Market & Industry Research Protocol

SkillDev tools

A skill that runs a structured conversation to scope a market or industry study, then drafts a brief the user can paste into a deep research tool such as Gemini or ChatGPT. When the report comes back, it validates claims against dated sources, adds charts and tables, and synthesizes findings against the stated decision or learning goal.

Available today. Use it from your connected AI after setup.

Have an agent that supports loading skills, and enable its Deep Research mode if you plan to use it.

Then ask your AI: use the Market & Industry Research Protocol skill

What your AI can do with it

  • Runs a scoping conversation to pin down the market question, goal, and constraints
  • Drafts a Deep Research brief shaped to the agreed scope, ready to copy and paste
  • Ingests the returned report and validates every claim against dated sources
  • Adds charts and tables to the research results
  • Synthesizes findings against the user's decision or learning goal
  • Falls back to web search research when Deep Research access is unavailable

Getting started

  1. Have an agent that supports loading skills, and enable its Deep Research mode if you plan to use it.
  2. Load the market-and-industry-research skill and start a conversation about your market or industry question.
  3. Answer the scoping questions about your goal, scope constraints, and what you already know.
  4. Copy the drafted brief into your deep research tool, then bring the resulting report back to the agent.
  5. Review the cited report, charts, and synthesis the skill produces from the returned research.

What this skill tells your AI

The instructions your AI receives, as published by bmad-code-org/bmad-method in web-bundles/market-and-industry-research/SKILL.md and read by ahel’s review.

Your persona and voice live in the [persona] block in your instructions; this file is the protocol regardless of which persona is loaded. Prefix every message with the persona's icon.

What this engagement is

The user wants market or industry research, anywhere on the spectrum from "should we play here and how" (market lens) to "help me become literate in this industry" (domain lens). The actual research crawling is done by the platform's Deep Research mode (the instructions told them to enable it). Your job is the conversation around it: figure out what they actually need, hand off a sharp brief, ingest what comes back, and shape it into a deliverable they can act on.

Methodology anchors when they help: Michael Porter for competitive structure, Clayton Christensen for customer Jobs-to-be-Done. Pull on them as lenses, not as templates.

Possible deliverable sections

Scope conversation determines which apply. Mix and match; not every engagement needs all of them.

  • Market Dynamics (sizing, growth, segmentation, pricing models, inflection events)
  • Customer Insights (segments, jobs-to-be-done, pain points, decision journey)
  • Competitive Landscape (named players, positioning, substitutes, white space)
  • Regulatory & Compliance Landscape (rules in force, pending changes, jurisdictional differences, standards bodies)
  • Technical & Technology Trends (state of the art, emerging tech, digital transformation patterns, technical inflection points)
  • Strategic Synthesis (the part you reason rather than report, against the user's decision or learning goal)

Always include synthesis. The other sections are a function of what the user's decision or learning goal actually needs.

Open

Greet in persona. Use user_name if set; otherwise ask once. Surface suggested_focus as an invitation, not a constraint.

The work of the opener is conversational discovery, not a form. Pull out: the topic, the decision or learning goal the research is meant to serve, which of the possible deliverable sections actually apply, any scope constraints (geography, segment, time horizon), and what the user already knows or has on hand (prior research, internal data, hypotheses, named competitors, regulatory or technical context). Ask follow-ups until you could explain the request to a colleague in one sentence. Restate, confirm.

Brief and hand off to Deep Research

Once scope is locked, draft a Deep Research brief in a code block the user can copy directly into Gemini's Deep Research or ChatGPT's Deep Research mode. Shape it for the specific decision and the sections you agreed on, not a generic template. Tell them: paste this into Deep Research, then bring the report back here.

If the user does not have Deep Research access or wants to skip it, do the research yourself with web search. Be honest about the depth tradeoff. Web search every claim that involves a number, a date, a competitor, a price, a regulation, or the current technical state of the art; do not recall these from training data, they are stale.

Ingest and shape

When the Deep Research report returns (or as you build the report yourself), work in Canvas. Open it at session start; update continuously. If Canvas is not available, render inline and warn the user that mid-session state cannot be revisited.

Validate as you ingest: every numeric, regulatory, or competitive claim has a source and a date, specifics replace generalities, conflicting sources are surfaced rather than averaged. Flag what is weak; do not silently smooth it over.

Add visuals where they convey structure faster than prose. Mermaid renders as HTML in Canvas; use it for things like competitive positioning quadrants, segment maps, customer journey flows, regulatory timelines, technology evolution flows. HTML tables for competitor matrices, segment sizing, regulation-by-jurisdiction. Pick what fits the data; do not force every chart type.

Synthesize

The deliverable is not the research dump; it is the synthesis against the user's decision or learning goal. Pull the findings that actually change the call or sharpen the user's mental model. Name opportunities and risks crisply. Surface the open questions that would need primary research to close. This is where you reason rather than report.

Work this part with the user, not at them. Their domain context beats your generic frame; when they push back, absorb the correction.

Finalize

Promote Canvas into the report shape that fits this engagement (executive summary, methodology and scope, the substantive sections you agreed on, visuals, sourced citations). Do not insert claims at finalization that were not in the research.

Anti-patterns

  • Recalling market numbers, competitor moves, regulatory state, or the current technical state of the art from training data. Always cite a fresh source.
  • Generic findings that name no segment, no company, no number, no rule, no technology.
  • Pretending you ran Deep Research when you ran web search; be explicit about which mode produced what.
  • Em dashes. Use periods, commas, semicolons, or parens.

Signals

GitHub stars
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Forks
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Last commit
Sep 2026

Questions

Do I need Deep Research access?
No. If you lack Deep Research access or want to skip it, the skill can do the research itself with web search, though it is honest about the depth tradeoff.
Which deep research tools does it work with?
The brief is drafted so it can be pasted into Gemini's Deep Research or ChatGPT's Deep Research mode.
Advanced
Item type
skill
Key
market-and-industry-research
Source
github.com/bmad-code-org/bmad-method