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Schedule

Skill sdhilip200/api-warehouse/skills/schedule

Point a coding agent at any API's docs → client-ready assessment, sample data, and raw data landed in your warehouse (BigQuery/Snowflake/Postgres/Azure/files). A Claude Code plugin. Raw-landing only, security-first, validated.

Install
npx -y skills add sdhilip200/api-warehouse --skill schedule

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What its author says it does

Copied from the file, not written here

Use whenever the user wants to schedule, deploy, or automate the pipeline — run it on a cron, package it for Cloud Run, Azure Container Apps, or AWS ECS/Fargate, or set up any recurring cloud execution. Trigger even if they don't say "schedule": phrases like "run it every day", "containerize the pipeline", "deploy to the cloud", "automate the pipeline", or "set up a job" all qualify.

SKILL.md

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Schedule Skill

What this skill does (v1)

Produces a deploy-ready bundle — a Dockerfile, a .dockerignore, a starter requirements.txt, and platform-specific deployment instructions — inside a deploy/ directory the user can inspect and run themselves.

v1 does not auto-deploy. You run the commands. Auto-deploy is planned for v2.

Before writing any files, read MEMORY.md for platform-specific quirks that have caught users before.

Step 1 — Ask the user which platform and cron schedule

Ask both questions in one message:

Which cloud platform would you like to deploy to?

  1. Google Cloud Run (Cloud Run Job + Cloud Scheduler)
  2. Azure Container Apps (Job with cron trigger)
  3. AWS ECS (Fargate scheduled task + EventBridge)

What cron schedule would you like? (e.g. "0 6 * * *" for 6 AM UTC daily)

Do not proceed until you have both answers.

Step 2 — Assemble the deploy bundle

Create a deploy/ directory with these four files:

  1. deploy/Dockerfile — copy from templates/Dockerfile.
  2. deploy/.dockerignore — copy from templates/.dockerignore. This file is required: without it, COPY . . in the Dockerfile copies .env and any local credential files into the image layer. Never omit it.
  3. deploy/requirements.txt — list dlt and requests as a baseline, then add any packages the user's pipeline.py imports.
  4. deploy/<platform>.md — copy from templates/deploy/<platform>.md, then substitute the user's cron expression for CRON_SCHEDULE and fill in any project or account IDs the user has provided.

Secrets (API keys, database URLs, credentials) go into the platform's managed secret store — Cloud Secret Manager, Azure Key Vault, or AWS Secrets Manager. They are injected as environment variables at container start. Nothing secret belongs in the Dockerfile, the image, or any file committed to the repo.

Step 3 — Confirm and summarise

Tell the user:

Your deploy bundle is ready in deploy/.

Next steps (run these yourself):

  1. Check deploy/requirements.txt and add any missing packages.
  2. Follow deploy/<platform>.md to build, push, and schedule the container.

Secrets are injected at runtime via your platform's secret manager — nothing sensitive is in the image.

Self-check

After producing the bundle, spin up a grader agent with a clean context. Give it EVALS.md and the contents of deploy/, and nothing else. Run the eval loop described in ../../references/running-evals.md. Fix any fail verdicts before reporting done to the user. On platforms without subagents (e.g. Codex), run the same checklist inline in a fresh reasoning pass instead — see ../../references/running-evals.md.

For user-facing text in the bundle and instructions, apply the checks in ../../references/anti-slop.md: cut filler verbs, hedging openers, and any sentence that would read identically in a different product's docs.

Platform quirks from past runs are in MEMORY.md — check it before writing platform-specific instructions.

Keep looking

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