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Digitalocean Agent Skill

Use when deploying or operating a workload on DigitalOcean — Droplet vs App Platform vs Functions, doctl, app spec YAML, Managed Postgres/MySQL/Valkey on the VPC, S3-compatible Spaces + CDN. NOT host-agnostic CI/CD or rollback strategy (that is `deployment`), NOT a bare Hetzner VPS (that is `hetzner`), NOT another managed PaaS (that is `railway`).

6k tokens
context cost
the whole folder, loaded on every use
6
files
ships runnable scripts
0
copies elsewhere
how many repositories repackaged it
105
stars on the repo
on the repository, not the skill itself

Install

one command, takes just this skill from the repository
npx skills add https://github.com/ericrisco/rsc-harness --skill digitalocean

What comes with it

13 777 bytes besides the instruction
evals/README.md
evals/cases.yaml
references/app-spec.md
references/droplet-ops.md
scripts/verify.sh

The instruction itself

9 sections, as written by the author

DigitalOcean — Droplet vs App Platform, doctl, Managed DBs, Spaces

You own one decision: where on DO does this run, and how do I ship it. Host-agnostic

CI/CD, release gating and rollback strategy are ../deployment/SKILL.md; authoring the

Dockerfile is docker; DNS records and the registrar are domains-dns; a bare Hetzner box

and its economics are hetzner. Pick the compute model first, then wire data and storage,

then the ops that bite in production.

workload → [Droplet | App Platform | Functions] → Managed DB (VPC private host) → Spaces (S3 + CDN)
              raw VPS    managed PaaS   event fn        Postgres/MySQL/Valkey         object store

The core decision: Droplet vs App Platform vs Functions

Settle this before writing a single command. Most web apps want App Platform; reach for a

Droplet only when you need the box itself.

| Axis | Droplet (VPS) | App Platform (PaaS) | Functions |

| --- | --- | --- | --- |

| Control | Full root, any daemon, any port | Build+run only, no SSH | Per-invocation, no host |

| Ops burden | You patch/secure/restart it | DO runs it, auto TLS, auto deploy | Zero infra |

| Price floor | Per-second billing since 2026-01-01, min 60s or $0.01 | 3 static sites free; dynamic from $5/mo per service | Pay per call |

| Scaling | Resize/clone/load-balance yourself | Set instance count + size, autoscale | Implicit |

| Best for | Stateful daemons, cron hosts, custom networking, "give me a Linux box" | Web service / API / worker / static site from a repo | Event glue, webhooks |

Rules of thumb:

  • Stateless web service or API from a Git repo → App Platform. It builds, deploys, gives

you TLS and a URL, and re-deploys on push. No box to patch. **Static frontend → App

Platform static site (free, up to 3)**; don't run a Droplet to serve HTML.

  • You need root, a long-lived stateful daemon, custom ports, or a cron host → Droplet.
  • A managed Postgres/MySQL/Valkey → always the Managed Database product, never a DB you

hand-install on a Droplet, unless you have a hard reason.

doctl setup

doctl is the official DO CLI; it drives everything below.

# macOS
brew install doctl
# Linux: download the release tarball from github.com/digitalocean/doctl/releases, then:
#   tar xf doctl-*.tar.gz && sudo mv doctl /usr/local/bin

# Authenticate with a token from cloud.digitalocean.com/account/api/tokens
doctl auth init                       # pastes a token, validates, stores a context
doctl auth init --context prod        # a named context per account/env
doctl auth switch --context prod      # switch the active context
doctl account get                     # verify the token works

Never commit the token. It is a full-account credential. Keep it in your shell keychain,

a secret manager, or CI secret — never in the repo, never in an app spec. Why: a leaked

dop_v1_… token lets anyone create/destroy your whole account.

App Platform via app spec

App Platform deploys from an app spec (YAML or JSON). Treat the spec as the source of

truth, version it, and apply it with doctl.

# .do/app.yaml — minimal web service + managed Postgres
name: my-api
region: nyc
services:
  - name: web
    github:
      repo: me/my-api
      branch: main
      deploy_on_push: true
    instance_size_slug: apps-s-1vcpu-1gb   # ~$5/mo basic; sizes go up to dedicated
    instance_count: 1
    http_port: 8080
    envs:
      - key: NODE_ENV
        value: production
        scope: RUN_TIME              # RUN_TIME | BUILD_TIME | RUN_AND_BUILD_TIME
      - key: DATABASE_URL
        value: ${db.DATABASE_URL}    # injected from the managed DB below
        scope: RUN_TIME
      - key: API_SIGNING_KEY
        value: ${API_SIGNING_KEY}
        type: SECRET                 # encrypted at rest; never plaintext
        scope: RUN_TIME
databases:
  - name: db
    engine: PG
    production: true

Lifecycle — validate, then create or update:

doctl apps spec validate .do/app.yaml          # structural lint, no deploy
doctl apps create --spec .do/app.yaml          # first deploy; prints the app id
doctl apps update <app-id> --spec .do/app.yaml # apply changes (and to roll back: re-apply the prior spec)
doctl apps list                                # find the id

Env scoping that matters:

  • type: SECRET encrypts the value at rest and hides it in the dashboard. Use it for every

key, token, password. Plain value: is readable.

  • scope: BUILD_TIME for things only the build needs; RUN_TIME for runtime; don't leak

build-only secrets into the running container.

  • ${db.DATABASE_URL} (and ${db.HOSTNAME}, ${db.PORT}, etc.) are auto-injected when the

service references a databases: entry — you never paste the connection string.

Operate:

doctl apps logs <app-id> --type run --follow     # run | build | deploy
doctl apps logs <app-id> <component> --type run  # one component

Rollback = re-apply the previous spec (git-revert .do/app.yaml and doctl apps update),

or redeploy a prior deployment from the dashboard. There is no magic rollback verb — your

git history of the spec is the rollback mechanism.

For multi-component apps (web + worker + static_site + job + db, health checks, ingress

routes, autoscaling, instance-size table, alerts) see references/app-spec.md.

Droplets

A Droplet is a Linux VPS. Bootstrap it declaratively, lock it down with a *cloud* firewall.

doctl compute droplet create web-1 \
  --region nyc3 --size s-1vcpu-1gb --image ubuntu-24-04-x64 \
  --ssh-keys <fingerprint> \
  --vpc-uuid <vpc-uuid> \
  --user-data-file cloud-init.yaml \
  --wait
  • Cloud firewall, not just ufw. A cloud firewall filters at DO's edge before traffic

reaches the box, and applies to a tag/group of Droplets. Use it as the real perimeter;

host ufw is defense-in-depth, not the only line. Why: a misconfigured ufw after a

reboot still leaves the edge firewall protecting you.

  • Reserved IP is free while assigned to a Droplet (you pay only when it's unassigned).

Assign one so you can re-point it to a replacement Droplet without DNS changes — that's

your failover handle.

  • Snapshots/backups are your restore path; enable automated backups or snapshot before

risky changes.

  • Per-second billing since 2026-01-01 (min 60s or $0.01): short-lived Droplets are cheap

to spin up and destroy, but a powered-off Droplet still bills for storage — destroy, don't

just power off, to stop charges.

Cloud-init recipes, firewall inbound/outbound rule sets, snapshot cadence + restore,

reserved-IP failover, and the VPC + private-DB layout live in references/droplet-ops.md.

Managed Databases

Use the Managed Database product for Postgres/MySQL/Valkey — DO handles patching, failover,

and backups.

  • Provision sizing: managed Postgres starts ~$15/mo single node; HA (primary+standby)

from ~$30/mo; read replicas available for read scaling.

  • Connect over the VPC private host, not the public one. Same-region Droplets and

Managed DBs talk over the VPC with no bandwidth charge and sub-ms latency, and the private

host keeps the DB off the public internet. Add the app's Droplet/App as a trusted source

so only it can connect.

  • Connection pooling (PgBouncer) is built in. Use a pool when many short-lived clients

(serverless, lots of App Platform instances) would otherwise exhaust connections — a

cluster supports up to ~21 pools / up to ~1,000 connections depending on size, and the

pool listens on a separate pool port from the raw DB port. Point your app at the pool

connection string, not the raw one.

  • For schema design, indexing, query tuning, migrations → that's ../postgresdb/SKILL.md,

not here. This skill only provisions and wires the cluster.

Spaces (S3-compatible object storage + CDN)

Spaces is S3-API-compatible object storage with a built-in CDN.

  • $5/mo includes 250 GiB storage + 1 TiB outbound transfer; inbound is free; the CDN is

included at no extra cost across 200+ edge locations.

  • Spaces keys are NOT your API token. Generate a separate Spaces access key + secret;

the doctl/API token does not work for S3 operations.

  • Use any S3 SDK or aws-cli/s3cmd against the regional endpoint:
# boto3 against DO Spaces (nyc3 region/endpoint)
import boto3
s3 = boto3.client(
    "s3",
    endpoint_url="https://nyc3.digitaloceanspaces.com",
    region_name="nyc3",
    aws_access_key_id="<SPACES_KEY>",       # Spaces key, not the DO API token
    aws_secret_access_key="<SPACES_SECRET>",
)
s3.upload_file("photo.jpg", "my-bucket", "photo.jpg", ExtraArgs={"ACL": "public-read"})

Serve public assets through the bucket's CDN edge URL; set CORS on the bucket if a

browser fetches it cross-origin, and a lifecycle rule to expire/transition old objects.

Basic ops + cost gotchas

  • Set alerts on the app/DB (deploy failures, CPU, restart count) so you hear about

trouble before users do. App-level dashboards/alerting *strategy* is ../monitoring.

  • Scale by editing instance_count/instance_size_slug in the spec and re-applying;

capacity *strategy* that's host-agnostic is ../scaling.

  • App Platform data transfer overage is $0.02/GiB, billed separately from Droplet

transfer — a chatty media app can run up a bill; front heavy static assets with Spaces+CDN.

  • An unassigned reserved IP is billed. Release reserved IPs you're not using.

Anti-patterns

| Anti-pattern | Why it bites | Do instead |

| --- | --- | --- |

| Hardcoding a dop_v1_… token or DB password as a plain value: in the app spec | Spec is in git, value is readable, full-account compromise | type: SECRET env, or ${db.*} injection; token only in doctl auth |

| Connecting app→DB over the public host | Public exposure + egress cost + latency | VPC private host + trusted source |

| Running a stateful long-lived daemon as an App Platform service | Components are restartable/stateless; state is lost | Droplet (or a job/worker designed to be stateless) |

| Sizing a dedicated apps-d-2vcpu-4gb (~$78/mo) for a hobby app | Paying enterprise rates for toy traffic | Start apps-s-1vcpu-1gb (~$5) and scale up on real metrics |

| ufw on the Droplet as the only firewall | A bad reboot/config leaves the host open | Cloud firewall at the edge + host ufw as defense-in-depth |

| Hand-installing Postgres on a Droplet "to save money" | You now own patching, backups, failover | Managed Database unless you have a hard reason |

| Powering off a Droplet to stop billing | Powered-off Droplets still bill for storage | Snapshot then destroy |

| Leaving a reserved IP unassigned | It's billed when not attached | Release it |

How to use it

Copy the folder

Take ericrisco/digitalocean from the repository into ~/.claude/skills for personal use, or into .claude/skills inside a project.

Check the name does not clash

The agent identifies a skill by the name field in its header. Two skills with the same name cannot sit side by side — one of them will be ignored.

Install what it needs

The instructions reference brew. Without those the skill loads but fails at the first command.