artifact-deploy

KiroCrew One-Click Deploy (MVP)

Shipped by the Artifact Deploy app (apps/builtins/deploy_web/). Installing the app activates this skill -- that's how the fullstack deploy capability is distributed. The app page (sidebar -> Artifact Deploy) owns AWS profile setup, verification, and the fleet/cost view; this skill is the deploy action.

AWS config -- resolve the profile from the app's registry, don't ask

The Artifact Deploy app owns the AWS configuration as a multi-profile registry at ~/.kiro/crew/deploy/profiles.json ({"profiles": [{"name", "region", ...}], "default": "<name>"}). Resolve the deploy profile in this order, before asking the user anything:

1. User picked one: if the deploy request names a profile (the artifact card's dropdown injects Use the AWS profile "<name>".), use that entry's name/region from the registry. If the name is not in the registry, stop and send the user to the app page to register it -- never deploy with an unregistered profile name.
2. Registry default: otherwise use the entry named by default.
3. Legacy fallback: if profiles.json doesn't exist, read the old ~/.kiro/crew/deploy/config.json (legacy: ~/.kiro/crew/apps/deploy-web/data/config.json) ({"profile", "region"}).
4. Unconfigured (no registry, no legacy profile): do NOT walk the user through manual profile setup in chat -- link them to the app page (sidebar -> Artifact Deploy), which has the Profiles control plane (register / create + Verify access + IAM policy generator). Resume once a profile exists.

After a successful deploy, back-fill webapp_metadata.deploy_target.profile with the profile NAME actually used, so the fleet table and the artifact's control card show which identity owns the deployment.

This replaces the old "pick the AWS profile" step: profiles are managed once in the app, then every deploy (static publish or fullstack webapp) reuses them. Optionally confirm reachability via the app's verify endpoint (POST /api/deploy/verify with {"profile": "<name>"} -- an STS read, no credential access).

Deploy a pre-built static site (and later, an app with a backend) into the user's own AWS account, served globally over HTTPS via CloudFront.

Where this fits — app artifacts come first

An app-style artifact (kind="webapp") is generated first, as the primary object: every generated app is saved as an artifact up front (initially not deployed). Deploy is an optional downstream action on that existing artifact, offered two ways — a skill (in-conversation) and a Deploy button on the artifact card. This skill is that deploy action; it does NOT create the artifact. Deploying fills in the artifact's webapp_metadata (deploy target, architecture, cost, TTL, teardown) and flips the card from its not-deployed state (Deploy button) to the deployed control card.

Producer — register at generation time. When you generate a deployable app, create the artifact immediately (before any deploy) via artifact_save(name, content=<one-line human summary>, kind="webapp", webapp_metadata=…). For a not-yet-deployed app: fill architecture (intended tiers) + cost (projected from the model), set lifecycle.status="draft", and leave deploy_target.public_url empty — the card then shows the Deploy button. Filling cost.estimates is REQUIRED (empty estimates render a blank cost area on the card; artifact_save warns when you skip it). For the unit prices, GET /api/deploy/pricing?profile=<name> on the gateway — it returns live AWS Pricing API rates for the profile's region (source: "live") or the fallback table when the API is unreachable; multiply into per-bucket what-if totals (e.g. 1,000 / 100,000 / 1,000,000 views). (The MCP artifact_save tool accepts kind="webapp" + webapp_metadata.) Deploy — via this skill or the card's Deploy button — fills in the rest.

Model — why it's cheap AND instant

• One shared base stack per account (kirocrew-deploy-base): a private S3 bucket + a global CloudFront distribution. Created once (~5-15 min for the first CloudFront propagation — this is the only slow step, ever).
• Each deploy is just an S3 upload under a /<slug>/ prefix + a CloudFront invalidation → seconds. No per-deploy stack, no per-deploy cold-create.
• Cost lives in the user's account and is scale-to-zero (S3 storage + CloudFront requests). Idle ≈ $0. AWS earns the consumption; KiroCrew pays nothing.

Invocation — how this skill is summoned (and where isolation comes from)

Every deploy runs in its own isolated, agent-debuggable context — deploys are long (CloudFront cold-create), fail in ways that need iterative fixing (IAM, boto3 Decimal, framework quirks), and are context-heavy. There are two entry points; the isolation source differs:

• The "Deploy" button (preferred — solves discoverability). Most users won't know this skill exists. After a user has an app, a Deploy button (on the app / app-artifact card / dashboard) opens a fresh session pre-loaded with this skill and a seed prompt. That new session IS the isolated context — run the whole adapt→deploy→debug flow inline in it. Do NOT spawn a subagent here (you are already in a dedicated session; debug errors directly in-session).
• In-conversation ("deploy my app" mid-session). The user asks inside an existing, busy session. Here you MUST run the deploy via a spawn_run subagent — the subagent is the isolation boundary, so the long/noisy deploy and its debugging don't pollute or blow the current session's context.

Rule of thumb: one isolated context per deploy — a fresh session (button) OR a subagent (in-conversation). Never run a deploy inline in a busy existing session.

The button is a launcher, not an executor: it does not run the deploy itself (a button can't debug a failed CloudFormation/IAM step) — it summons a skill-loaded session where the agent can. That is why deploy is a skill, not a button-triggered script.

The deploy contract (target shape)

The deploy scripts consume ONE fixed layout. The skill's real job is to get the user's app into this shape (see Adapter playbook), then ship it:

<app>/public/   static SPA — index.html at root  → S3 + CloudFront
<app>/api/      an HTTP backend                   → API Gateway → Lambda at /<slug>/api/*
                (index.py handler, OR any HTTP server wrapped in a Lambda shim —
                 see playbook; pick language via --runtime)
state           DynamoDB single table (--table), read via os.environ["TABLE_NAME"]

Static-only apps need just public/. No auth in MVP (content is public).

Adapter playbook — wrap, don't rewrite

A user's app almost never arrives in the contract shape. Conform it — but prefer wrapping the app's existing HTTP server in a Lambda adapter over hand-rewriting its routes (hand-rewriting is usually a language port + a data-model rewrite = fragile). Wrapping keeps the user's code and adds a thin shim:

• ASGI (FastAPI / Django / Starlette) → Mangum adapter, --runtime python*.
• Express / Node / Next.js standalone → serverless-http or AWS Lambda Web Adapter, --runtime nodejs*.
• Static SPA → point the build output at public/.
• Don't force Python — deploy-backend.sh --runtime already supports the app's own language.

This broadens "our format" from one Python handler to static assets + any HTTP server + DynamoDB, so the adapter mostly places dirs + adds a shim, not rewrites business logic.

Tiers — what adapts, and where to fail loud

• Tier 1 — mechanical (reliable): static SPA + a simple JSON API. Place dirs, align the handler entry, done.
• Tier 2 — wrap (agent-doable, must test): a framework backend in a supported runtime + stateless or KV-mappable state → wrap in a shim + DynamoDB. Deploy, then exercise the real endpoints (incl. the base64 body path) before handoff.
• Tier 3 — needs redesign (fail loud, don't fake it): SSR needing a live server beyond a single Lambda, websockets, background workers/cron, or a relational schema with joins/transactions. Detect these and tell the user they're unsupported / need a redesign — never silently ship a broken deploy.

Database strategy (a real boundary, not laziness)

• Stateless or KV-mappable → DynamoDB (--table, TABLE_NAME).
• SQL / relational → out of tier. The "$0-idle, cents-per-demo" cost story depends on scale-to-zero DynamoDB. SQL means RDS/Aurora → floor cost + VPC + not scale-to-zero, which breaks the cheap/ephemeral promise. So map state to DynamoDB, or tell the user this app doesn't fit the cheap-ephemeral tier (offer a redesign). Do not silently mangle a relational schema into KV.

Prerequisites

• POSIX platform (Linux / macOS) — deploy scripts require bash. Windows is not supported; use WSL (Windows Subsystem for Linux) to run the KiroCrew gateway if your host OS is Windows. The backend returns HTTP 400 with a clear message on unsupported platforms.
• AWS access configured once in the Artifact Deploy app (profiles registered in ~/.kiro/crew/deploy/profiles.json, verified via the app page). Prefer a least-privilege deploy profile, not admin (see Security).
• The app is conformed to the deploy contract (above) — producing that layout is the skill's job (Greenfield: generate in-contract; Brownfield: run the Adapter playbook). The deploy scripts consume a built public/ (+ optional api/); they don't build or adapt for you, so the agent produces that layout first. Static apps need an index.html at the static root.

Commands (operator-terminal reference)

These are run from this skill's directory by human operators in a terminal, not by agents. Agents deploy via the POST /api/deploy/deploy API (see Agent workflow above). All accept --profile and --region (default us-west-2).

• Deploy (static): scripts/deploy.sh <app_dir> [--slug NAME] [--ttl HOURS] [--profile P] [--region R]
• Deploy (fullstack): scripts/deploy-app.sh <app_dir> --slug NAME [--table] [--wait] [--profile P] [--region R]
• Deploy (backend only): scripts/deploy-backend.sh <handler_dir> --slug NAME [--table] [--wait] [--profile P] [--region R]
• Install reaper: scripts/install-reaper.sh [--rate 'rate(1 hour)'] [--profile P] [--region R]
• Teardown: scripts/teardown.sh <slug> [--profile P] [--region R]
• Lifecycle: scripts/list.sh, scripts/cost.sh [slug], scripts/persist.sh <slug>, scripts/detach_backend.py --slug NAME

Backend handler contract

Copy templates/handler-example.py as your app's api/index.py starting point. Behind an API Gateway HTTP API (payload v2):

• Route on the path after /api/: event["rawPath"].split("/api/",1)[1].
• Decode the body via isBase64Encoded — API Gateway base64-encodes the request body when Content-Type isn't a known text type (or is absent, e.g. curl -d without -H application/json). Calling json.loads(event["body"]) directly will 500 for those callers. base64.b64decode first when event.get("isBase64Encoded"). (Browsers sending application/json are unaffected — a browser-only test won't catch this.)
• Return {"statusCode", "headers", "body": json.dumps(...)}.
• Stateful apps: table name in os.environ["TABLE_NAME"] (deploy with --table).

Agent workflow — deploy via the audited API (MCP-first)

First honor Invocation (above): summoned mid-session → run the steps below inside a spawn_run subagent; launched in a fresh Deploy-button session → run them inline.

When the user asks to deploy / ship / share a demo: 0. Conform the app to the contract (Adapter playbook) — greenfield: generate in-contract; brownfield: wrap the existing server in a Lambda shim. Fail loud on Tier-3 apps rather than shipping something broken.

1. Confirm the resulting app dir has public/ (an index.html at its static root) and, if there's a backend, api/.

2. Resolve the AWS profile from the app registry (see "AWS config" above): user-picked > registry default > legacy config; if unconfigured, send the user to the Artifact Deploy page for the one-time setup. Then confirm which account (verify endpoint returns the account id) -- this provisions REAL resources that cost money. Never guess a prod account; if unsure, ask.

3. Scan the app for internal tokens first (see Security) — this content is going to the public internet.

4. Deploy via the audited API (PRIMARY path — all agent deploys MUST use this):

   POST /api/deploy/deploy
   Body: { "site_id": "<slug>", "artifact_slug": "<slug>" }
   

   For static apps: use artifact_slug — the artifact's rendered HTML is staged and deployed.

   For fullstack apps (with a public/ + api/ layout): use local_dir pointing at the conformed app's public/ directory:

   POST /api/deploy/deploy
   Body: { "site_id": "<slug>", "local_dir": "/path/to/app/public" }
   

   This deploys the static frontend. The backend Lambda (api/) must be attached separately by the operator in their terminal via scripts/deploy-backend.sh — the agent cannot execute this (it requires IAM write, CloudFormation stack creation, and interactive debugging). Tell the user: "Your static site is live. To attach the backend API, run: deploy-backend.sh <app_dir> --slug <slug> --profile <profile>"

   This is a two-call confirm flow:

   • First call (no confirm): returns { "requires_confirm": true, ... } with a preview (size, scan status).
   • If scan-blocked: returns HTTP 409 with { "blocked": true, "reason": "scan", "findings": "...", "count": N }. Show findings to the user. To override: second call with "override_scan": true.
   • Second call ("confirm": true): executes the deploy and returns { "url": "https://...", ... }.

   The API path provides: schema validation, fail-closed scan gate, confirm gate, SEL audit trail, and _deny_restricted session guard. Do NOT bypass it by calling deploy scripts directly.

5. Return the https://<dist>.cloudfront.net/<slug>/ link + the TTL. Important ordering: after the deploy succeeds, immediately back-fill the artifact's webapp_metadata (public_url, lifecycle.status, deploy_target) before performing endpoint verification (HTTP GET on the deployed URL). The endpoint check can timeout (~30s+) or be killed by a session budget wall — if the metadata write happens after it, a timeout leaves the artifact in a stale "draft" state even though the deploy succeeded. Metadata first, verify second.

6. Offer tear down / promote-to-persistent.

MCP deploy_artifact tool (preview-only in MCP-tool-capable sessions)

Agents with MCP tool access use the deploy_artifact tool to get a deploy preview (cost, scan results, size). The tool is preview-only — it never confirms or executes the deploy. Human confirmation happens exclusively in the dashboard UI (Artifact Deploy page), where the user clicks the confirm button.

This design prevents an LLM caller from self-confirming a public deployment.

Parameters:

• site_id (required): deploy slot name
• artifact_slug: slug of a webapp artifact to deploy (renders HTML)
• local_dir: validated path to a static directory (fullstack public/ root)
• profile: AWS profile override (default: registry default)
• ttl_hours: hours until auto-cleanup (default: 72)

Exactly one of artifact_slug or local_dir is required. The tool returns a preview summary; to execute the deploy, the user must confirm via the Artifact Deploy page in the dashboard.

Operator-terminal commands (NOT for agent use)

The following scripts are for human operators running in a terminal — they are the underlying implementation that the API wraps. Agents do NOT call these directly (the API route enforces audit + scan + session guards that scripts cannot).

• scripts/teardown.sh <slug> — destructive; agent-blocked by design
• scripts/install-reaper.sh — one-time account setup; operator-only
• scripts/deploy.sh / scripts/deploy-app.sh / scripts/deploy-backend.sh — the raw deploy scripts; operators may use for debugging or manual deploys
• scripts/list.sh / scripts/cost.sh / scripts/persist.sh / scripts/detach_backend.py — lifecycle management utilities

Security

• Credentials hard rule — the agent NEVER executes credential writes and NEVER reads credentials files. "Configure a profile" means: generate the commands (aws configure --profile X / ada profile add ...) for the USER to run in their terminal, then verify with aws sts get-caller-identity --profile X (a read). After a successful deploy, fill webapp_metadata.deploy_target.profile with the profile NAME (display only — never a credential value).
• Least privilege — never run with admin credentials if a scoped deploy profile exists; the deploy identity needs only S3 + CloudFront + CloudFormation on the managed stack.
• Internal-data leak — before deploying, scan the app dir for internal tokens (internal hostnames, corp identifiers) and refuse on a hit. This is public.
• Private by default — the bucket stays private (CloudFront OAC only); CloudFront adds security headers (nosniff / HSTS / frame-ancestors 'self' + loopback so the dashboard can live-preview the site) and enforces TLS 1.2+ via redirect-to-https.
• No auth — MVP serves static content publicly with no auth. If the app expects a protected backend, warn the user.

Status / Roadmap

• M1 static — DONE, live-validated (S3 + CloudFront OAC + security headers).
• M2 backend — DONE via API Gateway HTTP API → Lambda behind CloudFront /<slug>/api/* (app-apigw.yaml + deploy-backend.sh). Live-validated: /<slug>/ and /<slug>/api/ both 200 on one public link. Chosen over a raw Lambda Function URL because some managed corporate accounts run automated guardrails that auto-mitigate world-accessible Lambda Function URLs (Principal:*). API Gateway keeps the Lambda non-world-accessible (scoped apigateway.amazonaws.com invoke) so no auto-mitigation fires. app-lambda.yaml (Function URL + CloudFront OAC) is kept as the lighter variant for unrestricted accounts.
• M3 lifecycle — DONE: list.sh / persist.sh / detach_backend.py, plus the in-account scheduled reaper (install-reaper.sh → EventBridge-timed Lambda; templates/reaper.yaml + scripts/reaper_lambda/) that deletes expired non-persistent deploys (S3 prefix + CloudFront behavior + backend stack) via a role scoped to kirocrew-deploy-app-*. Runs in-account with no local creds — the reliable mechanism. Local reaper.sh is a dev-only fallback.
• M4 cost — DONE: cost.sh live usage estimate (per-slug S3 exact + shared CloudFront account-wide; honest "estimate not bill" labeling).
• Graduation (next) — the app artifact is generated up front; deploy just fills in its webapp_metadata (target/cost/TTL/teardown) and flips the card from a not-deployed state (Deploy button) to the deployed control card. Build: the Deploy button on the card (launches a skill-loaded session — see Invocation) + fold the deploy engine into a core AwsDeployProvider (the PublishProvider pattern). Card FE needs a not-deployed state + Deploy button.