AgentField is an open-source control plane that turns AI-agent code into callable APIs and coordinates its execution across workflows. Developers use it to route, queue, retry, trace, and scale agents written in Python, Go, or TypeScript, from individual agents to large fan-outs. Catalogue add-ons support working with AgentField and its agent-building workflow.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/agent-field/agentfield/agentfield-personalnpx skills add Agent-Field/agentfield --skill agentfield-personalgit clone --depth 1 https://github.com/Agent-Field/agentfieldWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/agent-field/agentfield/agentfield-personal)<a href="https://agentmods.dev/skills/agent-field/agentfield/agentfield-personal"><img src="https://agentmods.dev/badge/skills/agent-field/agentfield/agentfield-personal.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00134 | $0.01452 |
| Opus 5 | $0.00067 | $0.00726 |
| Sonnet 5 | $0.00027 | $0.00290 |
| Haiku 4.5 | $0.00013 | $0.00145 |
Grade A, and why
agentfield-personal scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 117 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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~/.agentfieldinstallation copy. Run language-native syntax checks and tests on the source before installing.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 117 lines · 134 tokens per session scan A 3df3d8eff1be
agentfield-personal is a skill published in the GitHub repository Agent-Field/agentfield (2,550 stars, last pushed yesterday), licensed Apache-2.0. It adds 134 tokens to every session and 1,452 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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