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Customer Card Render

MicrosoftOfficialPublished by Microsoft

  • Skill
  • Content generator
  • Documents and content
  • MIT
  • Low risk
  • Has scripts

From the author

Generate customer-card slide content YAML from Design Thinking canonical artifacts
— the description by Microsoft

AutoGineer summary

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

The customer-card-render skill automates the transformation of canonical Design Thinking markdown files into template-driven content.yaml files. It acts as a mapping layer that extracts specific sections from artifacts like vision statements, problem statements, scenarios, personas, and use cases, organizing them into structured slide content. This tool is designed to be renderer-neutral, producing reviewable YAML files that can be consumed by downstream presentation authoring tools. It does not handle the visual rendering or styling of the final presentation, focusing instead on data extraction and content organization.

What it does

  • Parses canonical Design Thinking markdown files for structured content.
  • Maps extracted sections to predefined content.yaml templates.
  • Supports multiple artifact types including Vision, Problem, Scenario, Persona, and Use Case.
  • Splits complex Use Case artifacts into a structured 4-slide layout.
  • Normalizes text content to ensure consistent formatting in generated YAML.
  • Provides a CLI for batch processing of project-specific artifacts.

When to use it

  • When you need to convert raw Design Thinking markdown documentation into structured slide content.
  • When preparing content for automated presentation generation workflows.
  • When you require a consistent, template-driven structure for customer-facing cards.
  • When integrating with Design Thinking coaching agents to automate slide content creation.

Advantages

  • Decouples content mapping logic from presentation rendering tools.
  • Ensures consistent slide structure across different Design Thinking artifacts.
  • Produces renderer-neutral, reviewable YAML artifacts.
  • Automates the tedious task of manual slide content population.

Usage examples

“Generate slide content for the current project's Design Thinking artifacts.”
The script processes the markdown files in the canonical directory and outputs structured content.yaml files to the designated output folder.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/microsoft/customer-card-render/download
  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip customer-card-render.zip -d .agents/skills

Documentation by Microsoft

Customer Card Render Skill

Converts canonical Design Thinking markdown artifacts into template-driven content.yaml slide definitions.

Overview

This skill handles the Design Thinking-specific mapping layer: extracting sections from canonical markdown artifacts and filling template-driven content.yaml files. It does not build, theme, export, or validate a presentation file. The separate hve-slides skill owns HTML presentation authoring and rendering.

Keeping this layer separate means:

  • Customer-card mapping logic stays independent from any presentation tooling.

  • The skill can be included in packages independently.

  • The generated YAML stays a reviewable, renderer-neutral artifact.

Prerequisites

  • Python 3.11+

  • uv package manager, installed with a package manager that verifies the download against a published hash:

    # Windows
    winget install --id astral-sh.uv -e
    
    # macOS / Linux (Homebrew)
    brew install uv

    To install from a release archive instead, download it from the uv releases page and check it against its .sha256 file before extracting.

Directory Structure

.github/skills/experimental/customer-card-render/
├── SKILL.md
├── pyproject.toml
├── references/
│   └── mapping-spec.md
├── scripts/
│   └── generate_cards.py
├── templates/
│   ├── global-style.yaml
│   ├── persona.content.yaml
│   ├── problem.content.yaml
│   ├── scenario.content.yaml
│   ├── use-case-slide1.content.yaml
│   ├── use-case-slide2.content.yaml
│   ├── use-case-slide3.content.yaml
│   └── vision.content.yaml
└── tests/
    ├── fuzz_harness.py
    └── test_generate_cards.py

Supported Artifact Types

Artifact TypeSlide Layout
Vision StatementSingle slide
Problem StatementSingle slide
ScenarioSingle slide
Use Case4 slides (see below)
PersonaSingle slide
Use Case 4-Slide Layout

Each Use Case expands into 4 consecutive slides with distinct sections:

SlideContent
Slide 1Use Case Description, Use Case Overview, Business Value, Primary User
Slide 2Secondary User, Preconditions, Steps, Data Requirements
Slide 3Equipment Requirements, Operating Environment, Success Criteria, Pain Points
Slide 4Extensions, Evidence

Cards are ordered by artifact type (Vision → Problem → Scenario → Use Case → Persona), then alphabetically by title within each type. Use Cases appear with all 4 slides consecutive (Slide N, N+1, N+2, N+3).

Generate Slide YAML

Generate slide YAML from canonical markdown
python "<customer-card-render-skill-root>/scripts/generate_cards.py" \
  --canonical-dir .copilot-tracking/dt/<project-slug>/canonical \
  --output-dir .copilot-tracking/dt/<project-slug>/render/content

Resolve <customer-card-render-skill-root> from the loaded skill location before running the command.

generate_cards.py CLI Reference
FlagRequiredDefaultDescription
--canonical-dirNo<skill-root>/canonicalDirectory containing canonical DT markdown files
--output-dirNo<skill-root>/scripts/contentDirectory to write generated content.yaml files
-v, --verboseNo—Enable debug-level logging

The script reads each markdown file in --canonical-dir, detects the artifact type from frontmatter, extracts required sections, and generates content.yaml files. Vision, Problem, Scenario, and Persona artifacts produce one slide each. Use Case artifacts produce 4 consecutive slides per use case.

For the section-to-field mapping contract and Use Case 4-slide layout details, see references/mapping-spec.md.

The generated content.yaml files are this skill's final output. For an explicitly requested HTML presentation, hand reviewed YAML to hve-slides authoring. For a levelled demo, use hve-demo-material and its references/slide-content.md adapter contract. Resolve skills by name, preserve canonical order, source labels and unknown-knowledge markers, and never claim a presentation exists until rendered and validated. This mapping skill does not synthesize narration or dispatch a deck subagent.

DT Coach Integration

The dt-coaching-foundation skill's canonical-deck reference provides opt-in workflow integration for the Design Thinking coaching agent. When a user opts in, the coaching agent offers to generate customer-card YAML at method exit points. The generation command above runs as part of that workflow with --canonical-dir and --output-dir resolved from the active DT project slug in .copilot-tracking/dt/.

Canonical artifacts are produced by the DT coach and live under .copilot-tracking/dt/<project-slug>/canonical/.

Running Tests

cd "<customer-card-render-skill-root>"
uv sync --group dev
uv run pytest tests/

Tests cover parsing, template selection, YAML emission, and regressions. The tests/fuzz_harness.py file is an Atheris polyglot fuzz harness for OSSF Scorecard compliance.

Content Fidelity Note: Use Case Cards

Use Case cards are split across 3 opinionated slides, each with dedicated sections:

  • Slide 1: Introduces the use case with Description, Overview, Business Value, and Primary User

  • Slide 2: Details execution with Secondary User, Preconditions, Steps, and Data Requirements

  • Slide 3: Captures quality criteria with Equipment Requirements, Operating Environment, Success Criteria, Pain Points, and Evidence

This structure ensures all 16 Use Case sections fit legibly across 4 slides without compression. Each section appears in its own textbox with appropriate styling and heading.

For complete mapping details, see references/mapping-spec.md.

Troubleshooting

IssueCauseSolution
uv not founduv not installedRun winget install --id astral-sh.uv -e (Windows) or brew install uv (macOS/Linux)
Python not found by uvNo Python 3.11+ on PATHRun uv python install 3.11
Template not found--canonical-dir contains unknown typeCheck frontmatter type: field against supported artifact types
Empty output directoryNo canonical markdown files foundConfirm --canonical-dir path and that files have --- frontmatter

Files

scripts/generate_cards.py

#!/usr/bin/env python3
# Copyright (c) 2026 Microsoft Corporation. All rights reserved.
# SPDX-License-Identifier: MIT

"""Generate slide content YAML from canonical DT markdown artifacts."""

from __future__ import annotations

import argparse
import logging
import re
from dataclasses import dataclass
from datetime import datetime, timezone
from pathlib import Path

LOGGER = logging.getLogger(__name__)

_SCRIPT_DIR = Path(__file__).resolve().parent
_SKILL_ROOT = _SCRIPT_DIR.parent
_TEMPLATES_DIR = _SKILL_ROOT / "templates"
_DEFAULT_CANONICAL = _SKILL_ROOT / "canonical"
_DEFAULT_OUTPUT = _SCRIPT_DIR / "content"

STYLE_TEMPLATE = _TEMPLATES_DIR / "global-style.yaml"

TYPE_TO_TEMPLATE = {
    "Vision Statement": "vision.content.yaml",
    "Problem Statement": "problem.content.yaml",
    "Scenario": "scenario.content.yaml",
    "Use Case": "use-case-slide1.content.yaml",
    "Persona": "persona.content.yaml",
}

ORDER = ["Vision Statement", "Problem Statement", "Scenario", "Use Case", "Persona"]


@dataclass(frozen=True)
class Card:
    artifact_type: str
    title: str
    summary: str
    source_path: str
    last_updated: str
    slide_part: int = 0
    sections: dict[str, str] | None = None


def configure_logging(verbose: bool) -> None:
    level = logging.DEBUG if verbose else logging.INFO
    logging.basicConfig(level=level, format="%(levelname)s: %(message)s")


def parse_frontmatter(text: str) -> tuple[dict[str, str], str]:
    match = re.match(r"^---\s*\n(.*?)\n---\s*\n", text, re.DOTALL)
    if not match:
        return {}, text

    fields: dict[str, str] = {}
    for line in match.group(1).splitlines():
        key, _, value = line.partition(":")
        if key and _:
            fields[key.strip().lower()] = value.strip().strip('"').strip("'")

    return fields, text[match.end() :]


def extract_section(body: str, heading: str) -> str:
    pattern = (
        rf"(?ims)^\s*#{{2,3}}\s+(?:\d+\.?\s*)?{re.escape(heading)}\s*\r?\n"
        r"(.*?)(?=^\s*#{2,3}\s+|\Z)"
    )
    match = re.search(pattern, body)
    return match.group(1).strip() if match else ""


def extract_first_heading(body: str) -> str:
    match = re.search(r"(?im)^\s*#{1,3}\s+(.+?)\s*$", body)
    return match.group(1).strip() if match else ""


def extract_intro_block(body: str) -> str:
    match = re.search(r"(?ims)^\s*##\s+.+?\s*\r?\n(.*?)(?=^\s*#{2,3}\s+|\Z)", body)
    return match.group(1).strip() if match else ""


def infer_artifact_type(path: Path) -> str:
    filename = path.name.lower()
    parent = path.parent.name.lower()
    if filename == "vision-statement.md":
        return "Vision Statement"
    if filename == "problem-statement.md":
        return "Problem Statement"
    if parent == "scenarios":
        return "Scenario"
    if parent == "use-cases":
        return "Use Case"
    if parent == "personas":
        return "Persona"
    return "Unknown"


def template_for_type(artifact_type: str, slide_part: int = 0) -> Path:
    if artifact_type == "Use Case" and slide_part > 0:
        return _TEMPLATES_DIR / f"use-case-slide{slide_part}.content.yaml"
    try:
        return _TEMPLATES_DIR / TYPE_TO_TEMPLATE[artifact_type]
    except KeyError as exc:
        raise ValueError(f"Unsupported artifact type: {artifact_type}") from exc


def yaml_escape(text: str) -> str:
    # Normalize canonical prose before YAML encoding.
    # Hard-wrapped lines are merged into single-line paragraphs while list
    # item boundaries are preserved.
    text = normalize_text(text)
    # Escape backslashes first to avoid double-escaping, then quotes, then
    # convert logical line breaks to explicit escape sequences.
    text = text.replace("\\", "\\\\").replace('"', '\\"')
    return text.replace("\n", "\\n")


def normalize_text(text: str) -> str:
    """Merge hard-wrapped prose lines while preserving list structure."""
    if not text.strip():
        return ""

    list_pattern = re.compile(r"^\s*(?:[-*+]\s+|\d+[\.)]\s+)")
    normalized_blocks: list[str] = []
    prose_lines: list[str] = []
    list_lines: list[str] = []

    def flush_prose() -> None:
        if prose_lines:
            normalized_blocks.append(
                " ".join(line.strip() for line in prose_lines if line.strip())
            )
            prose_lines.clear()

    def flush_list() -> None:
        if list_lines:
            normalized_blocks.append(
                "\n".join(line.strip() for line in list_lines if line.strip())
            )
            list_lines.clear()

    for raw_line in text.splitlines():
        line = raw_line.rstrip()
        if not line.strip():
            flush_prose()
            flush_list()
            continue

        if list_pattern.match(line):
            flush_prose()
            list_lines.append(line)
            continue

        flush_list()
        prose_lines.append(line)

    flush_prose()
    flush_list()

    return "\n\n".join(block for block in normalized_blocks if block.strip())


def _scenario_summary(body: str) -> str:
    description = extract_section(body, "Description")
    narrative = extract_section(body, "Scenario Narrative")
    hmw = extract_section(body, "How Might We")

    blocks = []
    if description:
        blocks.append(f"Description:\\n{description}")
    if narrative:
        blocks.append(f"Scenario Narrative:\\n{narrative}")
    if hmw:
        blocks.append(f"How Might We:\\n{hmw}")
    return "\\n\\n".join(blocks)


def _vision_sections(body: str) -> dict[str, str]:
    """Extract sections for Vision Statement card."""
    return {
        "V_VISION_STATEMENT": extract_section(body, "Vision Statement"),
        "V_WHY_THIS_MATTERS": extract_section(body, "Why This Matters"),
    }


def _problem_sections(body: str) -> dict[str, str]:
    """Extract sections for Problem Statement card."""
    return {
        "P_PROBLEM_STATEMENT": extract_section(body, "Problem Statement")
        or extract_section(body, "Customer-friendly summary"),
    }


def _scenario_sections(body: str) -> dict[str, str]:
    """Extract sections for Scenario card."""
    return {
        "SC_DESCRIPTION": extract_section(body, "Description"),
        "SC_SCENARIO_NARRATIVE": extract_section(body, "Scenario Narrative"),
        "SC_HOW_MIGHT_WE": extract_section(body, "How Might We"),
    }


def _persona_sections(body: str) -> dict[str, str]:
    """Extract sections for Persona card."""
    return {
        "PE_DESCRIPTION": extract_section(body, "Description"),
        "PE_USER_GOAL": extract_section(body, "User Goal"),
        "PE_USER_NEEDS": extract_section(body, "User Needs"),
        "PE_USER_MINDSET": extract_section(body, "User Mindset"),
    }


def _use_case_slide1(body: str) -> dict[str, str]:
    """Extract sections for Use Case Slide 1."""
    return {
        "UC_DESCRIPTION": extract_section(body, "Use Case Description"),
        "UC_OVERVIEW": extract_section(body, "Use Case Overview"),
        "UC_BUSINESS_VALUE": extract_section(body, "Business Value"),
        "UC_PRIMARY_USER": extract_section(body, "Primary User"),
    }


def _use_case_slide2(body: str) -> dict[str, str]:
    """Extract sections for Use Case Slide 2."""
    return {
        "UC_PRIMARY_USER": extract_section(body, "Primary User"),
        "UC_SECONDARY_USER": extract_section(body, "Secondary User"),
        "UC_STEPS": extract_section(body, "Steps"),
        "UC_PRECONDITIONS": extract_section(body, "Preconditions"),
        "UC_DATA_REQUIREMENTS": extract_section(body, "Data Requirements"),
    }


def _use_case_slide3(body: str) -> dict[str, str]:
    """Extract sections for Use Case Slide 3."""
    return {
        "UC_EQUIPMENT_REQUIREMENTS": extract_section(body, "Equipment Requirements"),
        "UC_SUCCESS_CRITERIA": extract_section(body, "Success Criteria"),
        "UC_OPERATING_ENVIRONMENT": extract_section(body, "Operating Environment"),
        "UC_PAIN_POINTS": extract_section(body, "Pain Points"),
    }


def _use_case_slide4(body: str) -> dict[str, str]:
    """Extract sections for Use Case Slide 4."""
    return {
        "UC_EXTENSIONS": extract_section(body, "Extensions"),
        "UC_EVIDENCE": extract_section(body, "Evidence"),
    }


def parse_card(path: Path, canonical_root: Path) -> Card | None:
    text = path.read_text(encoding="utf-8")
    frontmatter, body = parse_frontmatter(text)

    artifact_type = infer_artifact_type(path)
    if artifact_type == "Unknown":
        LOGGER.debug("Skipping unknown artifact: %s", path)
        return None

    title = (
        frontmatter.get("title")
        or extract_first_heading(body)
        or path.stem.replace("-", " ").title()
    )
    source_path = frontmatter.get("source path") or frontmatter.get("source file path")
    if not source_path:
        source_path = path.relative_to(canonical_root).as_posix()

    metadata_last_updated = frontmatter.get("last updated")
    last_updated = metadata_last_updated or datetime.now(timezone.utc).strftime(
        "%Y-%m-%d"
    )

    # Default summary (used as fallback if sections are empty)
    summary = extract_intro_block(body) or ""

    return Card(
        artifact_type=artifact_type,
        title=title,
        summary=summary,
        source_path=source_path,
        last_updated=last_updated,
    )


def expand_cards(card: Card, body: str) -> list[Card]:
    """Expand a single card into multiple slides if needed.

    Use Case artifacts expand into 4 slides; all others return a single-element list
    with section-specific placeholders.
    """
    sections: dict[str, str] | None = None
    slide_part = 0

    if card.artifact_type == "Use Case":
        # Use Cases expand to 4 slides
        slide1_sections = _use_case_slide1(body)
        slide2_sections = _use_case_slide2(body)
        slide3_sections = _use_case_slide3(body)
        slide4_sections = _use_case_slide4(body)

        return [
            Card(
                artifact_type=card.artifact_type,
                title=card.title,
                summary=card.summary,
                source_path=card.source_path,
                last_updated=card.last_updated,
                slide_part=1,
                sections=slide1_sections,
            ),
            Card(
                artifact_type=card.artifact_type,
                title=card.title,
                summary=card.summary,
                source_path=card.source_path,
                last_updated=card.last_updated,
                slide_part=2,
                sections=slide2_sections,
            ),
            Card(
                artifact_type=card.artifact_type,
                title=card.title,
                summary=card.summary,
                source_path=card.source_path,
                last_updated=card.last_updated,
                slide_part=3,
                sections=slide3_sections,
            ),
            Card(
                artifact_type=card.artifact_type,
                title=card.title,
                summary=card.summary,
                source_path=card.source_path,
                last_updated=card.last_updated,
                slide_part=4,
                sections=slide4_sections,
            ),
        ]
    elif card.artifact_type == "Vision Statement":
        sections = _vision_sections(body)
    elif card.artifact_type == "Problem Statement":
        sections = _problem_sections(body)
    elif card.artifact_type == "Scenario":
        sections = _scenario_sections(body)
    elif card.artifact_type == "Persona":
        sections = _persona_sections(body)

    return [
        Card(
            artifact_type=card.artifact_type,
            title=card.title,
            summary=card.summary,
            source_path=card.source_path,
            last_updated=card.last_updated,
            slide_part=slide_part,
            sections=sections,
        )
    ]


def collect_cards(canonical_root: Path) -> list[Card]:
    files: list[Path] = []
    files.extend(
        path
        for path in [
            canonical_root / "vision-statement.md",
            canonical_root / "problem-statement.md",
        ]
        if path.exists()
    )

    for folder in ["scenarios", "use-cases", "personas"]:
        dir_path = canonical_root / folder
        if dir_path.exists():
            files.extend(sorted(dir_path.glob("*.md"), key=lambda p: p.name.lower()))

    cards: list[Card] = []
    for path in files:
        card = parse_card(path, canonical_root)
        if card is None:
            continue
        _, body = parse_frontmatter(path.read_text(encoding="utf-8"))
        expanded = expand_cards(card, body)
        cards.extend(expanded)

    cards.sort(
        key=lambda card: (
            ORDER.index(card.artifact_type),
            card.title.lower(),
            card.slide_part,
        )
    )
    return cards


def render_slide(card: Card, slide_number: int) -> str:
    template_text = template_for_type(card.artifact_type, card.slide_part).read_text(
        encoding="utf-8"
    )

    # Base replacements for all slides
    replacements = {
        "SLIDE_NUMBER": str(slide_number),
        "TITLE": yaml_escape(card.title),
        "SOURCE_PATH": yaml_escape(card.source_path),
        "LAST_UPDATED": yaml_escape(card.last_updated),
        "TYPE_LABEL": yaml_escape(card.artifact_type.upper()),
    }

    # All artifact types now use section-specific placeholders
    if card.sections:
        for key, value in card.sections.items():
            replacements[key] = yaml_escape(value)

    rendered = template_text
    for key, value in replacements.items():
        rendered = rendered.replace(f"{{{{{key}}}}}", value)
    return rendered


def write_outputs(cards: list[Card], output_dir: Path) -> None:
    output_dir.mkdir(parents=True, exist_ok=True)
    (output_dir / "global").mkdir(parents=True, exist_ok=True)

    (output_dir / "global" / "style.yaml").write_text(
        STYLE_TEMPLATE.read_text(encoding="utf-8"),
        encoding="utf-8",
    )

    for index, card in enumerate(cards, start=1):
        slide_dir = output_dir / f"slide-{index:03d}"
        slide_dir.mkdir(parents=True, exist_ok=True)
        (slide_dir / "content.yaml").write_text(
            render_slide(card, index),
            encoding="utf-8",
        )

    LOGGER.info("Generated %d slide content files in %s", len(cards), output_dir)


def create_parser() -> argparse.ArgumentParser:
    parser = argparse.ArgumentParser(
        description=("Generate customer-card content YAML from canonical markdown.")
    )
    parser.add_argument("--canonical-dir", type=Path, default=_DEFAULT_CANONICAL)
    parser.add_argument("--output-dir", type=Path, default=_DEFAULT_OUTPUT)
    parser.add_argument("-v", "--verbose", action="store_true")
    return parser


def main() -> int:
    args = create_parser().parse_args()
    configure_logging(args.verbose)

    canonical_dir = args.canonical_dir.resolve()
    output_dir = args.output_dir.resolve()

    if not canonical_dir.exists() or not any(canonical_dir.glob("**/*.md")):
        LOGGER.error("Canonical directory is missing or empty: %s", canonical_dir)
        return 1

    cards = collect_cards(canonical_dir)
    if not cards:
        LOGGER.error("No canonical artifacts found in: %s", canonical_dir)
        return 1

    write_outputs(cards, output_dir)
    return 0


if __name__ == "__main__":
    raise SystemExit(main())

Requirements and compatibility

Written for: GitHub Copilot

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: Needs a command-line tool, Needs Python

The author says: Requires Python 3.11+ and uv

Security

What our scanner noticed· Low risk

  • Scripts that your agent may run

This skill includes scripts that your agent may run. Read them before you use it.

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Published by
Microsoft
Publisher
Microsoft (Official)
Source
microsoft/hve-core · .github/skills/experimental/customer-card-render · commit f425e94
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
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Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download customer-card-render.zip