Api warehouse

api-warehouse — Full API Ingestion Orchestrator

Security Rule

Secrets (API tokens, credentials, connection strings) must travel via environment variables, never typed into chat. Reference them by name (e.g. MY_API_TOKEN) and store values in .env or a secrets manager. See references/security.md for the full policy.

Scope Guard

RAW LANDING ONLY. This pipeline writes raw API responses to the destination unchanged. No transformation, renaming, or modelling happens here. Downstream work (dbt, etc.) is out of scope.

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The Loop

Five step-skills run in order:

connect → assess → [CHECKPOINT] → land → validate → schedule

This orchestrator calls each step in sequence and pauses at the checkpoint after assess so the user can review findings and confirm scope before any data moves. Each step runs its own eval loop per ../../references/running-evals.md. After all steps complete, run this skill's own EVALS.md and append any notable run quirks to MEMORY.md. Apply the prose standards in ../../references/anti-slop.md to any generated summaries.

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Step 1 — connect

Run the connect skill to verify credentials and destination reachability.

Invoke: /connect

Tests that the API token is valid, the base URL responds, and the destination warehouse accepts writes. No data is moved. The step ends with a pass/fail status for each check. Do not proceed until all checks pass.

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Step 2 — assess

Run the assess skill against the API documentation.

Invoke: /assess

Reads the API docs, lists endpoints, determines pagination style, checks incremental support, fetches real sample rows, writes endpoints.json, and renders assessment.html.

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CHECKPOINT — Review Before Landing

After assess completes, stop and tell the user:

assessment.html is ready. Open it, review the endpoint list, incremental verdict, and estimated volume. Confirm scope before we proceed to land — share it with your client for sign-off if needed.

Do not run land until the user explicitly confirms scope. This is where misunderstandings about which endpoints to include, key fields, or load strategy (full-replace vs incremental) get caught.

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Step 3 — land

Once the user confirms scope, run the land skill.

Invoke: /land

Reads endpoints.json, calls build_rest_api_config to build the dlt pipeline config, runs the pipeline to fetch all in-scope endpoints, and writes raw rows to the destination as configured in references/destinations.md. Raw landing only — no transformation.

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Step 4 — validate

Run the validate skill immediately after land completes.

Invoke: /validate

Calls render_validation to check row counts, schema presence, and freshness; produces a validation report; notes any checks skipped because they could not be automated. Fix any failures before scheduling.

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Step 5 — schedule

Once validation passes, run the schedule skill.

Invoke: /schedule

Packages the pipeline into a container image using the project Dockerfile, generates a platform-specific deploy manifest (Cloud Run, Azure Container Apps, or AWS ECS), and registers the cron schedule. After this step, follow deploy/<platform>.md to run the pipeline on the agreed cadence yourself — v1 produces a deploy-ready bundle, it does not auto-deploy.

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Quick Reference

Step	Skill	Key output
1	connect	credential + destination health
2	assess	endpoints.json, assessment.html
CHECKPOINT	—	user/client sign-off
3	land	raw rows in warehouse
4	validate	validation report
5	schedule	deployed, scheduled container