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Get Started Free →Control Coolify Cloud or self-hosted Coolify instances from Codex using the bundled Coolify API MCP server. Use when the user asks to inspect, deploy, create, update, restart, stop, configure, or troubleshoot Coolify applications, services, databases, servers, projects, deployments, environment variables, domains, teams, private keys, GitHub apps, cloud tokens, scheduled tasks, backups, or other Coolify API resources. For connecting the plugin, first-time setup, saved credentials, login, logout,
.claude/skills/hashgraph-online-coolify/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 51% | 0% |
| case-09 | ✗→✓ | ▲ Improved | 180% | 0% |
| case-14 | ✗→✓ | ▲ Improved | 40% | 0% |
| case-20 | ✗→✓ | ▲ Improved | 60% | 0% |
Use this skill when working with Coolify Cloud or a self-hosted Coolify instance.
Assume the plugin may already have a saved local connection. If a tool reports missing token configuration, expired credentials, or a wrong instance, switch to the coolify-setup skill and help the user connect Coolify. Do not ask the user to paste secrets into chat.
Use MCP tools from the coolify server when available. In Codex tool names this normally appears as the mcp__coolify namespace, with the tool names below:
coolify_health: verify instance health and version.coolify_config_status: check saved connection state without revealing secrets.coolify_list: list common resources.coolify_get: inspect one resource.coolify_lifecycle: start, stop, or restart apps, services, and databases.coolify_deploy: trigger deployments by UUID or tag.coolify_deployment_history: list deployment history for one application or all applications.coolify_application_logs: fetch application logs.coolify_envs: manage environment variables.coolify_openapi_search: find less common endpoint details.coolify_request: call any relative /api/v1 endpoint.For endpoints not covered by named tools, call coolify_openapi_search first, then use coolify_request with the path, query, and body shape from the bundled OpenAPI spec.
If tool_search cannot find mcp__coolify or the named Coolify tools even though the plugin was referenced, switch to the coolify-setup skill. Do not simulate Coolify operations from repo-local scripts for marketplace users. A failed thread may have stale plugin capability metadata, so the recovery path is reinstall/update plus a brand-new top-level thread, not a same-thread retry or fork.
coolify_health.coolify_request for /teams/current.project, server, resource, application, service, and database as needed.coolify_deployment_history; do not rely on coolify_list with kind: "deployment" alone.coolify_deployment_history.coolify_deploy.coolify_envs with action: "list" first.action: "create", action: "update", or action: "bulk_update".coolify_request for server validation, domains, backups, scheduled task executions, or specific API endpoints after finding them in OpenAPI./api/v1.Authorization: Bearer <token>./health may be available outside /api/v1; the MCP health tool handles both forms.GET /deployments lists currently running deployments, not all historical deployments.GET /deployments/applications/{uuid}. Use coolify_deployment_history for user-facing deployment inventories.GET /deploy triggers deployment by UUID or tag. Coolify also accepts deployment parameters in other forms, but prefer the tool unless OpenAPI lookup says otherwise.| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 10,847 | 15,028 | +39% | 1 | 1 | 0% | 558 | 1,244 | +123% | 0 | 0 | — |
case-02 | fail→fail | 19,279 | 18,543 | -4% | 1 | 1 | 0% | 608 | 1,678 | +176% | 0 | 0 | — |
case-03 | fail→fail | 15,919 | 18,747 | +18% | 1 | 1 | 0% | 818 | 1,775 | +117% | 0 | 0 | — |
case-04 | fail→fail | 6,040 | 13,762 | +128% | 1 | 1 | 0% | 984 | 1,704 | +73% | 0 | 0 | — |
case-05 | fail→pass | 19,346 | 10,527 | -46% | 1 | 1 | 0% | 2,788 | 1,933 | -31% | 0 | 0 | — |
case-06 | fail→fail | 19,706 | 12,759 | -35% | 1 | 1 | 0% | 613 | 1,959 | +220% | 0 | 0 | — |
case-07 | fail→fail | 8,964 | 21,296 | +138% | 1 | 1 | 0% | 1,608 | 2,980 | +85% | 0 | 0 | — |
case-08 | fail→pass | 8,742 | 10,211 | +17% | 1 | 1 | 0% | 1,265 | 1,914 | +51% | 0 | 0 | — |
case-09 | fail→pass | 10,217 | 6,218 | -39% | 1 | 1 | 0% | 709 | 1,987 | +180% | 0 | 0 | — |
case-10 | fail→fail | 10,508 | 33,521 | +219% | 1 | 1 | 0% | 676 | 1,546 | +129% | 0 | 0 | — |
case-11 | fail→fail | 11,234 | 18,045 | +61% | 1 | 1 | 0% | 839 | 1,847 | +120% | 0 | 0 | — |
case-12 | pass→fail | 4,401 | 7,842 | +78% | 1 | 1 | 0% | 706 | 1,612 | +128% | 0 | 0 | — |
case-13 | fail→fail | 19,454 | 13,725 | -29% | 1 | 1 | 0% | 2,794 | 1,800 | -36% | 0 | 0 | — |
case-14 | fail→pass | 9,997 | 19,272 | +93% | 1 | 1 | 0% | 1,819 | 2,543 | +40% | 0 | 0 | — |
case-15 | pass→pass | 14,493 | 12,878 | -11% | 1 | 1 | 0% | 1,525 | 2,200 | +44% | 0 | 0 | — |
case-16 | fail→fail | 14,234 | 11,417 | -20% | 1 | 1 | 0% | 649 | 2,285 | +252% | 0 | 0 | — |
case-21 | fail→fail | 15,979 | 17,189 | +8% | 1 | 1 | 0% | 1,794 | 1,829 | +2% | 0 | 0 | — |
case-17 | fail→fail | 7,686 | 7,811 | +2% | 1 | 1 | 0% | 1,047 | 1,654 | +58% | 0 | 0 | — |
case-18 | pass→pass | 14,358 | 9,399 | -35% | 1 | 1 | 0% | 1,639 | 1,814 | +11% | 0 | 0 | — |
case-19 | pass→pass | 16,320 | 10,227 | -37% | 1 | 1 | 0% | 1,961 | 1,954 | -0% | 0 | 0 | — |
case-20 | fail→pass | 12,370 | 3,905 | -68% | 1 | 1 | 0% | 1,046 | 1,673 | +60% | 0 | 0 | — |
case-22 | fail→pass | 5,984 | 7,790 | +30% | 1 | 1 | 0% | 934 | 1,553 | +66% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted, and 11 counted toward the lift figure. The other 11 produced results that are not comparable between the two arms, so they are excluded from the headline rather than averaged into it. The headline lift of +23 percentage points is the difference between those two pass rates over the 11 comparable cases. 2 cases got worse with the skill loaded, and they are included in that figure.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.