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agentfield-personal

价格未确认★ 2,545 GitHub Stars目录更新于 · 2026年9月2日agent-skill

概览

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.

展开完整说明

以下为来源文档，不是本网站的操作指令。执行命令前请先核实权限。

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: 9301
dependencies:
  python: [requests]
user_environment:
  - name: OPENROUTER_API_KEY
    description: LLM provider key used for all reasoning calls
    type: secret
    scope: global
```
Declare secrets safely. For every external key the source actually uses, declare a user_environment entry with name, an actionable description, type: secret, and an explicit scope. Use scope: global only for deliberately reusable credentials such as a model-provider key; use scope: node for credentials or configuration specific to this agent. Do not declare invented keys.
Install and configure. Run af install ~/agentfield-agents/<name>. Configure each declared global key with af secrets set KEY and each node key with af secrets set --node <name> KEY, letting the CLI prompt/stdin take the value. Never invent, echo, commit, put into agentfield-package.yaml, or include secret values in a handoff.
Start and verify registration. Run af run <name>, then poll GET ${AGENTFIELD_SERVER:-http://localhost:8080}/api/v1/nodes until the node ID is registered in an active/healthy state. An install entry, af list entry, or successful process spawn alone is not success.
Invoke live. Invoke the public entry reasoner through the control plane with a representative request. For nontrivial work use async execution and poll (the agentfield-use skill documents the execute/poll surface); require a terminal successful result before calling the build done.
Handle failures honestly. Diagnose and safely retry correctable failures from installation, secret setup, startup, registration, or invocation (af logs <name> is the first stop). If a required secret value is known only to the user, stop with a blocking handoff that names the needed key and scope but never its value. Do not claim completion until healthy registration and a live reasoner result both succeed.
Hand off. Tell the user the agent is installed, running, and now appears in the AgentField Desktop app, where its declared keys are presented as a form and its lifecycle has an auto-start toggle. Include: the stable source path, the manifest path, the installed name, the public entry reasoner's invocation target, the registration and live-call verification results, and the commands to restart (af stop <name> && af run <name>), stop (af stop <name>), inspect logs (af logs <name>), and update after source edits (af install ~/agentfield-agents/<name> followed by af run <name>).

查看来源 ↗

文件元数据

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."

查看原始文本

---
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

1. **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.

2. **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.

```yaml
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: 9301
dependencies:
python: [requests]
user_environment:
- name: OPENROUTER_API_KEY
description: LLM provider key used for all reasoning calls
type: secret
scope: global
```

3. **Declare secrets safely.** For every external key the source actually
uses, declare a `user_environment` entry with `name`, an actionable
`description`, `type: secret`, and an explicit scope. Use `scope: global`
only for deliberately reusable credentials such as a model-provider key;
use `scope: node` for credentials or configuration specific to this agent.
Do not declare invented keys.

4. **Install and configure.** Run `af install ~/agentfield-agents/<name>`.
Configure each declared global key with `af secrets set KEY` and each node
key with `af secrets set --node <name> KEY`, letting the CLI prompt/stdin
take the value. Never invent, echo, commit, put into
`agentfield-package.yaml`, or include secret values in a handoff.

5. **Start and verify registration.** Run `af run <name>`, then poll
`GET ${AGENTFIELD_SERVER:-http://localhost:8080}/api/v1/nodes` until the
node ID is registered in an active/healthy state. An install entry, `af
list` entry, or successful process spawn alone is not success.

6. **Invoke live.** Invoke the public entry reasoner through the control plane
with a representative request. For nontrivial work use async execution and
poll (the `agentfield-use` skill documents the execute/poll surface);
require a terminal successful result before calling the build done.

7. **Handle failures honestly.** Diagnose and safely retry correctable
failures from installation, secret setup, startup, registration, or
invocation (`af logs <name>` is the first stop). If a required secret value
is known only to the user, stop with a blocking handoff that names the
needed key and scope but never its value. Do not claim completion until
healthy registration and a live reasoner result both succeed.

8. **Hand off.** Tell the user the agent is installed, running, and now
appears in the AgentField Desktop app, where its declared keys are
presented as a form and its lifecycle has an auto-start toggle. Include:
the stable source path, the manifest path, the installed name, the public
entry reasoner's invocation target, the registration and live-call
verification results, and the commands to restart
(`af stop <name> && af run <name>`), stop (`af stop <name>`), inspect logs
(`af logs <name>`), and update after source edits
(`af install ~/agentfield-agents/<name>` followed by `af run <name>`).

查看并核实来源

获取价格与运行成本

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许可证: Apache-2.0
价格未确认: 我们尚未确认此 Skill 的价格，现有来源与安装入口仍可使用。

免费获取不代表免费运行，价格标签不代表安全评级。提交价格信息 →

已记录技能来源

已记录技能指令路径，不代表本站运行测试、安全保证或兼容性认证。

安装前审查: 避免自动安装

许可证: Apache-2.0

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

打开完整审计

工具列表来自元数据，并非已测试的兼容性；Agent 提示词是建议的交接方式。

从一个小任务开始

1阅读来源，确认输入、预期输出、依赖和权限。
2先让 Agent 提出计划，批准环境配置和费用，再进行隔离的小规模测试。
3检查输出和变更文件，只报告实际执行结果，并保留来源版本以便复现。

请在来源中核实依赖、API 密钥及第三方费用。公开仓库不代表所有服务免费。

来源与使用须知

已收录

仓库元数据和审核信号仅供参考。受欢迎、已发现来源、成功运行是不同的事实。

来源仓库: Agent-Field/agentfield
许可证: Apache-2.0
版本: 0.1.0
最近 GitHub 推送: 2026年9月1日
目录更新于: 2026年9月2日
技能指令路径: control-plane/internal/skillkit/skill_data/agentfield-personal/SKILL.md @ 2825ddc9f72d

版本来自目录元数据，使用前请核实来源发布记录。

质量

78/100

强

信任

67/100

仅限沙盒

审计

79/100

需审查

打开完整审计查看评估报告

Dependency or permission surface needs review
Permission surface may require sandboxing
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

Verified installs: —
结果: —

复制不等于安装。安装数需有成功安装回报，不代表全面的质量保证。

Agent 接入

本页通过 Registry API 提供相同的决策、信任、审计、场景和安装信号，让 Agent 无需抓取界面即可排序。

打开安装 API LLM 文本格式打开 Manifest 自动解析计划

更多详情

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  "skill": {
    "slug": "agent-field-agentfield-personal",
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    "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.",
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      "label": "No agent outcome data yet"
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    ]
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    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 78,
    "label": "Strong"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "Research agents",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "yanliudesign-mono-color-skill",
      "name": "mono-color",
      "url": "https://www.openagentskill.com/skills/yanliudesign-mono-color-skill",
      "stars": 1919,
      "install_command": "npx skills add yanliudesign/mono-color-skill --skill mono-color",
      "trust_score": 83,
      "audit_score": 90
    },
    {
      "slug": "assafelovic-gpt-researcher",
      "name": "GPT Researcher",
      "url": "https://www.openagentskill.com/skills/assafelovic-gpt-researcher",
      "stars": 29542,
      "install_command": "",
      "trust_score": 85,
      "audit_score": 90
    }
  ],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Quality score needs review",
    "Permission surface needs review: secrets or environment access, shell or command execution"
  ],
  "agent_contract": {
    "task_input": "Use agentfield-personal in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 75/100 Strong shortlist",
      "Audit: 79/100 Needs review",
      "Safety: 31/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "agent-field-agentfield-personal (agentfield-personal)",
      "install_command": "npx skills add Agent-Field/agentfield --skill agentfield-personal",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "agent-field-agentfield-personal",
      "task": "Use agentfield-personal in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/agent-field-agentfield-personal",
    "api": "https://www.openagentskill.com/api/agent/skills/agent-field-agentfield-personal",
    "audit": "https://www.openagentskill.com/skills/agent-field-agentfield-personal/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=agent-field-agentfield-personal&task=Use%20agentfield-personal%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20agentfield-personal%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20agentfield-personal%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/agent-field-agentfield-personal/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/agent-field-agentfield-personal"
  }
}

创作者工具

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可认领

此列表来自公开来源，维护者认领获批前不会标记为官方。

创作者: Agent-Field
来源: Agent-Field/agentfield
收录方: OpenAgentSkill 社区索引

归属链接指向公开仓库或创作者主页。创作者可认领列表以更新所有权信号。

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这条 Registry 收录列表归属于 Agent-Field，但尚未标记为官方。认领后可增加已验证所有者信号，使后续发布、安装和审计更新更值得信赖。

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将证据徽章加入你的 README

在开发者评估仓库的位置展示规范页面、当前信任与审计信号，以及真实的 Agent 验证证据。

[![Listed on OpenAgentSkill](https://www.openagentskill.com/api/badge/agent-field-agentfield-personal?metric=listed&label=Listed)](https://www.openagentskill.com/skills/agent-field-agentfield-personal?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Trust](https://www.openagentskill.com/api/badge/agent-field-agentfield-personal?metric=trust&label=Trust)](https://www.openagentskill.com/skills/agent-field-agentfield-personal?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)
[![OpenAgentSkill Audit](https://www.openagentskill.com/api/badge/agent-field-agentfield-personal?metric=audit&label=Audit)](https://www.openagentskill.com/skills/agent-field-agentfield-personal/audit)
[![Agent Proven](https://www.openagentskill.com/api/badge/agent-field-agentfield-personal?metric=proven&label=Agent%20Proven)](https://www.openagentskill.com/skills/agent-field-agentfield-personal?ref=github&utm_source=github&utm_medium=referral&utm_campaign=creator_badge)

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