deploy-platform
Skill41 starsChanged 3 days agoApache-2.0View on GitHub
Deploy this AgentOS using its own provider instructions, configure JWT verification, and verify the public service. Use for first deployment, redeployment, or production configuration changes.
- Reads credentials
We split this skill into the rules it gives an agent
13 rules. 0 are common, 9 are distinctive, and 4 are very specific to this project.
0
Common
Most of the 4 edited versions we read keep these.
9
Distinctive
This author's own choices. Worth a look.
4
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 8
Read AGENTS.md, the README deployment section, and this repository's deploy configuration and scripts.
- Distinctive: line 11
Do not assume every template has deploy scripts or supports the same flags.
- Distinctive: line 15
Never print credentials or the MCP connect secret.
- Distinctive: line 20
Check the required tools and the authenticated provider account.
- Very specific: line 28
Keep production authentication enabled.
- Very specific: line 30
If a sandbox is requested, configure its provider and REPOSITORY in app/sandbox.py using the sandbox setup guide.
- Distinctive: line 37
Never put secret values in source.
- Distinctive: line 42
Use the provider's status/list commands, saved project identifiers, and local configuration to find an existing deployment.
- Distinctive: line 55
If a command fails, inspect existing resources and any partial settings changes before retrying.
- Distinctive: line 65
Do not treat an exit code or missing-key stop as identical across providers.
- Very specific: line 67
Ensure AGENTOS_URL is the public HTTPS URL used by the scheduler and MCP OAuth.
- Very specific: line 68
Keep MCP_CONNECT_SECRET in the production settings for browser MCP connections.
- Distinctive: line 69
Check how this provider clears remote variables.
“Common” is measured: kept by at least 6 in 10 of the 4 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Deploy the Platform
Read AGENTS.md, the README deployment section, and this repository's deploy
configuration and scripts. Use those files to identify the provider and its exact
commands, required CLI versions, environment file, and teardown behavior.
Do not assume every template has deploy scripts or supports the same flags.
For Docker or an existing Kubernetes cluster, follow the repository's Compose or
Helm instructions.
Never print credentials or the MCP connect secret. The JWT verification key is public and may be shared to complete configuration.
1. Prepare
- Check the required tools and the authenticated provider account. Interactive logins belong in the user's terminal.
- Confirm the intended workspace, account, project, region, or cluster context before making changes. Use the inputs required by this repository's commands.
- Set up the project environment if the deploy scripts need it. Use the production
env file documented in the README; create it from
example.envonly if absent. Configure credentials without printing them. Check how the deploy scripts load settings rather than assuming they read.envor exported shell variables. - Keep production authentication enabled. Do not upload
RUNTIME_ENV=dev. ConfigureJWT_VERIFICATION_KEYor aJWT_JWKS_FILEavailable on the server. - If a sandbox is requested, configure its provider and
REPOSITORYinapp/sandbox.pyusing the sandbox setup guide. AgentCore needs an existing runtime, host AWS access, and an S3 checkpoint bucket. SetSANDBOX_ENABLED = Trueand the repository in code; setSANDBOX_PROVIDER=agentcore, runtime ARN and checkpoint bucket/prefix in the hosted environment. Do not attach an API disk or use PostgreSQL for checkpoint archives. Supply GitHub tokens at runtime and optional Bedrock overrides through the provider'senv. Never put secret values in source. - See docs/scaling.md for current scaling guidance.
2. Choose first deployment or update
Use the provider's status/list commands, saved project identifiers, and local configuration to find an existing deployment. A failed health check does not mean its resources are gone. Resolve authentication, permission, and network errors before deciding to create anything. Link an existing project where required.
For a new deployment, explain which resources will be created, their billing, and the actual teardown command and data it deletes. Run the README's first-deploy procedure with the intended account and target explicitly selected. For owned infrastructure, configure its public URL and JWT key before first boot.
For an update, follow the README's code and environment update commands. Read the
scripts to establish their order and whether updating settings also triggers a
redeploy. Use flags such as --no-deploy only when implemented by that script.
If a command fails, inspect existing resources and any partial settings changes
before retrying. Do not create a second project to recover from a failed deploy.
3. Complete authentication and settings
Some providers create a URL before the API can start. Follow this repository's
handoff: use that URL to connect a Live OS at https://os.agno.com/?ref=railway-template, enable token-based
JWT authorization, and obtain the public verification key. Save the full key as a
quoted multiline JWT_VERIFICATION_KEY in the production settings, or configure
JWT_JWKS_FILE. Then use the documented sync/deploy commands to finish.
Do not treat an exit code or missing-key stop as identical across providers.
Ensure AGENTOS_URL is the public HTTPS URL used by the scheduler and MCP OAuth.
Keep MCP_CONNECT_SECRET in the production settings for browser MCP connections.
Check how this provider clears remote variables; removing a local line may not
remove the saved value. Database credential rotation is separate from updating
API settings. Disable a sandbox by setting SANDBOX_ENABLED = False in app/sandbox.py and
redeploying.
4. Verify and hand over
Build submission is not successful deployment. Inspect bounded provider logs and
test the public URL: /health must succeed; unauthenticated /agents requests and
MCP initialization POSTs must reject access. A plain GET /mcp may return public
server-card metadata; it does not test authorization. With MCP OAuth enabled,
verify the POST's Bearer challenge. Exercise an authenticated request with an
existing token, then confirm the UI connects at
os.agno.com.
Model prompts spend calls; report exactly what ran. Coding agent HTTP runs, delegation, and Company Task completion need separate verification before claiming they work.
Provide the URL, verification results, and this repository's commands for code
updates, settings, logs, and teardown. For browser MCP, use <AGENTOS_URL>/mcp and
tell the user to enter MCP_CONNECT_SECRET from the production settings into the
consent page; do not print it. For coding clients, use
uvx agno connect --url <AGENTOS_URL>.
Other versions4 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 4
- agno-agi/agentos-docker★ 9same name
- agno-agi/agentos-render★ 4same name
- agno-agi/agentos-fly★ 2same name
- agno-agi/agentos-helm★ 1same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository7 other files it gives its agents.
- AGENTS.mdAGENTS.md
- create-agentSkill
- create-evalsSkill
- extend-agentSkill
- improveSkill
- review-and-improveSkill
- setup-platformSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

