@maya-chenagentic-engineering-video-storyboardTextPublicUpdated Jun 14, 2026

Agentic Engineering prompt that turns a concept into a short-form video prompt and shot list and returns scene-by-scene video prompt, shot timing, motion notes, and edit checklist.

AI Agents Code Review Evals Software Video

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Prompt

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# 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 a concept into a short-form video prompt and shot list 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. Open with a visual hook
2. Keep each shot bounded and inspectable
3. Specify motion continuity
4. Add safety and brand constraints

# Required output sections
1. Narrative hook
2. Shot list
3. Camera and motion
4. On-screen text
5. Sound and pacing
6. Review checklist

# Output contract
Return one final Seedance 2.0 video prompt plus a shot table.
The shot table must include timestamp, frame, camera movement, subject action, on-screen text, and transition notes.
Keep the prompt inspectable: one concrete visual moment per shot, no vague atmosphere-only language.

# 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 Short-Form Video Storyboard

Inputs used

Project context: a Codex-powered triage and implementation workflow for a TypeScript monorepo
Target audience: staff engineers, engineering managers, platform teams
Desired depth: Production-ready

Generated Seedance 2.0 Prompt

Create a 20-second product explainer for a Codex-powered triage and implementation workflow for a TypeScript monorepo. The video opens with a concrete before-state from AI-native software delivery with coding agents, CI automation, and repo governance, then shows the AI workflow turning messy inputs into a clear operator-ready artifact. Keep the visual style grounded, modern, and professional. Use smooth camera movement, readable interface moments, realistic team environments, and no exaggerated science-fiction effects.

Shot Table

| Time | Shot | Motion | On-screen text | Purpose | |---|---|---|---|---| | 0-3s | Wide shot of a team reviewing repository structure | Slow push-in | "From scattered signals..." | Establish the pain | | 3-7s | Close-up of the workflow ingesting pull request history | Match cut | "to structured action" | Show transformation | | 7-12s | Interface view with checks for hallucinated APIs | Gentle pan | "Risks visible before launch" | Build trust | | 12-17s | Operator approves a clear recommendation | Locked camera | "Human review stays in control" | Show governance | | 17-20s | Final artifact appears: scene-by-scene video prompt, shot timing, motion notes, and edit checklist | Clean fade | "Agentic Engineering: ready to use" | Close with value |

Review Checklist

Every shot has one inspectable visual idea.
The video never implies full autonomy where human review is required.
The highest-risk failure mode, hallucinated APIs, is visibly controlled.

README

README.md

Agentic Engineering: Short-Form Video Storyboard

Use this prompt when you need scene-by-scene video prompt, shot timing, motion notes, and edit checklist 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.