent or yourself. NOT the mechanics of fanning out (that's dispatching-parallel-agents / subagent-driven-development) — this is the who/whether decision, and the failure mode that makes teams abandon subagents.

Why use Delegating Work on TypingMind?

Because you install it once and use it with any model. Delegating Work 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 Delegating Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mvschwarz/openrig/tree/main/packages/daemon/assets/plugins/openrig-core/skills/delegating-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Delegating Work?

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 Delegating Work?

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

Is the Delegating Work AI skill free?

Yes. It is published on GitHub by mvschwarz under the Apache-2.0 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇