Agami deploy

agami deploy — prepare a self-host bundle the user ships to their own host

You are preparing a deploy bundle on the user's machine so they can stand up the multi-user agami server (the HTTP MCP server with OAuth + admin) that their team's Claude connects to. The bundle pulls the published image (ghcr.io/agamiai/agami-core) — there is no repo to clone and nothing to build. You gather a few inputs, write the bundle locally, and hand off the cloud steps you can't do.

The local mirror of this (single-player, no network, no auth) is agami-serve — if the user only wants agami in their own Claude Desktop, point them there instead.

HARD RULES (load-bearing — a deploy handles secrets)

1. Never ask for the admin password (or any secret) in chat — not even temporarily. The password is typed by the user directly into the agami.env file (Phase 2 hand-off), exactly like agami-connect does for DB credentials. You never see it.
2. Never put a secret on a Bash command line. prepare_deploy.py takes only non-secret values; deploy_preflight generates the signing secret into the file. Hosts render Bash calls in chat.
3. Username/password is the only auth this skill sets up. Do not collect Google/Microsoft client id/secret. Social login ships free but is a manual agami.env step — point the user at the in-repo deploy README if they ask, and move on.
4. No signup, no license key, no LLM/embedding key. None are required; don't ask for any.

Conversation style

Tight and oriented. Print one-line progress markers (✓ Bundle written to …, ✓ agami.env validated). Be honest about what's the user's clicks (provision the VM, point DNS) vs what you automate.

Phase −1: Plan-mode preflight

Run the detection logic from shared/plan-mode-check.md. This skill writes files and may run Docker. If plan mode is active, refuse with: "I can't prepare a deploy bundle in plan mode — it writes the bundle + your agami.env and may run Docker. Switch to Auto or Edit Automatically mode (Shift+Tab) and re-invoke me." DO NOT write a plan file or call ExitPlanMode.

Phase 0: Preflight

1. Resolve the environment — python3 "$AGAMI_PLUGIN_ROOT/scripts/connect_resolve.py" prints JSON; read data.artifacts_dir (the local model dir) and data.interpreter.python3 (call it $PY — the interpreter that has the agami-core package; use it for deploy_preflight).
2. Model present — <artifacts_dir>/<active_profile>/datasource.yaml must exist. If there's no model yet, stop and invoke /agami-connect first — the deployed server has nothing to serve without one.

Phase 1: Gather the hard floor, then write the bundle

Ask only these (everything else is defaulted or generated). Prefer one compact exchange:

1. Hostname — "What address will your team connect to?" It must be a hostname, not a bare IP (TLS + OAuth need a DNS name). → PUBLIC_BASE_URL=https://<host>.
   • If they have no domain / can't open ports, offer the Cloudflare tunnel path (profiles bundled-db,tunnel; they'll add CLOUDFLARE_TUNNEL_TOKEN to agami.env). The tunnel still needs a domain on Cloudflare — it removes the public IP, not the name.
2. Admin — first name, last name, work email (the email is the admin identity).
3. (only if they bring their own Postgres) note it → use profiles edge (drops the bundled DB). The managed postgresql://… URL is a credential, so do not collect it in chat — after the bundle is written, the user sets APP_DATABASE_URL in agami.env themselves (the same hand-off as the password).
4. Which datasource(s)? — list the models in the artifacts dir (one per profile: <artifacts_dir>/<profile>/datasource.yaml). If there's exactly one, use it silently. If there's more than one, ask: "You have N datasources — <names>. Deploy all, or pick?" Pass the chosen set as --datasources a,b (omit to deploy all). The server serves every datasource you stage, and each needs its own DSN (Phase 2).

Confirm where to write the bundle. Ask: "Where should I put the deploy bundle? (default ~/agami-deploy)" and use their answer as --target. It must not be inside the artifacts dir (prepare_deploy rejects that — it copies the model out of artifacts into the bundle). Then write it:

python3 "$AGAMI_PLUGIN_ROOT/scripts/prepare_deploy.py" \
  --target <chosen-dir, default ~/agami-deploy> \
  --artifacts-dir "<data.artifacts_dir>" \
  --public-base-url "https://<host>" \
  --admin-email "<email>" --admin-first "<first>" --admin-last "<last>" \
  --profiles "bundled-db,edge"

Append these flags to the command when they apply (add each as another \-continued line): --datasources "a,b" to stage a subset of models; on a version upgrade --image-tag "<version>" to bump it (omit it on a model-only re-stage so an existing pin isn't changed).

(Use --profiles "bundled-db,tunnel" for the tunnel, or --profiles "edge" for managed Postgres — then have the user set APP_DATABASE_URL in agami.env by hand, never on the command line.)

Read the status line and branch on the first token:

• PREPARED <dir> — a fresh bundle. Go to Phase 2 (fill the secrets).
• UPGRADED <dir> new_keys=<a,b,…> — an existing bundle upgraded in place: every value the user typed (password, secret, DSN) is kept. If new_keys is non-empty, this version added settings — tell the user exactly which to set (e.g. "this version added DATASOURCE_URL — set it in agami.env before we restart"). If it's empty, nothing new is needed. Existing secrets are already there — don't re-ask; continue to Phase 3 (deploy).

Phase 2: Hand off the secrets (then end the turn)

Open the file for them so they don't have to hunt for it (it's a plain visible file, agami.env, in the bundle dir): open -t -- "<target>/agami.env" on macOS (opens it in the default text editor; the -- stops a path that starts with - being read as a flag); on other platforms just print the absolute path. Then tell the user (do not proceed past this in the same turn). These are credentials — the user types them by hand; you never see them, they stay in the file on their machine:

Open <target>/agami.env (I just opened it for you) and set, then save:

• AGAMI_ADMIN_PASSWORD= — a strong admin password.
• the warehouse DSN(s) — the connection string(s) the model queries (the scheme picks the type: postgresql:// mysql:// redshift:// snowflake://…). (These live here now — not shipped in the bundle.) Name them per the datasource(s) you deployed:
  • one datasource → DATASOURCE_URL= (e.g. postgresql://<user>:<password>@host:5432/db).
  • several → one per datasource: DATASOURCE_URL__<TOKEN>=, where <TOKEN> is the datasource id upper-cased with every non-alphanumeric char turned to _ (so sales-pg → DATASOURCE_URL__SALES_PG). (List the exact var names for the datasources they chose in Phase 1.4.) agami only runs read-only SELECTs, so the warehouse user only needs read access — a read-only user is safest. Ask for "the read-only grant" and I'll hand you the SQL (shared/readonly-grants.md).
• (only if you chose managed Postgres) APP_DATABASE_URL= — your Postgres URL.

Then tell me to continue.

End the turn here. The user fills the secrets and re-invokes (or says "continue").

Phase 3: Finalize + deploy

1. Validate + generate the signing secret: $PY -m deploy_preflight ~/agami-deploy/agami.env. If it reports missing inputs (e.g. the password still blank, or a non-https URL), relay them and stop. On success it has written AGAMI_SIGNING_SECRET + derived AGAMI_PUBLIC_HOST into the file.
2. Bring it up:
   • Docker present here (the user is on the target host, or testing locally) — run cd ~/agami-deploy && ./deploy.sh (pulls the image + docker compose up -d).
   • No Docker / deploying to a remote VM — hand off: have them copy ~/agami-deploy to the host (scp -r or a synced folder), then run ./deploy.sh there. Give them the cloud checklist below.
3. Print the share lines: the connector URL <PUBLIC_BASE_URL>/mcp (what teammates add in claude.ai → Connectors), and the admin console <PUBLIC_BASE_URL>/admin.

The cloud steps you can't do (👤 — walk them through it)

• A VM (~2 vCPU / 4 GB RAM / 20 GB disk), Ubuntu LTS, only ports 80 + 443 open (or the tunnel).
• DNS: an A-record for their hostname → the VM's public IP. (Skip for the tunnel.)
• Docker on the host: curl -fsSL https://get.docker.com | sudo sh.
• Then ./deploy.sh on the host → wait ~30s for Caddy to issue the cert → open <PUBLIC_BASE_URL>/admin.

Re-running later (model update vs version upgrade)

A re-run of /agami-deploy over an existing bundle is non-destructive — it never touches the secrets the user typed (UPGRADED status); it re-stages the model, appends any settings new in this version, and reports them as new_keys. Two cases:

• Model update (they changed the semantic model): re-run without --image-tag (keeps the pinned version), then on the host docker compose restart agami — the server re-ingests the model on boot. No rebuild, no DB access.
• Version upgrade (a newer agami release): re-run with --image-tag "<version>" (bumps it), set any new_keys the run reports, then cd <target> && ./deploy.sh (pulls the new image + recreates).

Notes

• This is the team path. For a quick local feel of the same tools, that's agami-serve (stdio, no network). For a fully managed, governed, always-on server, that's the hosted product — see docs/open-vs-hosted.md.
• The bundle is self-contained and re-shippable: the generated signing secret lives in its agami.env, so a VM rebuild that re-uses the same bundle keeps every connected Claude working (no reconnect).