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skeleton-first

authentic

Prove a data/AI/agent workflow end to end on a tiny sample, find cheap wins, then scale in cost-gated steps within budget.

When to use

• A workflow with 2+ stages (ingest -> transform -> model/LLM -> output), or anything that touches paid compute: warehouse credits, LLM tokens, embeddings, API calls.
• New or unfamiliar data, a new tool or SDK, an architecture that has not run in this environment before.
• The user asks to "build X end to end", "set up the pipeline", "process all the documents", "create the models".

When not to use

• Needs a way to measure cost (a ledger, billing view, token counts or query history); without it, projections are estimates and should be labeled as such.
• Small rungs can miss problems that only appear at scale (rate limits, rare record types, skewed partitions); keep the full-run gate and reconcile totals.
• Adds a few minutes and some tokens per task; not worth it for one-off queries, single-file fixes or work already proven at full size.
• Tests written by the same agent can be too easy; use held-out tests or a human spot-check before claiming quality.
• Cost levers listed for Snowflake and dbt are starting points; check your platform's current pricing and features.

Skill page

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