Extract customer insights from interviews and online sources

Acts as an expert customer researcher whose goal is uncovering what customers actually think, feel, say and struggle with, so positioning, product and copy are grounded in reality rather than assumption. Before starting, it checks for existing product-marketing context (.agents/product-marketing.md, an agent's local skill directory, or the legacy product-marketing-context.md filename) and uses it to skip questions already answered.

It runs in two modes, often combined on the same engagement. Mode 1 analyzes existing research assets already on hand (transcripts, surveys, reviews, tickets) to extract signal. Mode 2 goes and finds research from online sources (Reddit, G2, forums, communities, review sites), knowing where to look and what to extract.

In Mode 1, different asset types get different treatment. Customer interview and sales-call transcripts are mined for pains, triggers, desired outcomes, language used, objections and alternatives considered, watching for the moment they decided to look for a solution, what they tried before, and what success looks like to them. Survey results get segmented by customer tier, use case or tenure before drawing conclusions, flagging where open-ended answers conflict with multiple-choice answers, and identifying the roughly 20% of responses carrying the most useful signal. Customer support conversations are mined for recurring complaints, confusion points, feature requests and "I wish it could..." language, categorized before analysis so bugs, confusion, missing features and expectation mismatches aren't treated as equal signal. Win/loss interviews and churned-customer notes ask what tipped a win, what almost lost it to a competitor, and whether a loss or churn was about price, features, fit or timing, segmented by reason rather than averaged. NPS responses treat passives and detractors as higher signal than promoters for improvement work, and pair scores with verbatims since a 9 with a specific complaint beats a 10 with no comment.

An extraction framework applies to every asset: Jobs to Be Done (the functional task, the emotional feeling wanted, and the social perception wanted), Pain Points (prioritizing those mentioned unprompted and in emotional language), Trigger Events (common ones include team growth, a new hire, a missed target, an embarrassing incident, or a competitor's move), Desired Outcomes (captured as exact quotes, not paraphrases), Language and Vocabulary (exact customer phrasing, valuable for copy, "we were drowning in spreadsheets" beats "manual process inefficiency"), and Alternatives Considered (including doing nothing, hiring someone, or building internally).

Synthesis across assets clusters similar pains, outcomes and triggers by theme, scores each theme by frequency and intensity, segments patterns by customer profile (company size, role, use case, tenure), identifies 5-10 verbatim "money quotes" per theme, and flags contradictions between what customers say and what they do. Every insight gets a confidence label before it's presented: High requires the theme in 3+ independent sources, mentioned unprompted, consistent across segments; Medium is 2 sources, or only prompted, or limited to one segment; Low is a single source that could be an outlier needing validation. Sources from the last 12 months are weighted more heavily since markets shift. Sample-bias checks are applied throughout: online reviewers skew toward power users with strong opinions, support tickets skew toward problems rather than value, and Reddit skews technical and skeptical relative to mainstream buyers. Personas or messaging conclusions are never drawn from fewer than 5 independent data points per segment.

Mode 2 treats online communities as where customers speak unfiltered, choosing sources by ICP type (B2B SaaS/technical buyers favor Reddit role-specific subs, G2/Capterra, Hacker News, LinkedIn, Indie Hackers and SparkToro; SMB/founders favor Reddit entrepreneur/small-business subs, Indie Hackers, Product Hunt and Facebook Groups; developer/DevOps favors r/devops, r/programming, Hacker News, Stack Overflow and Discord; B2C/consumer favors 1-3 star app-store reviews, hobby/lifestyle subreddits, and YouTube/TikTok/Instagram comments; enterprise favors LinkedIn, analyst reports, G2's enterprise filter and job postings), with detailed per-platform playbooks in references/source-guides.md. A quick decision guide routes by need: G2/Capterra for an existing product category, SparkToro for finding where an audience spends time, Reddit and YouTube comments for raw language, LinkedIn posts/job postings/Hacker News Ask-HN threads for trigger events, and competitor 4-star G2 reviews or Product Hunt discussions for competitive intel. Every extracted item captures its source (platform, thread URL, date), an exact verbatim quote, its context, its sentiment, a theme tag, and any customer-profile signals, then synthesizes into a fixed template ranking themes by frequency times intensity with a summary, frequency, intensity and representative quotes per theme.

Personas are built only from research, never invented, requiring at least 5-10 data points from a consistent segment before one is created, following a fixed structure covering profile, primary job to be done, trigger events, top pains, desired outcomes, objections and fears, alternatives considered, key vocabulary and how to reach them. Anti-patterns to avoid: cute persona names unless the team finds them genuinely helpful, averaging across segments (a persona representing everyone represents no one), inventing details instead of leaving gaps blank, and skipping the quarterly revisit personas need as the market evolves.

Six deliverable formats are on offer, chosen with the user before generating output: a research synthesis report, a VOC quote bank organized by theme, one to three persona documents, a jobs-to-be-done map by segment, a competitive-intelligence summary, and a research gap analysis. When context is unclear, it leads with the two most essential clarifying questions (the goal, and what research already exists) before following up on target segment, product, and desired deliverable, rather than asking all five at once. It hands off to related skills for the next step: copywriting for research-informed copy, cro for VOC-driven page optimization, competitors for a comparison page, churn-prevention for a churn strategy, ads for paid-ad planning, cold-email for pain/trigger-based cold email, prospecting for translating research into an ICP, content-strategy for topic planning, and marketing-plan for rolling research into a full plan.

Use it to conduct, analyze or synthesize customer research, including ICP research, talking to customers, analyzing transcripts or surveys, support-ticket analysis, or voice-of-customer work. Skip it when the task doesn't clearly match this upstream context, and never treat its output as a substitute for environment-specific validation, security review, or user approval before destructive or costly actions.

Input is existing research assets (transcripts, surveys, tickets, reviews) and/or online community sources to mine, plus the target segment and desired deliverable. Output is one of six formats: a synthesis report, a VOC quote bank, persona documents, a jobs-to-be-done map, a competitive-intelligence summary, or a research gap analysis, each insight labeled with a confidence level.

Reads existing product-marketing context files when present and consults references/source-guides.md for per-platform search operators and extraction playbooks; hands off to the copywriting, cro, competitors, churn-prevention, ads, cold-email, prospecting, content-strategy and marketing-plan skills.

Marketers, product teams and founders who need positioning, product decisions or copy grounded in what customers actually say rather than assumption.