fly-io
Deploy, configure, and manage applications on the Fly.io platform using flyctl CLI, fly.toml configuration, Fly Machines, Fly Volumes, private networking, secrets, health checks, autoscaling, and GitHub Actions CI/CD. Use when deploying any application to Fly.io, writing or modifying fly.toml configuration, managing Fly Machines or Volumes, configuring networking (public services, private 6PN, Flycast, custom domains, TLS), setting secrets, configuring health checks, setting up autostop/autostart or metrics-based autoscaling, deploying with GitHub Actions, managing Fly Postgres databases, or preparing an app for production on Fly.io.
What this skill does
# Fly.io Deployment & Management
## Core Concepts
- **Fly App**: Named group of Machines (VMs) + config + networking + secrets belonging to one org
- **Fly Machine**: Fast-launching VM (sub-second start). Ephemeral root filesystem. Lifecycle: created -> started -> stopped -> destroyed
- **Fly Volume**: NVMe-backed persistent storage, 1:1 with a Machine, region-pinned, encrypted at rest by default
- **Fly Proxy**: Edge proxy handling TLS termination, load balancing, autostop/autostart, HTTP routing
- **flyctl (fly)**: CLI for all Fly.io operations. Install: `brew install flyctl` or `curl -L https://fly.io/install.sh | sh`
- **fly.toml**: App configuration file. See [references/fly-toml-config.md](references/fly-toml-config.md) for complete reference
## Quick Start Workflow
```bash
# 1. Create app + fly.toml
fly launch
# 2. Set secrets
fly secrets set DATABASE_URL=postgres://... SECRET_KEY=...
# 3. Deploy
fly deploy
# 4. Check status
fly status
fly logs
```
## fly.toml Essentials
Minimal web app configuration:
```toml
app = 'my-app'
primary_region = 'ord'
[build]
dockerfile = "Dockerfile"
[http_service]
internal_port = 8080
force_https = true
auto_stop_machines = "stop"
auto_start_machines = true
min_machines_running = 1
[http_service.concurrency]
type = "requests"
soft_limit = 200
hard_limit = 250
[[vm]]
memory = '512mb'
cpu_kind = 'shared'
cpus = 1
```
For the complete fly.toml reference with all sections, see [references/fly-toml-config.md](references/fly-toml-config.md).
## Machines Management
```bash
# Scale horizontally
fly scale count 3 # 3 Machines in primary region
fly scale count 3 --region ord,iad # Across regions
# Scale vertically
fly scale vm performance-1x --vm-memory 2048
# Direct Machine control
fly machine run <image> --region ord
fly machine clone <machine-id> --region iad
fly machine update <machine-id> --vm-memory 512
fly machine stop <machine-id>
fly machine start <machine-id>
fly machine destroy <machine-id>
```
Machine sizes: `shared-cpu-1x` (256MB default), `shared-cpu-2x`, `shared-cpu-4x`, `shared-cpu-8x`, `performance-1x` through `performance-16x`. GPU options: `a10`, `l40s`, `a100-pcie-40gb`, `a100-sxm4-80gb`.
## Volumes
```bash
fly volumes create mydata --region ord --size 10 # 10GB
fly volumes list
fly volumes extend <vol-id> --size 20 # Extend to 20GB (cannot shrink)
fly volumes snapshots list <vol-id>
fly volumes fork <vol-id> # Copy a volume
```
Configure in fly.toml:
```toml
[mounts]
source = "mydata"
destination = "/data"
initial_size = "10gb"
auto_extend_size_threshold = 80
auto_extend_size_increment = "1GB"
auto_extend_size_limit = "50GB"
```
**Critical**: Volumes are 1:1 with Machines, region-pinned, no automatic replication. Always provision at least 2 Machines with volumes for redundancy. Your app must handle data replication between volumes.
## Secrets
```bash
fly secrets set KEY=value OTHER_KEY=other_value
fly secrets set KEY=value --stage # Stage without restarting
fly secrets deploy # Deploy staged secrets
fly secrets list # Names only, no values
fly secrets unset KEY OTHER_KEY
```
Secrets are encrypted at rest, injected as env vars at boot. Use `[[files]]` in fly.toml to mount secrets as files (value must be base64-encoded):
```toml
[[files]]
guest_path = "/etc/ssl/private/key.pem"
secret_name = "TLS_PRIVATE_KEY"
```
## Logs
```bash
fly logs # Continuous tail (default, streams indefinitely)
fly logs --no-tail # Output recent logs once and exit
fly logs --machine <machine-id> # Filter to a specific machine
fly logs --machine <machine-id> --no-tail # Recent logs for one machine, then exit
fly logs --region iad # Filter by region
fly logs --json # JSON-formatted output
```
**Important**: flyctl logs does NOT support `-n`, `--tail <count>`, or `--lines` flags. To limit output, pipe through standard tools:
```bash
fly logs --no-tail | head -50 # Last 50 lines
fly logs --no-tail | tail -20 # Most recent 20 lines
fly logs --machine <id> --no-tail | grep -i error # Search for errors
```
Available flags:
| Flag | Description |
|---|---|
| `--no-tail` (`-n`) | Output logs once and exit (do not stream) |
| `--machine <id>` | Filter to a specific machine ID |
| `--region <code>` | Filter by region (e.g., `iad`, `ord`) |
| `--json` (`-j`) | JSON output format |
| `--app <name>` (`-a`) | Specify app name (if not in fly.toml directory) |
## Networking
For detailed networking patterns (public services, private networking, Flycast, custom domains, DNS), see [references/networking.md](references/networking.md).
### Quick Reference
```bash
fly ips list # List allocated IPs
fly ips allocate-v6 # Dedicated IPv6 (free)
fly ips allocate-v4 --shared # Shared IPv4 (free)
fly ips allocate-v4 # Dedicated IPv4 (paid)
fly certs create mydomain.com # Add custom domain + TLS cert
```
**Private networking**: All apps in an org share a WireGuard mesh (6PN). Use `<appname>.internal` DNS for inter-app communication. Bind to `fly-local-6pn` or `[::]:port` to accept private connections.
## Health Checks
Service-level checks affect Fly Proxy routing. Top-level `[checks]` are for monitoring only.
```toml
# In [http_service] or [[services]]
[[http_service.checks]]
grace_period = "10s"
interval = "30s"
timeout = "5s"
method = "GET"
path = "/health"
# Top-level (monitoring, no routing impact)
[checks.my_check]
type = "http"
port = 8080
path = "/health"
interval = "30s"
timeout = "5s"
```
## Autoscaling
**Autostop/autostart** (Fly Proxy-based, for web services):
```toml
[http_service]
auto_stop_machines = "stop" # "off", "stop", or "suspend"
auto_start_machines = true
min_machines_running = 1
[http_service.concurrency]
type = "requests"
soft_limit = 200 # Used for excess capacity calculation
```
**Metrics-based autoscaling**: Deploy the autoscaler app in your org. It polls Prometheus metrics and creates/destroys Machines. See Fly.io docs for setup.
## Deployment Strategies
```toml
[deploy]
strategy = "rolling" # "rolling" (default), "immediate", "canary", "bluegreen"
max_unavailable = 0.33 # For rolling: fraction or integer
release_command = "bin/migrate" # One-off command before deploy (e.g., DB migrations)
```
- **rolling**: One-by-one replacement (default)
- **immediate**: All at once, skip health check waits
- **canary**: Boot one Machine, verify health, then rolling (no volumes)
- **bluegreen**: Boot new set alongside old, migrate traffic after health checks (requires health checks, no volumes)
## CI/CD with GitHub Actions
For complete CI/CD setup including deploy tokens, review apps, and multi-app deploys, see [references/cicd-github-actions.md](references/cicd-github-actions.md).
Quick setup:
```yaml
# .github/workflows/fly.yml
name: Fly Deploy
on:
push:
branches: [main]
jobs:
deploy:
runs-on: ubuntu-latest
concurrency: deploy-group
steps:
- uses: actions/checkout@v4
- uses: superfly/flyctl-actions/setup-flyctl@master
- run: flyctl deploy --remote-only
env:
FLY_API_TOKEN: ${{ secrets.FLY_API_TOKEN }}
```
Generate deploy token: `fly tokens create deploy -x 999999h`
## Databases & Storage
For detailed database and storage patterns, see [references/databases-storage.md](references/databases-storage.md).
- **Managed Postgres**: `fly mpg create` -- fully managed, automated backups
- **Tigris Object Storage**: S3-compatible, `fly storage create`
- **Upstash Redis**: Managed Redis, `flRelated in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.