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 skills add Agent-Field/agentfield --skill agentfield-usegit 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-use)<a href="https://agentmods.dev/skills/agent-field/agentfield/agentfield-use"><img src="https://agentmods.dev/badge/skills/agent-field/agentfield/agentfield-use/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/agent-field/agentfield/agentfield-use"><img src="https://agentmods.dev/badge/skills/agent-field/agentfield/agentfield-use.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 4 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 116 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Tool Misuse · line 556 Tool parameters are crafted to achieve unintended or unsafe behavior. Parameter abuse can bypass intended safety checks (e.g. shell=True, --force, dangerous glob patterns).Fix: Validate all tool parameters against an allowlist. Reject dangerous parameter values (shell=True, --force, -rf /) and use safe defaults.
- medium Excessive Agency · line 219 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Data Exfiltration · line 556 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 5.1 | $0.00259 | $0.09434 |
| Opus 5.5 | $0.00104 | $0.03774 |
| Sonnet 5.5 | $0.00052 | $0.01887 |
| Haiku 4.5 | $0.00026 | $0.00943 |
Grade A, and why
agentfield-use scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`-H 'X-API-Key: <key>'` for curl. Do not export `AGENTFIELD_SERVER` yourself to How it starts
The opening of the file, as written. The whole thing — 641 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AgentField subharnesses — offload work instead of doing it inline
If AGENTFIELD_HARNESS_DEPTH is set, you are running inside an AgentField
harness session: do not dispatch to AgentField agents unless explicitly asked.
A machine with AgentField has one or more control planes — a local one
(default http://localhost:8080) and possibly a cloud deployment configured
in AgentField Desktop — plus agent nodes installed under ~/.agentfield.
Each node exposes reasoners: typed functions you call through the control
plane, which routes the call, records the run, and returns the result. You never
talk to an agent's own port.
Those installed agents are subharnesses: workers you offload to. They run on smaller, cheaper models, they run in parallel, they run off your context, and every run is recorded on the control plane and watchable in its web UI. A review that would cost you 40k tokens of reading costs you one dispatch and one result.
Vocabulary rule. Internally — commands, API fields, discovery output —
everything stays agent / reasoner / node, exactly matching the CLI and API.
In what you SAY TO THE USER, call them subharnesses: "your AgentField
subharnesses", "the pr-af subharness". Never let the wording cost clarity: if the
user needs a command or a node name, give the literal one.
Offload by default
Whenever you have a discrete task — one the user delegated, or one that arose inside your own work — the first question is not "how do I do this?" but "does an installed subharness cover this?" When one does, dispatching to it is the default path, not an option to offer.
Coverage is the test, not size. A small job a subharness covers still goes to the subharness; a large job nothing covers stays with you. This skill carries no list of offloadable task types and cannot: users install new subharnesses at any time, so runtime discovery (§1) is the only source of truth about what your fleet does.
The check is cheap — that is the whole design. One af ls -e (or one
capabilities call) answers "does anything cover this?". Discover once per
session, remember the roster, and match later tasks against what you learned;
re-discover only when something suggests the fleet changed — an install, a start,
or a miss you doubt. Consulting this skill on a task nothing covers costs one
cheap lookup. That is the trade, deliberately.
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.
- 1mo ago First seen · 641 lines · 259 tokens per session scan A 3724dc056934
agentfield-use is a skill published in the GitHub repository Agent-Field/agentfield (2,614 stars, last pushed today), licensed Apache-2.0. It adds 259 tokens to every session and 9,434 once invoked, about $0.0010 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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