Delegating Work
Use when you have a task and must decide WHO does it, yourself, a spawned subagent, or...
Install
npx skills add mvschwarz/openrig --skill delegating-work -g -a claude-code -y- Repository
- mvschwarz/openrig8.5k
- Category
- AI and agents
- Updated
- 3d ago
- License
- Apache-2.0
- Context cost
- Lightabout 1,460 tokens
- Runs scripts
- No, instructions only
What it does
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, or how to brief the agent you hand it to, which depends on the model it runs. 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. Also use it to decide how far to trust a delegated result.
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.
