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Dt Rpi Integration

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From the author

Design Thinking handoff knowledge for research-ready rpi-research inputs and DT-aware rpi-plan, rpi-implement, and rpi-review context
— the description by Microsoft

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as the bridge between Design Thinking coaching and the RPI (Research, Plan, Implement, Review) workflow. It defines the formal handoff contracts, artifact schemas, and contextual framing required to ensure that insights gathered during Design Thinking are effectively utilized by downstream RPI phases. It ensures that regardless of the Design Thinking exit point, all projects enter the RPI pipeline through the research phase with standardized, validated documentation.

What it does

  • Defines the formal handoff contract for transitioning from Design Thinking to RPI phases.
  • Provides standardized schemas for documenting handoff artifacts and project constraints.
  • Offers guidance on framing research, planning, implementation, and review contexts based on DT artifacts.
  • Includes procedures for subagent dispatch to validate handoff readiness.
  • Provides specific instructions for generating lo-fi image prompts for concept visualization.

When to use it

  • When a team graduates from a Design Thinking space boundary and needs to begin the RPI workflow.
  • When you need to prepare research-ready inputs from completed Design Thinking methods.
  • When you need to ensure that RPI planning and implementation phases are informed by validated DT context.
  • When you need to generate neutral, lo-fi concept sketches for stakeholder evaluation.

Advantages

  • Ensures continuity of project context across different development phases.
  • Standardizes the quality and format of handoff documentation.
  • Reduces investigation scope by providing validated artifacts from earlier stages.
  • Maintains neutrality in visual concept generation through strict prompt enforcement.

Usage examples

“How do I structure the handoff summary when moving from the Problem space to RPI research?”
The agent provides the required YAML schema for the handoff-summary.md file, including fields for exit points, artifacts, and constraint tracking.
“Generate a lo-fi image prompt for a concept where a technician uses a tablet to check inventory.”
The agent generates a prompt following the required stick-figure template, ensuring no personal traits are included and the style is set to black-and-white line art.

Install

  1. Download the skill as a ZIP:

    https://autogineer.com/agent-skills/microsoft/dt-rpi-integration/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip dt-rpi-integration.zip -d .agents/skills

Documentation by Microsoft

Design Thinking → RPI Integration — Skill Entry

This skill is the entry point for Design Thinking to RPI integration knowledge.

The DT coach loads these references when Design Thinking coaching graduates into the RPI workflow. Every DT exit produces research-ready input for rpi-research. The retained downstream phases, rpi-plan, rpi-implement, and rpi-review, consume the resulting context. RPI Agent is the lifecycle wrapper when end-to-end coordination is needed; it does not replace rpi-research as the handoff target.

Integration references

ReferenceWhen to load
Handoff contractExit points, artifact schemas, RPI input contracts, and quality markers for lateral DT-to-RPI handoff
Space-exit handoffsStep-by-step Problem, Solution, and Implementation Space exit procedures that compile the handoff summary and the RPI entry document
Research contextDT-aware rpi-research framing for handoffs from the DT coach
Planning contextDT-aware rpi-plan context for plans originating from DT artifacts
Implement contextDT-aware rpi-implement context applying fidelity and stakeholder constraints
Review contextDT-aware rpi-review criteria for evaluating Design Thinking artifacts
Subagent handoffReadiness assessment, artifact compilation, and validation via subagent dispatch
Image prompt generationMethod 5 concept visualization with lo-fi prompt enforcement

Skill layout

  • SKILL.md — this file (skill entrypoint).

  • references/ — the DT-to-RPI integration reference documents.

    • rpi-handoff-contract.md — DT-to-RPI handoff contract: exit points, artifact schemas, RPI input contracts, and confidence markers.

    • space-exit-handoffs.md provides the per-space exit procedures, handoff summaries, transition entries, and RPI entry documents.

    • rpi-research-context.md provides DT-aware rpi-research context.

    • rpi-planning-context.md provides DT-aware rpi-plan context.

    • rpi-implement-context.md provides DT-aware rpi-implement context.

    • rpi-review-context.md provides DT-aware rpi-review context.

    • subagent-handoff.md provides the subagent dispatch workflow for handoff readiness, compilation, and validation.

    • image-prompt-generation.md provides Method 5 lo-fi image prompt generation guidance.

Files

references/image-prompt-generation.md


title: 'DT Image Prompt Generation' description: Guidance for writing lo-fi stick-figure visualization prompts during Method 5b Concept Articulation for stakeholder evaluation.

Purpose and Scope

During Method 5b (Concept Articulation), write visualization prompts that transform user concepts into lo-fi sketches for stakeholder evaluation.

Prompts are authored as part of YAML concept cards and optionally used with M365 Copilot or modern image models such as gpt-image-2 between Method 5b and 5c.

When to Offer Image Generation:

  • After drafting concept title and description during Method 5b

  • Before Method 5c Silent Review when visual concepts support stakeholder comprehension

  • Only if lo-fi prompt structure is clearly explained and enforced

When Not to Offer:

  • During Method 5a planning (concepts not yet articulated)

  • If user prefers text-only concept evaluation

  • If prompt violates lo-fi standards (redirect to structure before generating)

Prompt Generation Approach

Transform concept into visualization prompt using this sequence:

  1. Identify Core Interaction: What single action or outcome does this concept test?

  2. Extract Visual Elements: Who (stakeholder archetype by role), what (tool/object), where (environmental context from Methods 3-4)

  3. Remove Personal Traits: Apply the representation safeguards below before writing the prompt

  4. Constrain Text: Use only short, quoted labels when text is essential

  5. Apply Lo-Fi Enforcement: Add all 5 required style directive layers

  6. Validate 15-Second Test: Could this be sketched on a napkin in 15 seconds?

Concept → Prompt Workflow:

Concept: "Workers scan QR codes to access equipment manuals"
  ↓
Core Interaction: Worker scans code, sees instructions
  ↓
Visual Elements: Stick figure + phone + QR code + simple list
  ↓
Lo-Fi Layers: stick figures, minimal lines, plain background, b&w, no shading
  ↓
Prompt: "Create a simple stick-figure scene of a factory worker pointing
        a phone at a big QR code on a machine; on the phone screen, show
        a plain card with three bullet lines labelled 'Step 1, Step 2, Step 3'.
        Black-and-white line art, stick figures, minimal lines, no shading,
        plain white background."

Representation and Stereotype Safeguards

Concept prompts depict a role performing one interaction, never a specific person. Interview notes, personas, and customer cards often carry a participant's name, age, gender, ethnicity, or appearance; none of that belongs in a prompt by default.

  • Describe the person by role and action: "an office manager reviewing a dashboard", "a technician scanning a machine".

  • Leave out names, age, gender, ethnicity, body type, clothing, and other appearance details drawn from interviews or personas.

  • Include a personal characteristic only when Observed or Reported evidence from Methods 2-3 shows it matters to the concept. Then describe the need or constraint rather than the trait: "a person holding a phone at arm's length to read small text", not "an elderly person".

  • Do not infer a trait from a name, a role, or an age. An interview with a 50-year-old participant named Ashley becomes "a stick-figure office manager reviewing a dashboard", not "an older woman at a desk".

  • Image models can fill in stereotyped defaults even for role-only prompts, such as drawing every nurse as a woman. After generation, review each image for stereotyped or one-sided depictions. Regenerate, changing one prompt constraint at a time, until the image reads as a neutral role sketch.

Image Prompt Structure

Required Template:

Create a simple stick-figure sketch showing [ONE CORE INTERACTION].
[Optional: Environmental context if constraint-relevant].
Minimal lines, plain white background, black-and-white line art, no shading.

Components:

  • Opening: Action verb + "stick-figure" + subject (e.g., "Create a simple stick-figure sketch showing...")

  • Single Scenario: One interaction per concept, not multiple use cases

  • Optional Context: Environmental details only when relevant to constraints from Methods 3-4

  • Terminal Reinforcement: Combine 3-5 lo-fi descriptors in closing sentence

  • Text Constraint: Any on-image text must be short, quoted, and label-like; avoid sentences or dense UI copy

Subject, Context, Style, Focus, Exclusions:

  • Subject: Stakeholder archetype by role (worker, nurse, manager) + tool/object, with no personal traits unless evidence requires them

  • Context: Environmental constraints (factory floor, hospital bed, office desk) only when relevant

  • Style: Always "stick-figure" or "simple line drawing" with "black-and-white line art"

  • Focus: Single core interaction validating one key assumption

  • Exclusions: "no shading", "no detail", "minimal lines" to block implementation detail

Lo-Fi Visual Requirements

5-Layer Required Directive Stack (all prompts must include all layers):

  1. Human Representation: "stick figures" OR "simple line drawing"

  2. Complexity Reduction: "minimal lines" (required)

  3. Environmental Simplification: "plain white background"

  4. Visual Treatment: "black-and-white line art"

  5. Detail Blocking: "no shading" OR "no detail"

Effective Descriptor Vocabulary:

  • Core: "stick figures", "minimal lines", "plain white background", "black-and-white line art", "no shading"

  • Supplementary: "simple sketch", "napkin sketch", "whiteboard drawing", "basic shapes"

Anti-Pattern Vocabulary (never use):

  • "detailed", "interface mockup", "buttons and menus", "production-ready", "polished", "realistic", "high-fidelity"

Scrappy Principle Alignment:

  • Rough concepts invite structural feedback: "Does this solve the right problem?"

  • Polished concepts invite cosmetic feedback: "I'd change the wording here"

  • Maintain roughness to preserve validation quality

Method 5 Workflow Integration

Method 5b (Concept Articulation) — Prompts Written Here:

  • Draft 2-4 word concept title

  • Write 1-2 sentence description covering what and how

  • Create visualization prompt with mandatory lo-fi structure

  • Generate concepts.yml artifact with name/description/file/prompt fields

  • Validate against 15-second napkin sketch test

Between Methods 5 and 6 — Images Generated (Optional):

  • Optional generation: Use M365 Copilot or a modern image model to generate images from concepts.yml prompts

  • Review generated PNGs and regenerate if outputs violate lo-fi standards or show stereotyped depictions of people, changing one prompt constraint at a time

Method 5c (Concept Evaluation) — Images Used for Alignment:

  • Silent Review sequence with stakeholder representatives

  • Visual concepts support 30-second comprehension test

  • Three-lens D/F/V evaluation uses text + optional visuals

  • All DT coaching artifacts are scoped to .copilot-tracking/dt/{project-slug}/. Never write DT artifacts directly under .copilot-tracking/dt/ without a project-slug directory.

YAML Concept Card Schema:

concepts:
  - name: Concept Title              # 2-4 words
    description: >-                   # 1-2 sentences, what + how
      Brief explanation of solution.
    file: concept-title.png           # kebab-case
    prompt: >-                        # Stick figures, minimal lo-fi
      Create a simple stick-figure sketch showing [core interaction].
      Minimal lines, plain white background, black-and-white line art, no shading.

Example Prompt Patterns

Example 1: Manufacturing Safety Concept

Concept: QR Snap Manuals — Workers scan machine QR codes to access step-by-step safety procedures.

Prompt:

Create a simple stick-figure scene of a factory worker pointing a phone at a big
QR code on a machine; on the phone screen, show a plain card with three bullet
lines labelled 'Step 1, Step 2, Step 3'. Black-and-white line art, stick figures,
minimal lines, no shading, plain white background.

Rationale: Single scenario (scanning QR code), environmental context (factory machine), all 5 lo-fi layers, passes 15-second sketch test.

Example 2: Healthcare Alert Concept

Concept: Vital Sign Pulse — Nurses receive automatic alerts when patient vitals exceed thresholds.

Prompt:

Create a simple stick-figure sketch showing a nurse holding a tablet with an upward
arrow and exclamation mark, standing next to a patient in a bed. Minimal lines,
plain white background, black-and-white line art, no shading.

Rationale: Healthcare environment (patient bed), single interaction (alert notification), simple symbols (arrow, exclamation), no UI mockup detail.

Example 3: Office Efficiency Concept

Concept: Dashboard Glance — Managers view team metrics on a single dashboard page.

Prompt:

Create a simple stick-figure sketch showing a person at a desk looking at a screen
with three simple bar charts. Minimal lines, plain white background, black-and-white
line art, no shading.

Rationale: Office context (desk), single interaction (viewing metrics), basic shapes (bar charts), avoids "detailed interface mockup" trap.

Requirements and compatibility

Written for: GitHub Copilot, Claude Code

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Claude Code frontmatter

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Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/hve-core · .github/skills/design-thinking/dt-rpi-integration · commit f425e94
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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