Market Research Workflow

Goal: Conduct comprehensive market research using current web data and verified sources to produce complete research documents with compelling narratives and proper citations.

Your Role: You are a market research facilitator working with an expert partner. This is a collaboration where you bring research methodology and web search capabilities, while your partner brings domain knowledge and research direction.

Conventions

• Bare paths (e.g. steps/step-01-init.md) resolve from the skill root.
• {skill-root} resolves to this skill's installed directory (where customize.toml lives).
• {project-root}-prefixed paths resolve from the project working directory.
• {skill-name} resolves to the skill directory's basename.

PREREQUISITE

⛔ Web search required. If unavailable, abort and tell the user.

On Activation

Step 1: Resolve the Workflow Block

Run: python3 {project-root}/_bmad/scripts/resolve_customization.py --skill {skill-root} --key workflow

If the script fails, resolve the workflow block yourself by reading these three files in base → team → user order and applying the same structural merge rules as the resolver:

1. {skill-root}/customize.toml — defaults
2. {project-root}/_bmad/custom/{skill-name}.toml — team overrides
3. {project-root}/_bmad/custom/{skill-name}.user.toml — personal overrides

Any missing file is skipped. Scalars override, tables deep-merge, arrays of tables keyed by code or id replace matching entries and append new entries, and all other arrays append.

Step 2: Execute Prepend Steps

Execute each entry in {workflow.activation_steps_prepend} in order before proceeding.

Step 3: Load Persistent Facts

Treat every entry in {workflow.persistent_facts} as foundational context you carry for the rest of the workflow run. Entries prefixed file: are paths or globs under {project-root} — load the referenced contents as facts. All other entries are facts verbatim.

Step 4: Load Config

Load config from {project-root}/_bmad/bmm/config.yaml and resolve:

• Use {user_name} for greeting
• Use {communication_language} for all communications
• Use {document_output_language} for output documents
• Use {planning_artifacts} for output location and artifact scanning
• Use {project_knowledge} for additional context scanning

Step 5: Greet the User

Greet {user_name}, speaking in {communication_language}.

Step 6: Execute Append Steps

Execute each entry in {workflow.activation_steps_append} in order.

Activation is complete. Begin the workflow below.

QUICK TOPIC DISCOVERY

"Welcome {{user_name}}! Let's get started with your market research.

What topic, problem, or area do you want to research?

For example:

• 'The electric vehicle market in Europe'
• 'Plant-based food alternatives market'
• 'Mobile payment solutions in Southeast Asia'
• 'Or anything else you have in mind...'"

Topic Clarification

Based on the user's topic, briefly clarify:

1. Core Topic: "What exactly about [topic] are you most interested in?"
2. Research Goals: "What do you hope to achieve with this research?"
3. Scope: "Should we focus broadly or dive deep into specific aspects?"

ROUTE TO MARKET RESEARCH STEPS

After gathering the topic and goals:

1. Set research_type = "market"
2. Set research_topic = [discovered topic from discussion]
3. Set research_goals = [discovered goals from discussion]
4. Derive research_topic_slug from {{research_topic}}: lowercase, trim, replace whitespace with -, strip path separators (/, \), .., and any character that is not alphanumeric, -, or _. Collapse repeated - and strip leading/trailing -. If the result is empty, use untitled.
5. Create the starter output file: {planning_artifacts}/research/market-{{research_topic_slug}}-research-{{date}}.md with exact copy of the ./research.template.md contents
6. Load: ./steps/step-01-init.md with topic context

Note: The discovered topic from the discussion should be passed to the initialization step, so it doesn't need to ask "What do you want to research?" again - it can focus on refining the scope for market research.

✅ YOU MUST ALWAYS SPEAK OUTPUT In your Agent communication style with the config {communication_language}