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Mckinsey Market Research Deck

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norahe0304-art
mckinsey-market-research-deck

End-to-end playbook for producing a top-tier, McKinsey-style market-research deck (HTML page-turning presentation + print-ready PDF) for a brand or product category. Covers the research methodology (TAM/SAM/SOM bottom-up, Good/Better/Best framework, competitor profiling, customer pain points, unit economics, business case), the locked McKinsey visual design system, a reusable Python deck engine, an adversarial verify workflow for decision-grade numbers, an image-generation handoff, and a full QC checklist. Use when the user asks to "do market research", build a "market research deck / report", a "McKinsey-style deck / presentation", a "GBB / Good-Better-Best analysis", "market sizing", "competitive landscape deck", "投资/商业案例 deck", or to turn research into a polished slide deck or PDF.

Overview

Publishernorahe0304-art
Repository30x-mckinsey-research-deck
Skill namemckinsey-market-research-deck
Stars
55
Forks
6
Bundled files
4
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 4 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by norahe0304-art on GitHub. Read the source before you install it.

Installation

Install the Mckinsey Market Research Deck AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/norahe0304-art/30x-mckinsey-research-deck/tree/master/skills/mckinsey-market-research-deck
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/norahe0304-art/30x-mckinsey-research-deck.git /tmp/30x-mckinsey-research-deck
mkdir -p .claude/skills
cp -r /tmp/30x-mckinsey-research-deck/skills/mckinsey-market-research-deck .claude/skills/mckinsey-market-research-deck
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Mckinsey Market Research Deck in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Mckinsey Market Research Deck on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Mckinsey Market Research Deck is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

McKinsey-Style Market-Research Deck

Build a research-backed, visually elite, page-turning deck (HTML reviewed on screen → PDF for sharing). This skill is the distilled, reusable playbook. Read the four reference files as you reach each phase — do not try to hold all of it in head at once.

  • references/methodology.md — how to do the research and what each section must contain
  • references/design-system.md — the locked visual contract (tokens, page types, layout laws)
  • ../mckinsey-deck/assets/deck_engine.py — the canonical engine (owned by the mckinsey-deck style skill; this skill consumes it — never fork a local copy, that's how drift starts)
  • references/qc-checklist.md — the self-verify pass before delivery
  • references/image-handoff.md — the template that hands product/cover images to an image generator

The 7-page spine (always)

  1. The Answer — one answer_slide() right after the cover: the governing thought (the full recommendation in one sentence) + 3–4 pillar conclusions with key numbers. Pyramid Principle: the answer comes first; the rest of the deck is its proof. Drafted in Phase 1.5, finalized last.
  2. Market Overview — size, growth, channel, the structural shift
  3. Brand Landscape — Good/Better/Best ladder + brand-by-brand profiles
  4. Product Categories — per-subcategory competitor price ladder + pain points + the brand's lineup
  5. Customer Pain Points — sourced failure modes, each one a selling-point opening
  6. Opportunities — pain points → product direction
  7. The Solution — positioning, pricing/packaging, the line plan, and the decision pages (bottom-up market sizing, economics, business case) + the thesis

End with a full source register (every URL, numbered).

Workflow (run in order)

Phase 0 — Scope + Day-1 hypothesis

Get: the brand, the parent retailer/company, the category, the geography, the SKU-count target, and the strategic question (usually "what line should we build and why"). Confirm the deck is the deliverable (pure market research), not a precursor needing first-party data. Then write the Day-1 hypothesis — a one-paragraph draft of the answer ("we believe X because A/B/C") before researching. It steers the research (80/20: go deep only on the branches that confirm or kill it) and it is there to be falsified, not defended — revise it whenever the evidence disagrees, and say so in the deck.

Phase 1 — Research → one data file

Do the research per references/methodology.md. Land everything in a single <brand>-data.json (the deck is data-driven from it). Every number must carry a sourceUrl. Schema in methodology.md. Use WebSearch/WebFetch; capture competitor prices/plan tiers live with the capture date (shelf price for goods, plan/ACV for software, cost-to-adopt for OSS/service). Source bar (full rules in methodology.md § The source bar): prices from the vendor's own page only; market sizes from named research, never an SEO aggregator alone; pains quoted verbatim from a named venue; load-bearing inputs need 2 sources or an explicit "judgment call" label; floor of ≥1.5 unique URLs per content page with ≥50% primary/named-research — and zero padding URLs.

Phase 1.5 — Ghost deck (dot-dash storyline)

Before rendering a single page, write the headline-only outline: every page as one action-title sentence, in order, plus a one-line sketch of its exhibit. Then run the horizontal-logic test: read the headlines top to bottom — they must read as one persuasive essay (SCQA arc: situation → complication → question → answer). If a headline doesn't advance the argument, the page gets cut or merged now, before any layout work is spent. Draft the §0 governing thought + pillars here too.

Phase 2 — Generate the deck

Copy the canonical engine ~/.claude/skills/mckinsey-deck/assets/deck_engine.py into the project (single source of truth — engine fixes go back to that file, never to a project-local fork). Point it at <brand>-data.json, set BRAND, compose the 6-section build() (the engine ships the renderers + an example build). Render:

bash
python3 deck_engine.py                       # writes <Brand>-Deck.html
"/Applications/Google Chrome.app/Contents/MacOS/Google Chrome" --headless --disable-gpu \
  --no-pdf-header-footer --print-to-pdf="<Brand>-Deck.pdf" "<Brand>-Deck.html"

After EVERY build, assert structure: div diff must be 0 (an unclosed div breaks pagination).

bash
python3 -c "h=open('<Brand>-Deck.html').read();print('div diff:',h.count('<div')-h.count('</div>'))"

Phase 3 — Decision pages (adversarial verify)

The three pages that turn "opportunity scan" into "decision deck": bottom-up market sizing (TAM/SAM/SOM), economics (validate the value/margin claim with the buildup that fits the category — landed COGS for goods, CAC/payback for SaaS, adoption→conversion for OSS), business case (investment, 3 scenarios, payback). The questions are universal; the arithmetic forks by archetype — see methodology.md (§ Decision pages) for each pattern, and set exhibit.boldKeys to mark the answer rows. Generate + verify them with a Workflow pipeline — one analyst agent per page, then an adversarial verifier that re-derives every number. Persist each verified page as _decision_<id>.json.

Phase 4 — Images

List what's missing (cover hero + any product cutouts + optional dividers). Write the handoff with references/image-handoff.md, hand it to the image generator, then wire the returned PNGs into the engine.

Phase 5 — QC (mandatory, self-run)

Run references/qc-checklist.md end to end: render every page, eyeball for overlap / mid-word truncation / >30% whitespace / unblended images, and programmatically verify the source register count. Fix defects, re-render, re-check. Only then present.

Hard design laws (baked in — never violate)

These are user-confirmed preferences; treat as non-negotiable defaults:

  • No eyebrow/kicker labels. No small letter-spaced ALL-CAPS tags above titles or on the cover. The action-title headline carries the meaning.
  • Cover is ultra-minimal: title + one italic subtitle, on the navy hero image. No KPI band, no method/evidence block, no "Prepared ".
  • Action-title headlines: the headline is a full-sentence conclusion, not a topic label.
  • Charts ride beside their analysis. Embed the donut/bar/heatmap in a content page's right rail (essay_slide(side=...)); never put a chart alone on a standalone page.
  • ≥4 charts per deck, each centered-titled and load-bearing. A deck has at least four charts (donut / bar / heatmap mix) that each carry a real finding; chart titles are always centered.
  • Notes pinned to a uniform bottom position (margin-top:auto), same on every page.
  • Nothing overlaps the footer line. .pad reserves bottom clearance.
  • Fill by composition, not by stretching — <30% whitespace. Sparse prose spreads (space-evenly); sparse tables keep their uniform row height (header 34px / rows 52px, deck-wide) and take an extra= stat strip or more researched rows. Never balloon an element to hide thin content.
  • No mid-word truncation. Use the engine's clipw() (word boundary + "…"), never raw [:n].
  • Images: clean cutout on pure white, no shadow, mix-blend-mode:multiply to blend; every image gets an italic caption. Cover/divider images are the only ones on navy.
  • Flat only: no drop shadows, no bevels (navy cover/divider excepted).
  • Self-verify by rendering the PDF, not by trusting the browser tab (an already-open tab won't reload — tell the user to hard-refresh with Cmd+Shift+R).

Quality bar

Match AND exceed a reference McKinsey report on both content density and visual polish. Prose pages use "bold theme + flowing paragraph", not terse bullets. Be honest about assumptions — flag the load-bearing judgment calls rather than hiding them; that honesty is what reads as senior.

Two final gates before delivery:

  • Elevator test — the §0 governing thought + thesis page must sell the recommendation in 30 seconds, standalone. If it needs the rest of the deck to make sense, it isn't the answer yet.
  • Horizontal logic — re-run the headline read-through on the finished deck (QC §C); page edits during build often break the essay.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Mckinsey Market Research Deck AI skill do?

End-to-end playbook for producing a top-tier, McKinsey-style market-research deck (HTML page-turning presentation + print-ready PDF) for a brand or product category. Covers the research methodology (TAM/SAM/SOM bottom-up, Good/Better/Best framework, competitor profiling, customer pain points, unit economics, business case), the locked McKinsey visual design system, a reusable Python deck engine, an adversarial verify workflow for decision-grade numbers, an image-generation handoff, and a full QC checklist. Use when the user asks to "do market research", build a "market research deck / repor...

Why use Mckinsey Market Research Deck on TypingMind?

Because you install it once and use it with any model. Mckinsey Market Research Deck is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Mckinsey Market Research Deck in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/norahe0304-art/30x-mckinsey-research-deck/tree/master/skills/mckinsey-market-research-deck. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Mckinsey Market Research Deck?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Mckinsey Market Research Deck?

As many as you like. As long as a model supports skills, you can use Mckinsey Market Research Deck with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Mckinsey Market Research Deck AI skill free?

Yes. It is published on GitHub by norahe0304-art under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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