| name | agentfield-personal |
| version | 0.1.0 |
| description | Build and install a personal AI agent on this machine's AgentField: real source in ~/agentfield-agents, packaged with agentfield-package.yaml, installed with `af install`, started with `af run`, registered on the local control plane, and visible in AgentField Desktop with a keys form and an auto-start toggle. Use when the user wants an agent that lives on their machine as a persistent capability — a pricing agent, a support agent, a research agent — rather than a deployable project. A standalone repository with Docker Compose is the `agentfield` skill; calling agents that already exist is the `agentfield-use` skill. |
Building a personal AgentField agent
A personal agent is a capability installed on this machine. Once it's running,
the local control plane routes calls to it, other agents and coding assistants
can discover and delegate to it, and the AgentField Desktop app shows it with
its keys and lifecycle controls. The deliverable is not a repository — it is a
working, registered, callable agent.
This skill is the workflow for getting that done. It does not use Docker,
Docker Compose, a new Git repository, or a project CLAUDE.md unless the user
independently asks for one of those.
Before building
Check once whether an installed agent already covers the request: af list
for what's installed, and the control plane's discovery
(GET /api/v1/discovery/capabilities) for what each running agent's reasoners
actually do (the agentfield-use skill documents this surface). If a healthy
installed agent already does the job, say so and offer to use it instead of
building a duplicate — unless the user explicitly asked to build a new or
replacement agent, in which case build it. A stopped-but-capable installation
is not a reason to duplicate either; offer to start it with af run <name>.
For the agent's design, fetch the live SDK docs first —
https://agentfield.ai/llms.txt (and llms-full.txt for depth) — that is the
SDK ground truth. Decompose the job into reasoners the same way the
agentfield skill teaches: by cognitive jobs, not by a single catch-all
prompt. Personal agents are usually small — a handful of reasoners on one node
is normal — but the design bar is the same.
Workflow
-
Build stable real source. Choose one filesystem-safe kebab-case
package/name/node ID, <name>, and author the agent at
~/agentfield-agents/<name>. This directory is the durable source of truth
the user will edit later. Do not author in a temporary directory, a
disposable checkout, or the generated ~/.agentfield installation copy.
Run language-native syntax checks and tests on the source before
installing.
-
Package the source. Write the manifest at
~/agentfield-agents/<name>/agentfield-package.yaml. Put
config_version: v1 at the top — the manifest schema version, distinct
from the agent release version. Declare name, release version,
description, author, language, a runnable entrypoint.start that
matches the source and language, entrypoint.healthcheck: /health,
agent_node.node_id equal to <name>, its matching
agent_node.default_port, and only install dependencies the source needs.
config_version: v1
name: pricing-agent
version: 0.1.0
description: Answers pricing questions from the product catalog
author: <user>
language: python
entrypoint:
start: python main.py
healthcheck: /health
agent_node:
node_id: pricing-agent
default_port:
[]
