Market Research

Produce research that supports build-or-skip decisions, not research theater.

When to Activate

• evaluating whether a market is worth entering
• sizing a market opportunity
• comparing competitors, adjacent products, or OSS alternatives
• researching a technology, vendor, or infrastructure choice
• pressure-testing a thesis before building or entering a market
• validating pricing before writing code

Research Standards

1. Every quantitative claim must have a [SOURCE: ...] tag or be labeled [ESTIMATE].
2. Prefer recent data. Flag anything older than 18 months as [STALE].
3. Steel-man the opposite conclusion.
4. Translate findings into a decision, not just a summary.
5. For each key assumption, state what evidence would falsify it. Example: "Assumption: ML engineers will pay for managed experiment tracking. Kill condition: >60% of community threads recommend self-hosted and cite cost as primary reason."

Research Modes

Default to Market Sizing + Competitive Landscape. Add other modes only when the question demands them.

Market Sizing

Three lenses, plain language:

• TAM (Total Addressable Market) — everyone who could theoretically use this. The ceiling.
• SAM (Serviceable Addressable Market) — the slice you can actually reach with your product's scope and geography.
• SOM (Serviceable Obtainable Market) — what you can realistically capture in 1-2 years given your distribution, pricing, and team size.

For each:

• state the number and the assumption behind it
• use top-down data (reports, public datasets) cross-checked with bottom-up math (realistic customer counts x price)

Anchor SOM to the go-to-market motion:

• Solo/indie: "How many paying users can I reach through channels I can operate alone, at what price?"
• B2B/enterprise: "How many teams can I reach given sales cycle length, integration complexity, and deal size?"

Competitive & OSS Landscape

Collect:

• product reality, not marketing copy
• OSS alternatives (GitHub activity, contributor health, license, adoption curve)
• funding history if public (signals runway and priorities, not a scorecard)
• traction signals (users, revenue, community size) if public
• pricing and packaging
• strengths, weaknesses, and positioning gaps
• build vs. buy vs. fork trade-off for the user's context

Distribution:

• where target users already congregate (communities, forums, marketplaces, conferences)
• realistic customer acquisition cost for the user's go-to-market motion
• existing distribution moats (integrations, marketplaces, API ecosystems)

Pricing Analysis

Requires user-provided data (links, screenshots, forum threads) for specifics beyond known market structure.

Analyze:

• what do people currently pay for similar solutions?
• pricing tiers and anchoring in the category
• free vs. paid boundary — what features cross the pay threshold?
• for B2B: typical contract size, procurement friction, budget owner

Technology / Vendor Research

Collect:

• how it works (architecture, key trade-offs)
• adoption signals and ecosystem health
• integration complexity
• lock-in risk, data portability, and exit cost
• security, compliance, and operational burden
• cost trajectory at scale

Output Format

Default structure:

1. Decision summary — build, skip, or investigate further, in one paragraph
2. Key findings — with [SOURCE: ...] or [ESTIMATE] tags on quantitative claims
3. Assumptions & falsifiability — each key assumption with its kill condition
4. Risks and counterarguments — steel-manned opposing view
5. Recommendation — concrete next step
6. Sources — linked and dated

Quality Gate

Before delivering:

• all numbers are sourced or labeled as estimates
• stale data is flagged
• the recommendation follows from the evidence
• at least one steel-manned counterargument is included
• key assumptions have explicit kill conditions
• the output makes a build-or-skip decision easier