This guide walks through creating an agent from scratch. The example is a Research Assistant: a specialist that finds supporting facts, statistics, and references for the rest of the team. Substitute your own role if it fits better, but stay with one concrete example all the way through. Generic guides produce generic agents. If you have not yet completed first-run setup, do that first: First-run setup.Documentation Index
Fetch the complete documentation index at: https://docs.rundock.ai/llms.txt
Use this file to discover all available pages before exploring further.
Before you start
You need:- Rundock open with a workspace selected.
- A clear sense of what role you are creating. One sentence describing what the agent is for. If you cannot describe it in one sentence, the role is too vague to write good instructions for. Refine the role first.
1. Open the agents panel
Open the agents panel in Rundock and start the new-agent flow. Or, if Doc is in the conversation, ask Doc to help you create one. Either route writes the same markdown file to your workspace.2. Name and role
Slug
Use lowercase, hyphenated. The slug becomes the filename and the identifier other agents use to reference this one.Example:
research-assistantDisplay name
Short, memorable, character-style. This appears on the org chart card. Two to ten characters works well.Example:
Ari (or Research Assistant if you prefer descriptive names).3. Write the instructions
This is the most important part. The frontmatter is identity. The instructions body is behaviour. Good instructions focus on the role, not on technique. They tell the agent who it is, what it handles, what it does not handle, and how it should sound. They do not try to teach the agent how to do its job. The model already knows how to research, write, or analyse: it needs to know what role it is playing. Paste this into the instructions field as a starting point and adapt the bracketed parts:- The first paragraph names the role and the most common hand-off shape (“when X needs Y, they hand it to you”). The orchestrator reads the agent’s
descriptionfor routing, but other agents read the body when they decide whether to delegate to this one. - The boundary at the bottom is non-optional. Every specialist should have one. Without it, the agent will try to do whatever you ask, even when the work belongs elsewhere.
4. Set the org chart position
Pick a position on the chart. The orchestrator is0. Specialists are 1, 2, 3, and so on. Use a number that has not been claimed by another agent: collisions produce indeterminate sibling order.
Set reportsTo to the slug of the agent this one reports to. For a flat team, this is the orchestrator. For a multi-level team, it is the lead specialist.
You can also set:
- An
icon: a single unicode character used as the agent’s avatar. Avoid characters that look similar at small sizes. - A
colour: a hex value, quoted, for the org chart card and profile header. UK spelling: the field iscolour, notcolor. Hex values must be quoted because YAML treats a leading#as a comment.
5. Assign skills (optional)
If your agent should use existing skills, list them in theskills: array:
- slug lines. Inline arrays (skills: [a, b]) parse as empty.
If you do not have skills to assign yet, skip this. You can add them later. To create a skill from scratch, see Write a skill.
6. Save
Save the agent. Rundock writes the markdown file to.claude/agents/<slug>.md in your workspace. The agent appears on the org chart in the position you set.
The agent file is just a markdown file. You can edit it directly in any text editor (VS Code, Obsidian, even TextEdit) or through Rundock’s interface. Both routes write to the same file.
7. Test the agent
Click the new agent on the org chart to open a conversation scoped to it. Send a real task appropriate to the role. For the Research Assistant example:- Did the agent stay in its lane? The Research Assistant should return bullet points with sources, not a polished essay.
- Did the agent handle the boundary? If the request is partly outside scope (“research this and then draft a tweet”), the agent should hand the drafting back rather than try it.
- Did the agent ask for clarification when the request was ambiguous? An agent that guesses is harder to trust than an agent that asks.
What good looks like
You know an agent is working when:- You can talk to it in natural language and it stays in role.
- The orchestrator delegates the right kinds of work to it without prompting.
- Other specialists hand off to it when their task needs help in this domain.
- You stop noticing it. The agent does its job and you focus on the work that needs you.
Where to next
Write a skill
Add a reusable workflow to one or more agents.
Set up a routine
Schedule a prompt to fire automatically on a cadence.
Agent file format
Every frontmatter field, with a complete worked example.
Set up a team workspace
Share your agents across a team using a synced folder.