What does this skill do, and when should you use it?
Render Deployment Assistant helps users host applications on Render. It analyzes a codebase for runtime, build and start commands, environment variables, ports, and infrastructure needs, then can generate a render.yaml Blueprint. For simple single-service projects, it can also create services through Render MCP. Prebuilt Docker image services are supported by Render, but direct image-service creation through MCP is not supported.
Checks whether a Git remote exists and whether the repository is pushed; analyzes runtime, build and start commands, environment variables, datastores, and port binding; generates render.yaml Blueprints; runs Render CLI authentication and Blueprint validation when available; creates services and configures environment variables through Render MCP; produces a Render Dashboard Blueprint deeplink; and verifies deployment status, health responses, error logs, metrics, environment variables, and port binding after deployment.
- A developer has pushed a single web application to GitHub, GitLab, or Bitbucket and wants to deploy it to Render.
- A multi-service application needs a version-controlled render.yaml covering web services, workers, cron jobs, or databases.
- A team wants to provision Render databases, Key Value stores, cron jobs, or private services as part of deployment.
- A team has a prebuilt Docker image and needs guidance on Render Dashboard/API deployment or on using a Git-backed Blueprint with runtime: image.
How do you install this skill?
- This is a static source review only; no commands, MCP calls, or real deployment were executed.
- Deployment creates external cloud resources and may incur charges; cost confirmation, permission boundaries, and rollback procedures are not sufficiently specified.
- Reachability of Render, Git providers, and the MCP service may affect mainland-China users; localization and alternative deployment paths are not adequately evidenced.
- The README marks the repository as deprecated, and license metadata is unknown; confirm maintenance ownership and authorization before adoption.
- If the referenced files are unavailable, codebase analysis, Blueprint specification, direct-creation, and troubleshooting workflows may be incomplete.
- Shell / CLI
- Network access
- Local filesystem
- MCP Server
GitRender CLIRender MCP server
The skill is located at skills/.curated/render-deploy/ in the openai/skills repository. In Codex, install it with: $skill-installer render-deploy. Restart Codex after installation. The supplied repository metadata lists the license as unknown; the README only says that an individual skill license should be in its directory's LICENSE.txt.
tmp="$(mktemp -d)"
git clone --depth 1 https://github.com/openai/skills.git "$tmp"
mkdir -p ~/.claude/skills
cp -R "$tmp/skills/.curated/render-deploy" ~/.claude/skills/
rm -rf "$tmp"Generated from the source repository and skill path; it copies only this skill's folder. If the author's install steps above differ, follow those first. To scope it to one project, replace ~/.claude/skills with that project's .claude/skills.
How do you use this skill?
Once installed, send your agent any of these to trigger it:
- Deploy my application to Render.
- Create a render.yaml Blueprint for this project.
Example prompts include: "Deploy my application to Render." and "Create a render.yaml Blueprint for this project." At the start, choose Git repository deployment or a prebuilt Docker image, then clarify whether Render should provision the required infrastructure. Git-based methods require a repository pushed to GitHub, GitLab, or Bitbucket. For Blueprint deployment, generate and commit render.yaml, run render blueprints validate, push the file, and open the Render Dashboard deeplink. For a simple service, configure Render MCP before using Direct Creation.
What are this skill's strengths and limitations?
- Supports both Blueprint and Direct Creation workflows for Render.
- Covers web, worker, cron, static, private services, databases, and Key Value resources.
- Includes guidance for environment variables, secrets, Git remotes, validation, deployment verification, and basic triage.
- Clearly documents the boundary between Git-backed deployment and prebuilt Docker image deployment.
- Git-based workflows require a repository pushed to GitHub, GitLab, or Bitbucket.
- Direct Creation depends on Render MCP, while MCP cannot directly create image-backed services.
- Network access may require escalation, and deployment can take several minutes.
- The supplied source provides no skill-specific license text or test results.
How does this skill compare with similar options?
Side by side with related skills; every score comes from the same FSRS standard.
| Skill | FS score | Stars | Last updated | License |
|---|---|---|---|---|
| Render Deployment Assistant this page ✓ OpenAI · Official | 49 · Use with care | ★ 28k | 4mo ago | — |
| Google Cloud AI Agent Builder ✓ Google · Official | 53 · Use with care | ★ 21k | 11d ago | Apache-2.0 |
| Google Cloud Solution Architect ✓ Google · Official | 57 · Use with care | ★ 21k | 11d ago | Apache-2.0 |
| Google Cloud Operational Excellence Advisor ✓ Google · Official | 53 · Use with care | ★ 21k | 11d ago | Apache-2.0 |
| Azure Deployment Preparation ✓ Microsoft · Official | 45 · Not recommended | ★ 2.7k | 3mo ago | MIT |
The source explicitly contrasts Blueprint and Direct Creation: Blueprint is intended for multi-service applications, datastores, scheduled jobs, workers, and reproducible infrastructure-as-code, while Direct Creation targets simple single-service deployments. Prebuilt Docker image deployment should use the Render Dashboard/API, unless a Git repository containing a runtime: image Blueprint is used.
How did FollowSkills review this skill?
The skill requires choosing a deployment path, confirming the repository source, and marking secrets as sync:false or generateValue, which reduces accidental deployment and hardcoded-secret risk; however, MCP/CLI actions can create services and databases and trigger external deployment, while permission scope, data flow, cost impact, rollback, and granular pre-deployment confirmation are incomplete. API keys are also passed through environment variables and configuration. Deducted 10 points.
The main flow includes prerequisites, validation, logs, health checks, and stop-and-guide behavior for several failures; however, it depends on multiple referenced files not supplied here, does not establish MCP command availability, and inconsistently names the validation command as render blueprints validate versus render blueprint validate. Static evidence cannot demonstrate reproducible key paths. Deducted 12 points.
The audience, Render deployment scenarios, Git/Docker branches, and single-service versus multi-service heuristics are clearly described; however, the skill is non-fit outside Render, gives no Chinese-language support, and provides insufficient evidence about mainland-China reachability or practical alternatives for a workflow dependent on Render and overseas Git providers. Deducted 6 points.
The documentation is layered around prerequisites, deployment methods, validation, and troubleshooting, with templates and examples; however, dependency notes are incomplete, several linked reference files are absent from the supplied material, versioning, changelog, explicit maintenance responsibility, and update path are missing, license metadata is unknown, and the repository README says the repository is deprecated. Deducted 9 points.
The claimed outputs—render.yaml, deployed services, Dashboard deeplinks, configuration guidance, and post-deploy checks—cover the core deployment task in principle; however, templates require project-specific rewriting, static review cannot confirm configuration validity or deployment success, and users must still authenticate, push code, fill secrets, and operate the Dashboard. Awarded the static-review ceiling of 7 and deducted 8 points for these limitations.
The committed instructions, templates, and commands provide some auditable primary material; however, there is no committed test suite, CI coverage, or third-party execution evidence, so Render specification claims, MCP calls, and example validity cannot be independently reproduced from the files alone. Deducted 6 points.
Open a dimension to read why it scored that way
Evidence confidence：Low — Mostly static review, author material or a limited demo; useful for discovery, not high-risk decisions.
See the full review method →