Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add norahe0304-art/30x-mckinsey-research-deck --skill mckinsey-market-research-deckgit clone --depth 1 https://github.com/norahe0304-art/30x-mckinsey-research-deckWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/norahe0304-art/30x-mckinsey-research-deck/mckinsey-market-research-deck)<a href="https://agentmods.dev/skills/norahe0304-art/30x-mckinsey-research-deck/mckinsey-market-research-deck"><img src="https://agentmods.dev/badge/skills/norahe0304-art/30x-mckinsey-research-deck/mckinsey-market-research-deck/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/norahe0304-art/30x-mckinsey-research-deck/mckinsey-market-research-deck"><img src="https://agentmods.dev/badge/skills/norahe0304-art/30x-mckinsey-research-deck/mckinsey-market-research-deck.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00188 | $0.02285 |
| Opus 5 | $0.00094 | $0.01143 |
| Sonnet 5 | $0.00038 | $0.00457 |
| Haiku 4.5 | $0.00019 | $0.00229 |
Grade A, and why
mckinsey-market-research-deck scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 149 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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 containreferences/design-system.md— the locked visual contract (tokens, page types, layout laws)../mckinsey-deck/assets/deck_engine.py— the canonical engine (owned by themckinsey-deckstyle skill; this skill consumes it — never fork a local copy, that's how drift starts)references/qc-checklist.md— the self-verify pass before deliveryreferences/image-handoff.md— the template that hands product/cover images to an image generator
The 7-page spine (always)
- 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. - Market Overview — size, growth, channel, the structural shift
- Brand Landscape — Good/Better/Best ladder + brand-by-brand profiles
- Product Categories — per-subcategory competitor price ladder + pain points + the brand's lineup
- Customer Pain Points — sourced failure modes, each one a selling-point opening
- Opportunities — pain points → product direction
- 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.
What ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 149 lines · 188 tokens per session scan A bb2d41d24b77
mckinsey-market-research-deck is a skill published in the GitHub repository norahe0304-art/30x-mckinsey-research-deck (39 stars, last pushed 2mo ago), licensed MIT. It adds 188 tokens to every session and 2,285 once invoked, about $0.0009 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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