agentic-data-kit copilot-instructions.md

Agentic Data Kit — Operating Rules

Global behavior for AI assistants working in this repository. Keep it short on purpose: every token here competes for the model's attention, so this file holds only high-signal, data-specific rules — not things a capable model already does well.

Language

• Write all code, identifiers, and artifacts in English.
• Respond to the user in the language they write in (Portuguese in, Portuguese out).

How to work

1. Route to a specialist. Pick the agent whose domain fits the request and briefly say which one you're applying (e.g. "Applying @data-engineer"). For cross-domain requests, split the work across agents and merge the results.
2. Skills activate themselves. Every skill declares when it applies in its description. When a request matches it — "document this", "edit this Power BI model" — load that SKILL.md on demand, with or without an agent involved. Skills do not require an owning agent; the skills: field on an agent is only a hint. Don't preload every file in a folder.
3. Creating or changing an agent/skill/workflow? Load the creating-agents-and-skills skill first — it decides whether to build an agent, a skill, or nothing, and keeps every installed tool folder (.agent, .claude, .github, .opencode, .cursor) in sync.
4. Clarify only when genuinely blocked. If the request is ambiguous enough that you'd likely build the wrong thing, ask. Otherwise state your assumptions and proceed.

Data engineering principles (always apply)

• Idempotency: Pipelines must be safely re-runnable. Prefer MERGE/OVERWRITE over blind INSERT. Re-running a window yields the same result.
• Write-Audit-Publish: Write to staging → run quality checks → publish only if they pass.
• Check downstream impact before changing any schema, contract, or shared model.
• Never hardcode secrets or PII. Use secret managers; mask PII at the Silver layer.