Prompt

Preview

# Role
You are a staff engineer who designs reliable coding-agent workflows, tests, and rollout gates. 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
Turn analytics questions into a decision-grade data product for AI-native software delivery with coding agents, CI automation, and repo governance.

# Inputs
- Project context: a Codex-powered triage and implementation workflow for a TypeScript monorepo
- Target audience: staff engineers, engineering managers, platform teams
- Success metric: activation, quality, revenue, risk reduction, or cycle time
- Available tools and data: GitHub, CI logs, code search, unit tests, MCP repo tools
- Constraints: small reversible changes; tests before implementation; clear ownership boundaries
- Desired depth: Fast triage
- Output tone: Clear operator memo

# Domain signals to inspect
- repository structure
- pull request history
- CI logs
- architecture decision records

# Known risks to control
- hallucinated APIs
- weak tests
- hidden coupling
- unsafe repository writes

# Method
1. Define grain and denominator
2. List confounders and caveats
3. Prioritize actionability over chart volume
4. Write one recommendation with confidence level

# Required output sections
1. Decision to support
2. Metric contract
3. Data sources
4. Analysis method
5. Dashboard layout
6. Decision memo

# 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.

Artifacts

1 artifacts

Example Output: Agentic Engineering Data Product Brief

Inputs used

Project context: a Codex-powered triage and implementation workflow for a TypeScript monorepo
Target audience: staff engineers, engineering managers, platform teams
Success metric: activation, quality, and risk reduction
Available tools and data: GitHub, CI logs, code search, unit tests, MCP repo tools
Desired depth: Production-ready
Output tone: Clear operator memo

Generated Result

metric contract, analysis plan, dashboard outline, and decision narrative

Decision to support

Use repository structure as evidence, apply the constraint "small reversible changes", and explicitly note how the plan reduces hallucinated APIs. The output should be ready for a practitioner to act on without a follow-up explanation.

Metric contract

Define the metric grain before analysis. For a Codex-powered triage and implementation workflow for a TypeScript monorepo, the first dashboard view should show cohort, denominator, time window, and confidence note, not just top-line movement.

Data sources

Rank sources by authority before retrieval. Chunk around task intent rather than page boundaries, and require every answer to cite the exact source segment used for CI logs.

Analysis method

Define the metric grain before analysis. For a Codex-powered triage and implementation workflow for a TypeScript monorepo, the first dashboard view should show cohort, denominator, time window, and confidence note, not just top-line movement.

Dashboard layout

Define the metric grain before analysis. For a Codex-powered triage and implementation workflow for a TypeScript monorepo, the first dashboard view should show cohort, denominator, time window, and confidence note, not just top-line movement.

Decision memo

Use pull request history as evidence, apply the constraint "clear ownership boundaries", and explicitly note how the plan reduces weak tests. 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 repository structure and pull request history. Treat hallucinated APIs as the primary launch blocker. The first milestone should prove that the workflow produces a usable implementation plan, eval rubric, and release checklist with clear evidence, named owners, and a review path for ambiguous cases.

Expected quality checks

The result is specific to AI-native software delivery with coding agents, CI automation, and repo governance.
It includes the required sections: Decision to support, Metric contract, Data sources, Analysis method, Dashboard layout, Decision memo.
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: hallucinated APIs.

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

Agentic Engineering: Data Product Brief

Use this prompt when you need metric contract, analysis plan, dashboard outline, and decision narrative for AI-native software delivery with coding agents, CI automation, and repo governance.

Best for

staff engineers, engineering managers, platform teams
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.