docker-compose-skill
Local dev environments with Docker Compose - multi-service setups, databases, hot reload, debugging. Use when: docker compose, local dev, postgres container, redis local, dev environment.
What this skill does
<objective>
Set up and manage local multi-service development environments using Docker Compose. Provides compose.yml templates, health checks, hot reload, and essential commands for PostgreSQL, Redis, MongoDB, and other services.
</objective>
<quick_start>
1. Copy the compose.yml template below for your stack (Postgres, Redis, etc.)
2. Create a `.env` file with database credentials
3. Run `docker compose up -d` to start services
4. Use `docker compose logs -f` to monitor
</quick_start>
<success_criteria>
- All services start with `docker compose up -d` and reach healthy state
- Health checks configured for every database/cache service
- Environment variables externalized to `.env` (no hardcoded secrets in compose.yml)
- Hot reload working for application code via volume mounts
- `docker compose down -v` cleanly removes all containers and volumes
</success_criteria>
# Docker Compose Skill
Local development environments using Docker Compose for multi-service setups.
## Quick Start
### Common Services
| Service | Image | Default Port |
|---------|-------|--------------|
| PostgreSQL | `postgres:16-alpine` | 5432 |
| Redis | `redis:7-alpine` | 6379 |
| MongoDB | `mongo:7` | 27017 |
| MySQL | `mysql:8` | 3306 |
### Basic compose.yml
```yaml
services:
db:
image: postgres:16-alpine
environment:
POSTGRES_USER: ${DB_USER:-app}
POSTGRES_PASSWORD: ${DB_PASSWORD:-secret}
POSTGRES_DB: ${DB_NAME:-app_dev}
ports:
- "5432:5432"
volumes:
- postgres_data:/var/lib/postgresql/data
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ${DB_USER:-app}"]
interval: 5s
timeout: 5s
retries: 5
redis:
image: redis:7-alpine
ports:
- "6379:6379"
volumes:
- redis_data:/data
volumes:
postgres_data:
redis_data:
```
## Essential Commands
```bash
# Start services (detached)
docker compose up -d
# Start with logs visible
docker compose up
# View logs
docker compose logs -f [service]
# Shell into container
docker compose exec db psql -U app
# Stop and remove containers
docker compose down
# Stop and remove volumes (full reset)
docker compose down -v
# Rebuild without cache
docker compose build --no-cache
```
## Environment Variables
Create `.env` file in project root:
```bash
# .env
DB_USER=app
DB_PASSWORD=secret
DB_NAME=myapp_dev
REDIS_URL=redis://localhost:6379
```
Reference in compose.yml:
```yaml
environment:
POSTGRES_USER: ${DB_USER:-app}
```
## Health Checks
Always add health checks for service dependencies:
```yaml
services:
db:
healthcheck:
test: ["CMD-SHELL", "pg_isready -U ${DB_USER:-app}"]
interval: 5s
timeout: 5s
retries: 5
app:
depends_on:
db:
condition: service_healthy
```
## Hot Reload Setup
Mount source code for development:
```yaml
services:
app:
build: .
volumes:
- .:/app # Source code
- /app/node_modules # Preserve node_modules
environment:
- NODE_ENV=development
```
## Profiles for Optional Services
```yaml
services:
mailhog:
image: mailhog/mailhog
profiles: ["mail"]
ports:
- "8025:8025"
# Start with: docker compose --profile mail up
```
## Reference Files
- `reference/compose-patterns.md` - Common compose file patterns
- `reference/services.md` - Database, cache, queue service configs
- `reference/networking.md` - Ports, networks, volumes
- `reference/dev-workflow.md` - Development workflow commands
## Emit Outcome Sidecar
As the final step, write to `~/.claude/skill-analytics/last-outcome-docker-compose.json`:
```json
{"ts":"[UTC ISO8601]","skill":"docker-compose","version":"1.0.0","variant":"default",
"status":"[success|partial|error]","runtime_ms":[estimated ms from start],
"metrics":{"services_configured":[n],"containers_running":[n]},
"error":null,"session_id":"[YYYY-MM-DD]"}
```
Use status "partial" if some stages failed but results were produced. Use "error" only if no output was generated.
Related 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.