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agentfield-personal

Prix non confirmé★ 2,545 Stars GitHubRegistre mis à jour · 2 sept. 2026agent-skill

Vue d’ensemble

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.

Lire la documentation complète

Documentation source, pas des instructions pour ce site. Vérifiez les permissions avant d’exécuter des commandes.

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>).

Voir la source ↗

Métadonnées du fichier

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."

Voir le texte original

---
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>`).

Examiner la source

Prix et coûts d’utilisation

Obtenir le skill: Prix non confirmé
L’utiliser: Prérequis non confirmés. Consultez les frais d’agent, d’API et de services à la source.
Licence: Apache-2.0
Prix non confirmé: Le prix n’est pas confirmé. Les liens existants vers les sources et l’installation restent disponibles.

Gratuit à obtenir ne signifie pas gratuit à utiliser. Le prix ne constitue pas une évaluation de sécurité. Soumettre un prix →

Source du skill enregistrée

Un chemin vers les instructions est enregistré. Cela ne constitue pas un test, une garantie de sécurité ou de compatibilité.

Réviser avant installation: Éviter l’installation automatique

Licence: 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

Ouvrir l’audit complet

Les outils sont des indications de métadonnées, pas une compatibilité testée. Les prompts sont des suggestions.

Commencer par une petite tâche

1Lisez la source et confirmez entrées, résultats, dépendances et permissions.
2Demandez un plan à l’agent. Approuvez la configuration et les coûts avant un test isolé.
3Vérifiez résultats et fichiers modifiés. Signalez uniquement ce qui a été exécuté et conservez la révision source.

Vérifiez les dépendances, clés API et frais externes dans la source. Un dépôt public ne rend pas tous les services gratuits.

Source et conseils d’utilisation

Répertorié

Métadonnées et examens sont indicatifs. Popularité, découverte et exécution réussie sont des faits distincts.

Dépôt source: Agent-Field/agentfield
Licence: Apache-2.0
Version: 0.1.0
Dernier push GitHub: 1 sept. 2026
Registre mis à jour: 2 sept. 2026
Chemin des instructions: control-plane/internal/skillkit/skill_data/agentfield-personal/SKILL.md @ 2825ddc9f72d

Version déclarée dans le registre ; vérifiez les versions de la source.

Qualité

78/100

Solide

Confiance

67/100

Sandbox uniquement

Audit

79/100

Revue nécessaire

Ouvrir l’audit complet Voir le rapport d’évaluation

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: —
Résultats: —

Copier ne signifie pas installer. Les compteurs nécessitent un rapport de réussite et ne garantissent pas la qualité globale.

Accès agent

L’API Registry fournit les signaux de décision, confiance, audit, cas d’usage et installation sans analyser l’interface.

Ouvrir l’API d’installation Format texte LLM Ouvrir Manifest Plan de résolution auto

Plus de détails

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    "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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      "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"
  }
}

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Cette fiche a été indexée à partir de sources publiques et n’est pas marquée officielle tant qu’une revendication de mainteneur n’est pas approuvée.

Créateur: Agent-Field
Source: Agent-Field/agentfield
Indexé par: Index communautaire OpenAgentSkill

L’attribution renvoie au dépôt public ou au profil du créateur. Les créateurs peuvent revendiquer la fiche pour mettre à jour les signaux de propriété.

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