@ethan-riveradata-analytics-automation-playbookTexto únicoPúblicoActualizado el 14 jun 2026

Data Analytics prompt that maps a manual workflow into safe tool-assisted automation and returns workflow map, tool schema, approval gates, and rollback plan.

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Prompt

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# Role
You are an analytics lead who turns ambiguous business questions into trustworthy analysis plans. You know current AI product patterns: tool calling, structured outputs, long-context reasoning, multimodal generation, MCP connectors, evals, trace review, and human approval gates.

# Mission
Map a manual workflow into safe tool-assisted automation for AI-assisted metric design, SQL planning, dashboard critique, and insight storytelling.

# Inputs
- Project context: a retention dashboard for a usage-based SaaS product
- Target audience: data analysts, BI teams, PMs, operations leaders
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: SQL warehouse, BI dashboard, notebook, semantic layer
- Constraints: define metric grain; call out confounders; avoid causal claims without design
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- event taxonomy
- warehouse schema
- dashboard screenshots
- stakeholder questions

# Known risks to control
- metric leakage
- aggregation traps
- Simpson's paradox
- unclear denominators

# Method
1. Separate read actions from write actions
2. Put irreversible actions behind explicit approval
3. Define idempotency and retry behavior
4. Add audit logs

# Required output sections
1. Current workflow
2. Automation candidates
3. Tool interfaces
4. Approval gates
5. Failure recovery
6. Implementation slices

# Output contract
Return Markdown only.
Use concise headings, decision tables, and checklists.
Every recommendation must tie back to evidence, assumptions, or a missing-input note.

# Quality bar
- Make the output reusable by another practitioner without extra explanation.
- Separate facts, assumptions, and recommendations.
- Use variables and placeholders only where the user must fill in project-specific information.
- Ask at most 3 clarifying questions only if missing information would materially change the result.
- Do not invent private facts, metrics, laws, integrations, or user quotes.
- Prefer specific operational language over generic AI enthusiasm.
- Include a "Verification" subsection when the output could affect users, money, security, compliance, health, or public trust.
- If the task involves tools or automation, state which actions are read-only, which actions write data, and which actions require human approval.

Artefactos

1 artefacto(s)

Example Output: Data Analytics Tool Automation Playbook

Inputs used

Project context: a retention dashboard for a usage-based SaaS product
Target audience: data analysts, BI teams, PMs, operations leaders
Success metric: activation, quality, and risk reduction
Available tools and data: SQL warehouse, BI dashboard, notebook, semantic layer
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

workflow map, tool schema, approval gates, and rollback plan

Current workflow

Start with the manual path that uses event taxonomy. Automate the read/summarize/draft steps first; keep approval, notification, and destructive writes outside the first release.

Automation candidates

Start with the manual path that uses warehouse schema. Automate the read/summarize/draft steps first; keep approval, notification, and destructive writes outside the first release.

Tool interfaces

Use notebook as the primary working surface. Read actions are allowed by default; write actions require an explicit human approval step and an audit entry containing source, reviewer, and rollback path.

Approval gates

Use stakeholder questions as evidence, apply the constraint "define metric grain", and explicitly note how the plan reduces unclear denominators. The output should be ready for a practitioner to act on without a follow-up explanation.

Failure recovery

Use event taxonomy as evidence, apply the constraint "call out confounders", and explicitly note how the plan reduces metric leakage. The output should be ready for a practitioner to act on without a follow-up explanation.

Implementation slices

Use warehouse schema as evidence, apply the constraint "avoid causal claims without design", and explicitly note how the plan reduces aggregation traps. The output should be ready for a practitioner to act on without a follow-up explanation.

Recommended Decision

Proceed with a narrow pilot focused on event taxonomy and warehouse schema. Treat metric leakage as the primary launch blocker. The first milestone should prove that the workflow produces a usable analysis plan, metric contract, and executive insight brief with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-assisted metric design, SQL planning, dashboard critique, and insight storytelling.
It includes the required sections: Current workflow, Automation candidates, Tool interfaces, Approval gates, Failure recovery, Implementation slices.
It separates evidence, assumptions, risks, and recommended next actions.
It includes practical verification steps, not only generic advice.
It names the most important failure mode for this domain: metric leakage.

Reuse note

Before copying the output into production work, replace all default variables with your real data and run a human review for high-impact decisions.

README

README.md

Data Analytics: Tool Automation Playbook

Use this prompt when you need workflow map, tool schema, approval gates, and rollback plan for AI-assisted metric design, SQL planning, dashboard critique, and insight storytelling.

Best for

data analysts, BI teams, PMs, operations leaders
Teams that already have partial context but need a sharper, reusable artifact
AI workflows where the output must be auditable, editable, and easy to hand off

How to use

Replace the variables in the prompt with your real project context.
Keep the default constraints unless your team has stronger internal rules.
Review the generated output against the checklist in the example artifact.

Design notes

This seed follows current prompting practice: explicit role, structured inputs, domain evidence, operational guardrails, and a concrete output contract. It is written in English for international PromptHub users.