Market & Industry Research Protocol
SkillDev toolsThe market-and-industry-research skill is a tool for AI agents that supports agile-driven development workflows. It helps your agent plan and build software projects by applying a breakthrough method for agile AI driven development. This skill is useful when you need structured guidance for iterative planning and execution.
Use Market & Industry Research Protocol in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add Market & Industry Research Protocol and connect your AI. About a minute.
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Then ask your AI: use the Market & Industry Research Protocol skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; ahel provides instructions and does not run this skill.
No other account needed.
Ensure your AI agent supports skills.
What your AI can do with it
- Run agile-driven development workflows for planning software projects
- Support building software projects with iterative planning
- Apply a breakthrough method for agile AI driven development
Getting started
- Ensure your AI agent supports skills.
- Add the market-and-industry-research skill to your agent's configuration.
- Configure the skill according to your project needs.
- Invoke the skill when planning or building software projects.
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
- 54k
- Forks
- 6k
- Last commit
- Oct 2026
Questions
- What kind of tool is this?
- It is a skill for AI agents that enables agile-driven development workflows for planning and building software projects.
- What does it do for me?
- It lets your agent run agile-driven development workflows, helping you plan and build software projects using a breakthrough method for agile AI driven development.
- How do I start using it?
- Ensure your agent supports skills, add this skill to its configuration, configure it for your project, and invoke it when planning or building software.
- Does it work with any AI agent?
- The skill requires an AI agent that supports skills. Specific compatibility depends on your agent's capabilities.
- Can it help with non-software projects?
- The skill is designed for planning and building software projects. It may not be suitable for other types of projects.
Advanced
- Item type
- skill
- Key
market-and-industry-research- Source
- github.com/bmad-code-org/bmad-method
github.com/bmad-code-org/bmad-method
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