cursor-agent-supervisor
Skill30 starsChanged 10 months agoMITView on GitHub
Offloading tasks with a well-defined scope to sub-agents, for instance to use a sub-agent to implement a set of specs. Use this skill whenever a task should not need a broad knowledge of the whole project
In this repository13 other files it gives its agents.
- AGENTS.mdAGENTS.md
- github-pr-workflowSkill
- jj-todo-workflowSkill
- nix-profile-managerSkill
- nushell-plugin-builderSkill
- nushell-usageSkill
- package-npm-nixSkill
- read-bin-docsSkill
- research-assistantSkill
- searxng-searchSkill
- textual-builderSkill
- typst-writerSkill
- working-with-jjSkill
The file
We split this skill into the rules it gives an agent
5 rules. About 0 are common, 5 are distinctive, and 0 are very specific to this project.
≈0
Common · estimate
Many files like this have a rule of this kind.
≈5
Distinctive · estimate
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 56
Leave success criteria ambiguous
- Distinctive: line 86
Always verify the sub-agent's work:
- Distinctive: line 88
Check what changed: check the commit/revision diff, and adequation with the task
- Distinctive: line 89
Review the actual code: if the changes are non-trivial
- Distinctive: line 97
Fails or errors out: Read the output, fix any blocking issues, retry with more context
≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Agent Supervisor
You can start subagents (e.g. to work on a specific JJ revision) with:
# Create a conversation with a sub-agent:
cursor-agent --print --model <model-name> create-chat # Prints a conversation uuid
# Give a task to the sub-agent: (command will finish when sub-agent is done)
cursor-agent --print --resume <conversation-uuid> "...description of the subagent's task..."Model Selection
cursor-agent --print --model unknown-model will print an error that will list available models.
Unless you know which model is best for the task, just use sonnet-4.5 by default.
Invocation
Important: Sub-agent tasks can take several minutes. Always use a longer timeout:
# In your Shell tool call, set timeout to 10 minutes
timeout: 600000The --print flag makes the sub-agent run on its own, and reply only once it is done.
Setting Things up for the Sub-agent
Prepare things up so the agent can focus on its task (eg. if using JJ, don't ask them to run jj commands unless really necessary.
Notably run jj edit yourself first to get into the revision where the work should be done).
Ideally, the sub-agent should only have to read your instructions, hack on code, run build/tests, hack on code, etc.
until your instructions are implemented, and then reply with a final answer.
YOU are in charge of bookkeeping, not them. YOU have the big picture, they don't.
Giving Good Instructions
Give the subagent the instructions they will need to complete the task, but do not overwhelm them.
Do:
- Provide a clear, specific goal
- List key files to read/modify
- Specify what "done" looks like
- Include relevant patterns to follow or reference implementations
- Tell them which skills to load if needed
Don't:
- Dump or link to entire skills when they only need a subset
- Include irrelevant context
- Leave success criteria ambiguous
Task Description Template
Work on [specific task] in [repo/directory].
**Setup:**
- Skills to load or files to read IN FULL
- Summarized instructions from skills or files, tailored to the task
**Goal:**
[Clear description of what to implement/fix]
**Key files:**
- path/to/main/file.ts - [why it matters]
- path/to/reference.ts - [pattern to follow]
**Requirements:**
- [Specific requirement 1]
- [Specific requirement 2]
**Done when:**
- [Testable criterion 1]
- [Testable criterion 2]
- [e.g., "pnpm -F @pkg lint passes"]After Sub-agent Completes
Always verify the sub-agent's work:
- Check what changed: check the commit/revision diff, and adequation with the task
- Review the actual code if the changes are non-trivial
- Run verification commands (tests, type-check, lint)
- Update task status based on results (if using jj-todo-workflow)
When Things Go Wrong
If the sub-agent:
- Fails or errors out: Read the output, fix any blocking issues, retry with more context
- Produces incomplete work: Continue the work yourself or spawn another sub-agent with clarified instructions
- Goes off track: Review what happened, potentially revert changes, retry with tighter constraints
- Gets stuck on tooling: May need to provide explicit commands or paths
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
No reviews yet
People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
- It worked
- Partly
- It didn't
Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

