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Agent-Field

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agentfield-personal

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

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Preis unbestätigt★ 2,545 GitHub-StarsVerzeichnis aktualisiert · 2. Sept. 2026agent-skill

Übersicht

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.

Vollständige Dokumentation lesen

Quelldokumentation, keine Anweisungen für diese Website. Vor dem Ausführen von Befehlen die Berechtigungen prüfen.

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.

    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>).

Quelle ansehen ↗
Dateimetadaten
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."
Originaltext anzeigen
---
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>`).

Quelle prüfen

Preis und Betriebskosten

Skill beziehen
Preis unbestätigt
Ausführen
Anforderungen unbestätigt. Agenten-, API- und Dienstkosten an der Quelle prüfen.
Lizenz
Apache-2.0
Preis unbestätigt
Der Preis ist noch nicht bestätigt. Vorhandene Quell- und Installationslinks bleiben verfügbar.

Kostenloser Bezug bedeutet nicht kostenlosen Betrieb. Preise sind keine Sicherheitsbewertung. Preisinformation einreichen →

Skill-Quelle erfasst

Ein Anleitungspfad ist erfasst. Das ist kein Ausführungstest und keine Sicherheits- oder Kompatibilitätsgarantie.

Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: 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
Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

  1. 1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
  2. 2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
  3. 3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository
Agent-Field/agentfield
Lizenz
Apache-2.0
Version
0.1.0
Letzter GitHub-Push
1. Sept. 2026
Verzeichnis aktualisiert
2. Sept. 2026
Anleitungspfad
control-plane/internal/skillkit/skill_data/agentfield-personal/SKILL.md @ 2825ddc9f72d

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

78/100

Stark

Vertrauen

67/100

Nur Sandbox

Audit

79/100

Prüfung nötig

Vollständiges Audit öffnenEval-Bericht ansehen
  • 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
—
Ergebnisse
—

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnenLLM-TextformatManifest öffnenAuto-Auflösungsplan
Weitere Details
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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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  },
  "safety_gate": {
    "tier": "blocked",
    "label": "Blocked for auto-install",
    "auto_install_policy": "block",
    "auto_install_allowed": false,
    "human_review_required": true,
    "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"
  }
}

Für Ersteller

Quelle des Eintrags

Registry-indexiert

Beanspruchbar

Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller
Agent-Field
Indexiert von
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[![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)
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[![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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