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docker-agent-deploy

Prix non confirmé★ 221 Stars GitHubRegistre mis à jour · 26 sept. 2026agent-skill

Vue d’ensemble

Use this skill when exposing a Docker Agent as a server (MCP, HTTP API, A2A, ACP, or OpenAI-compatible chat), distributing an agent via an OCI registry with `docker agent share`, or measuring agent quality with `docker agent eval`. Even if the user just says they want to "turn my agent into an MCP server", "let Claude Desktop use my agent", "publish my agent to Docker Hub", "push my agent like an image", or "test my agent in CI", this skill applies. Covers `serve mcp/api/a2a/acp/chat` listen addresses and auth flags, `share push/pull`, eval session JSON format, scoring metrics, and the `--baseline` regression gate.

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Overview

This skill owns the integration surface of Docker Agent: making an agent reachable by other software (docker agent serve), distributing it through an OCI registry the way container images are distributed (docker agent share), and proving it still behaves after a change (docker agent eval). It assumes the agent config already exists — see docker-agent-config for authoring it, and docker-agent-run for interactive/local invocation.

When to use this skill

Activate this skill when:

The user wants an agent reachable over MCP, an OpenAI-compatible chat endpoint, a plain HTTP API, or A2A/ACP.
The user wants to publish an agent to Docker Hub (or any OCI registry) or pull one someone else published.
The user wants automated evaluations (regression tests) for an agent, or wants to gate CI on eval results.

Do not use this skill when

Do not use this skill when:

The task is authoring the agent.yaml itself (models, toolsets, sub_agents) — use docker-agent-config.
The task is running the agent interactively on a developer's machine, choosing --safety/--sandbox, or aliases — use docker-agent-run.

Core guidance

Serving an agent

Five server modes, each with its own default loopback listen address — never expose any of them beyond loopback without authentication:

Mode	Default listen	Auth flag	Has `--safety`?
`serve mcp`	`127.0.0.1:8081`	`--auth-token` (only with `--http`)	Yes (only with `--http`)
`serve api`	`127.0.0.1:8080`	`--auth-token`	No
`serve chat`	`127.0.0.1:8083`	`--api-key` / `--api-key-env`	Yes
`serve a2a`	`127.0.0.1:8082`	`--auth-token`	Yes
`serve acp`	(stdio only)	n/a	No

docker agent serve mcp ./agent.yaml --http --listen 127.0.0.1:9090 --auth-token "$TOKEN"

serve mcp defaults to stdio transport (for local clients like Claude Desktop); pass --http only when you need a network-reachable MCP endpoint, and set --auth-token whenever you do.
Binding any server flag to a non-loopback address without an auth token/key is refused; --insecure-no-auth exists to force it and must be treated as a deliberate, documented exception, never a default.
serve mcp (with --http), serve chat, and serve a2a expose --safety (strict/balanced/restricted/autonomous); Docker's docs state it defaults to restricted for these modes when unset. serve api and serve acp expose no --safety flag at all. Never raise --safety to autonomous on a network-reachable listener; if a served agent must approve more, prefer balanced and keep auth enabled.
serve api accepts a directory instead of a single file: every .yaml/.yml/.hcl in it is exposed under /api/agents. Use --session-workingdir-root to confine session working directories when the server is reachable by more than one user.

Push and pull agent configs the same way you push and pull images — same registry, same docker login auth:

docker agent share push ./agent.yaml docker.io/username/my-agent:latest
docker agent share pull docker.io/username/my-agent:latest

instruction_file contents are inlined into the pushed artifact automatically, so a published agent stays self-contained — you do not need to bundle the referenced files separately.
Pin sub_agents that reference the pushed artifact to a digest (name@sha256:...) once published, to avoid a per-run registry lookup and to guarantee the exact config a consumer gets.
Use --force on share pull only when you intend to overwrite a local copy that already exists; without it, an existing local config is left untouched.

Evaluating agents

Evals live in an evals/ directory next to the agent config by default; each eval is one JSON session file capturing a user message, the recorded tool calls, and an evals object with the scoring criteria.
Create eval sessions from real conversations rather than hand-writing JSON: run the agent interactively, then use the /eval slash command in the TUI to save the session, and edit in relevance/size/assertions criteria afterward.
Four scoring dimensions: Tool Calls (F1 against the recorded sequence), Relevance (LLM-judge, --judge-model, default anthropic/claude-opus-5), Size (S/M/L/XL response-length bucket), and Assertions (deterministic checks; see the complete assertion-type list in references/eval-format.md). Prefer assertions over relevance when a check can be exact: they need no judge model and are deterministic, not approximation-prone.
Evaluations run inside containers for isolation; a Docker-compatible runtime is required. Dedicated provider API keys (ANTHROPIC_API_KEY/OPENAI_API_KEY) are forwarded automatically. GITHUB_TOKEN/GH_TOKEN are not forwarded automatically (they're broad host credentials, not model keys) — pass them explicitly with -e GITHUB_TOKEN when an agent's provider needs one (e.g. github-copilot).
Gate CI on regressions, not on absolute scores, with --baseline:
```
docker agent eval ./agent.yaml --baseline results/2026-08-01-run.json --regression-tolerance 0.05
```
A previously-passing eval that now fails always gates regardless of tolerance; cost changes are reported but never gate. A baseline or run with zero evaluations (e.g. an --only pattern matching nothing) is rejected rather than reported as passing.
Use --keep-containers plus your runtime's exec to inspect a failed eval's container; the eval's .db session file holds the full conversation for offline debugging.

Verify

After changing a served agent's config, re-run its evals with the same explicit --safety value used in the deployment before restarting the listener — this catches an approval-policy regression before it reaches traffic. If a rollout must be rolled back, restore the prior config and safety flag; never restore an unauthenticated listener as a rollback shortcut.

For writing or changing the underlying agent.yaml, use docker-agent-config.
For local/interactive runs, safety-mode choice, and sandboxing, use docker-agent-run.

References

references/eval-format.md — full eval session JSON schema and CLI flag table.
references/sources.md — provenance of every rule in this skill.

Assets

assets/eval-session-example.json — a minimal eval session file to copy and adapt.

Checks

checks/verification.md — Verification runbook for serving, sharing, and evaluating an agent.

Voir la source ↗

Métadonnées du fichier

name: docker-agent-deploy
description: Use this skill when exposing a Docker Agent as a server (MCP, HTTP API, A2A, ACP, or OpenAI-compatible chat), distributing an agent via an OCI registry with `docker agent share`, or measuring agent quality with `docker agent eval`. Even if the user just says they want to "turn my agent into an MCP server", "let Claude Desktop use my agent", "publish my agent to Docker Hub", "push my agent like an image", or "test my agent in CI", this skill applies. Covers `serve mcp/api/a2a/acp/chat` listen addresses and auth flags, `share push/pull`, eval session JSON format, scoring metrics, and the `--baseline` regression gate.
license: Apache-2.0
compatibility: Requires the docker-agent CLI plugin (Docker Desktop 4.63+, or standalone). `docker agent eval` additionally requires a Docker-compatible container runtime (Docker Desktop/Engine, or Podman via `--container-runtime`). Verified against docker-agent as shipped with Docker CLI 29.7.2.

Voir le texte original

---
name: docker-agent-deploy
description: Use this skill when exposing a Docker Agent as a server (MCP, HTTP API, A2A, ACP, or OpenAI-compatible chat), distributing an agent via an OCI registry with `docker agent share`, or measuring agent quality with `docker agent eval`. Even if the user just says they want to "turn my agent into an MCP server", "let Claude Desktop use my agent", "publish my agent to Docker Hub", "push my agent like an image", or "test my agent in CI", this skill applies. Covers `serve mcp/api/a2a/acp/chat` listen addresses and auth flags, `share push/pull`, eval session JSON format, scoring metrics, and the `--baseline` regression gate.
license: Apache-2.0
compatibility: Requires the docker-agent CLI plugin (Docker Desktop 4.63+, or standalone). `docker agent eval` additionally requires a Docker-compatible container runtime (Docker Desktop/Engine, or Podman via `--container-runtime`). Verified against docker-agent as shipped with Docker CLI 29.7.2.
---

# Docker Agent: Serving, Sharing, and Evaluating

## Overview
This skill owns the integration surface of Docker Agent: making an agent
reachable by other software (`docker agent serve`), distributing it through
an OCI registry the way container images are distributed (`docker agent
share`), and proving it still behaves after a change (`docker agent eval`).
It assumes the agent config already exists — see `docker-agent-config` for
authoring it, and `docker-agent-run` for interactive/local invocation.

## When to use this skill
Activate this skill when:
- The user wants an agent reachable over MCP, an OpenAI-compatible chat endpoint, a plain HTTP API, or A2A/ACP.
- The user wants to publish an agent to Docker Hub (or any OCI registry) or pull one someone else published.
- The user wants automated evaluations (regression tests) for an agent, or wants to gate CI on eval results.

## Do not use this skill when
Do not use this skill when:
- The task is authoring the agent.yaml itself (models, toolsets, sub_agents) — use `docker-agent-config`.
- The task is running the agent interactively on a developer's machine, choosing `--safety`/`--sandbox`, or aliases — use `docker-agent-run`.

## Core guidance

### Serving an agent
- Five server modes, each with its own default loopback listen address —
never expose any of them beyond loopback without authentication:

| Mode | Default listen | Auth flag | Has `--safety`? |
| --- | --- | --- | --- |
| `serve mcp` | `127.0.0.1:8081` | `--auth-token` (only with `--http`) | Yes (only with `--http`) |
| `serve api` | `127.0.0.1:8080` | `--auth-token` | No |
| `serve chat` | `127.0.0.1:8083` | `--api-key` / `--api-key-env` | Yes |
| `serve a2a` | `127.0.0.1:8082` | `--auth-token` | Yes |
| `serve acp` | (stdio only) | n/a | No |

```bash
docker agent serve mcp ./agent.yaml --http --listen 127.0.0.1:9090 --auth-token "$TOKEN"
```
- `serve mcp` defaults to stdio transport (for local clients like Claude
Desktop); pass `--http` only when you need a network-reachable MCP
endpoint, and set `--auth-token` whenever you do.
- Binding any server flag to a non-loopback address without an auth
token/key is refused; `--insecure-no-auth` exists to force it and must be
treated as a deliberate, documented exception, never a default.
- `serve mcp` (with `--http`), `serve chat`, and `serve a2a` expose
`--safety` (`strict`/`balanced`/`restricted`/`autonomous`); Docker's docs
state it defaults to `restricted` for these modes when unset. `serve api`
and `serve acp` expose no `--safety` flag at all. Never raise `--safety`
to `autonomous` on a network-reachable listener; if a served agent must
approve more, prefer `balanced` and keep auth enabled.
- `serve api` accepts a directory instead of a single file: every
`.yaml`/`.yml`/`.hcl` in it is exposed under `/api/agents`. Use
`--session-workingdir-root` to confine session working directories when
the server is reachable by more than one user.

### Sharing agents via OCI registries
- Push and pull agent configs the same way you push and pull images — same
registry, same `docker login` auth:
```bash
docker agent share push ./agent.yaml docker.io/username/my-agent:latest
docker agent share pull docker.io/username/my-agent:latest
```
- `instruction_file` contents are inlined into the pushed artifact
automatically, so a published agent stays self-contained — you do not need
to bundle the referenced files separately.
- Pin `sub_agents` that reference the pushed artifact to a digest
(`name@sha256:...`) once published, to avoid a per-run registry lookup and
to guarantee the exact config a consumer gets.
- Use `--force` on `share pull` only when you intend to overwrite a local
copy that already exists; without it, an existing local config is left
untouched.

### Evaluating agents
- Evals live in an `evals/` directory next to the agent config by default;
each eval is one JSON session file capturing a user message, the recorded
tool calls, and an `evals` object with the scoring criteria.
- Create eval sessions from real conversations rather than hand-writing
JSON: run the agent interactively, then use the `/eval` slash command in
the TUI to save the session, and edit in `relevance`/`size`/`assertions`
criteria afterward.
- Four scoring dimensions: Tool Calls (F1 against the recorded sequence),
Relevance (LLM-judge, `--judge-model`, default `anthropic/claude-opus-5`),
Size (S/M/L/XL response-length bucket), and Assertions (deterministic
checks; see the complete assertion-type list in `references/eval-format.md`).
Prefer assertions over `relevance` when a check can be exact: they need no
judge model and are deterministic, not approximation-prone.
- Evaluations run inside containers for isolation; a Docker-compatible
runtime is required. Dedicated provider API keys
(`ANTHROPIC_API_KEY`/`OPENAI_API_KEY`) are forwarded automatically.
`GITHUB_TOKEN`/`GH_TOKEN` are **not** forwarded automatically (they're
broad host credentials, not model keys) — pass them explicitly with
`-e GITHUB_TOKEN` when an agent's provider needs one (e.g.
`github-copilot`).
- Gate CI on regressions, not on absolute scores, with `--baseline`:
```bash
docker agent eval ./agent.yaml --baseline results/2026-08-01-run.json --regression-tolerance 0.05
```
A previously-passing eval that now fails always gates regardless of
tolerance; cost changes are reported but never gate. A baseline or run
with zero evaluations (e.g. an `--only` pattern matching nothing) is
rejected rather than reported as passing.
- Use `--keep-containers` plus your runtime's `exec` to inspect a failed
eval's container; the eval's `.db` session file holds the full
conversation for offline debugging.

### Verify
- After changing a served agent's config, re-run its evals with the same
explicit `--safety` value used in the deployment before restarting the
listener — this catches an approval-policy regression before it reaches
traffic. If a rollout must be rolled back, restore the prior config and
safety flag; never restore an unauthenticated listener as a rollback
shortcut.

## Related skills
- For writing or changing the underlying `agent.yaml`, use `docker-agent-config`.
- For local/interactive runs, safety-mode choice, and sandboxing, use `docker-agent-run`.

## References
- `references/eval-format.md` — full eval session JSON schema and CLI flag table.
- `references/sources.md` — provenance of every rule in this skill.

## Assets
- `assets/eval-session-example.json` — a minimal eval session file to copy and adapt.

## Checks
- `checks/verification.md` — Verification runbook for serving, sharing, and evaluating an agent.

Examiner la source

Prix et coûts d’utilisation

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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.

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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
Stars/forks activity: 221 stars, 10 forks; issue activity unavailable in current metadata
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

RépertoriéExaminé par IA

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

Dépôt source: docker/skills
Licence: Apache-2.0
Version: Unknown
Dernier push GitHub: 25 sept. 2026
Registre mis à jour: 26 sept. 2026
Chemin des instructions: skills/docker-agent-deploy/SKILL.md @ ddbf34bfd8be

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

Qualité

70/100

Solide

Confiance

64/100

Sandbox uniquement

Audit

78/100

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
Stars/forks activity: 221 stars, 10 forks; issue activity unavailable in current metadata
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

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

Plus de détails

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      "installSafety": "standard package or runtime install path",
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      "documentation": "Strong README/SKILL.md context",
      "agentOutcomes": "No agent outcome data yet"
    },
    "outcome_evidence": {
      "total": 0,
      "successes": 0,
      "failures": 0,
      "not_relevant": 0,
      "success_rate": null,
      "recent_success_rate": null,
      "recent_failure_rate": null,
      "install_attempts": 0,
      "install_success_rate": null,
      "risk_blocked": 0,
      "setup_required": 0,
      "avg_output_quality": null,
      "production_outcomes": 0,
      "last_outcome_at": null,
      "label": "No agent outcome data yet"
    },
    "auto_install": {
      "allowed": false,
      "sandbox_required": true,
      "reason": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
    },
    "best_for": [
      "design-creative",
      "agent-skill"
    ],
    "known_risks": [
      "Quality score needs review",
      "Permission surface needs review: secrets or environment access, shell or command execution",
      "Stars/forks activity: 221 stars, 10 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "agent_proven": {
    "version": "agent-proven-v1",
    "score": 0,
    "tier": "unproven",
    "label": "Needs first agent run",
    "summary": "No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.",
    "metrics": {
      "totalOutcomes": 0,
      "successfulOutcomes": 0,
      "failedOutcomes": 0,
      "installAttempts": 0,
      "installSuccessRate": null,
      "successRate": null,
      "recentSuccessRate": null,
      "recentFailureRate": null,
      "riskBlocked": 0,
      "setupRequired": 0,
      "notRelevant": 0,
      "avgOutputQuality": null,
      "avgTimeToUsefulMs": null,
      "productionOutcomes": 0,
      "humanReviewRequired": 0,
      "uniqueAgents": 0,
      "lastOutcomeAt": null
    },
    "signals": [],
    "penalties": [
      "No real agent outcome evidence yet"
    ]
  },
  "audit": {
    "score": 78,
    "risk_level": "needs_review",
    "risk_label": "Needs review",
    "warnings": [
      "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",
      "Stars/forks activity: 221 stars, 10 forks; issue activity unavailable in current metadata",
      "Dependency/runtime risk: command execution surface, credential or environment access",
      "Permission surface: secrets or environment access, shell or command execution"
    ]
  },
  "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": 70,
    "label": "Strong"
  },
  "supply": {
    "track": "Design and creative production",
    "scenario": "Design and creative",
    "maintenance": "15d since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "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 docker-agent-deploy 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: 72/100 Strong shortlist",
      "Audit: 78/100 Needs review",
      "Safety: 34/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "docker-docker-agent-deploy (docker-agent-deploy)",
      "install_command": "npx skills add docker/skills --skill docker-agent-deploy",
      "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": "docker-docker-agent-deploy",
      "task": "Use docker-agent-deploy 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/docker-docker-agent-deploy",
    "api": "https://www.openagentskill.com/api/agent/skills/docker-docker-agent-deploy",
    "audit": "https://www.openagentskill.com/skills/docker-docker-agent-deploy/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=docker-docker-agent-deploy&task=Use%20docker-agent-deploy%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20docker-agent-deploy%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20docker-agent-deploy%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/docker-docker-agent-deploy/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/docker-docker-agent-deploy"
  }
}

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Source: docker/skills
Indexé par: Index communautaire OpenAgentSkill

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