Mine public reviews, app stores, and forums for unmet needs, competitor weaknesses, and switching triggers — with quoted evidence. Use when you want customer voice without waiting on interviews.

Voice of Customer Miner Purpose Mine public customer voice — review sites, app stores, Reddit and practitioner forums, community boards — for unmet needs, competitor weaknesses, and switching triggers: search plan → source sweep → verbatim capture → need themes → so what → next step options. This bridges competitive intelligence and discovery: it delivers customers' exact words without waiting on an interview cycle. But public voice skews toward the angry and the vocal, so every theme it surfaces is a hypothesis to validate , never a verdict — the output's last stop is always a real conversation. Input Works best with: the product(s) or competitor(s) to mine — yours, a rival's, or a set — and the decision this should inform . Also useful: a theme to focus on (onboarding, pricing, reliability) if you have one; otherwise the sweep runs open. Input supplied inline with the invocation — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it against the question budget; don't re ask. Arriving empty handed? That works too. The skill opens with at most 3 questions (whose voice, what decision, theme or open sweep) and proceeds on labeled assumptions if they go unanswered. Example invocation: Mine voice of customer for [Competitor A] and [Competitor B], focus on onboarding — informs whether our Q1 bet is a migration tool. Key Concepts Governing protocol: honors the [ autonomous investigation ](../autonomous investigation/SKILL.md) contract — question budget of 3, search plan gate, Fact/Inference/Assumption labels, Just Enough Mode, stable schema, 4 option Final Step. Discipline: OSINT's review and community layer (see [ intelligence collection disciplines ](../intelligence collection disciplines/SKILL.md)). Theme by need, not by feature. "Exports are broken" is a feature complaint; "I can't get my data where my team works" is the underlying need. Theming by need is the same solution free discipline as JTBD and painstorming — and it's what makes themes portable into discovery. Verbatims are the product. Short, real, quoted customer language with URLs. Verbatims teach persona language: the exact words customers use become interview probes and positioning copy. Never fabricate quotes, ratings, review counts, or reviewer roles. Every source has a known skew. Reviewers skew negative; vendor communities skew loyal; app stores over represent update anger. Note the bias per source — public voice is evidence with a known skew, not ground truth. Honest frequency. Recurring across sources ≠ concentrated in one thread ≠ isolated but vivid . Say which; one articulate ranter is not a theme. When NOT to use: no meaningful public footprint (early stage, niche enterprise) → run [ discovery interview prep ](../discovery interview prep/SKILL.md) instead; you need your users' voice on a private area → mine your own tickets and research; statistical confidence required → this is qualitative theming. Application 1. Credit inline context , then ask only the unanswered questions (max 3): 1. Whose customer voice — yours, a competitor's, or a set? 2. What decision should this inform? 3. Any specific theme to focus on, or open sweep? 2. Show the 3 bullet search plan — which voice sources you'll sweep, how you'll select representative verbatims, how observation will be separated from interpretation. Continue unless revised. 3. Sweep mixed voice sources — review sites (G2, Capterra, TrustRadius), app stores, Reddit and practitioner forums, community boards, social threads — capturing short real quotes with URLs and noting each source's bias. 4. Emit the schema below exactly. Output schema (do not reorder) ~~~markdown Voice of Customer Snapshot 1. Scope Products mined: Decision supported: Sources swept: As of date: 2. Need Themes For each of the top 3 5 themes: Theme: [Underlying need, solution free, 4 to 8 words] Frequency: [recurring across sources / concentrated / isolated] Verbatim: "[short real quote]" — [source, URL] Verbatim: "[short real quote]" — [source, URL] Who says it: [role/segment, if evident — labeled] Reading: [Inference — what this suggests] 3. Competitor Weak Points [Competitor]: [weakness in customers' words; frequency; URL] [Max 5, strongest evidence only] 4. Switching Triggers [What pushes customers off a product; what pulls them; labeled, cited] 5. So What? 3 opportunity hypotheses (phrased as problems, not features) 2 battle card ready weaknesses (with evidence quality noted) 3 assumptions to validate in real interviews Each bullet: label, confidence, URL where relevant. ~~~ A copy/paste fill in version of this schema, with quality checks, lives in [ template.md ](template.md). Final Step (offer exactly 4 options) 1. Generate discovery interview questions from the top theme ([ discovery interview prep ](../discovery interview prep/SKILL.md)) 2. Feed the weaknesses into a competitive battle card ([ battle card builder ](../battle card builder/SKILL.md)) 3. Build an opportunity solution tree from the top hypothesis ([ opportunity solution tree ](../opportunity solution tree/SKILL.md)) 4. Re run scoped to one theme in Verbose Mode Accept 1 , 2 , 3 , 4 , 1 and 2 , Verbose Mode , or a custom path. Examples A theme done right (fictional product, illustrative verbatims): Theme: getting historical data out at contract end Frequency: recurring — 9 reviews across two sites plus a forum thread, past 6 months Verbatim: "export took three support tickets and still dropped custom fields" — [G2 style review, URL] Verbatim: "we stayed a year longer than we wanted because leaving meant losing our audit trail" — [forum thread, URL] Who says it: ops managers at 50 200 person firms — Inference (reviewer titles where shown) Reading: exit friction is functioning as involuntary retention — Inference ; a rival with effortless migration turns this from their moat into their churn event. Notice the theme name contains no feature ("export tool") — it names the need, so discovery can explore solutions the reviews never imagined. See [ examples/sample.md ](examples/sample.md) for a complete worked mining run (fictional FSM software market) where frequency honesty caps a vivid theme at low confidence and each source's bias becomes a reading instruction. [ examples/sample industrial.md ](examples/sample industrial.md) shows the thin voice case — what honest mining looks like when the market barely posts reviews. Common Pitfalls Feature name theming. Clustering by the feature customers blame instead of the need underneath hands your roadmap to the loudest UI complaint. Verbatim laundering. Paraphrasing a review and quoting it. If it has quote marks, it must be a real excerpt at a real URL — this domain's do not invent list exists because fabricated customer quotes are both tempting and toxic. Rant amplification. One vivid one star review presented as a theme. Frequency honesty is the discipline: recurring, concentrated, or isolated — say which. Skew blindness. Reading review sites as a census. The angry and the vocal are over sampled; the satisfied and silent majority never posts. Bias notes per source are mandatory. Skipping the validation handoff. Shipping themes straight into the roadmap. The output's "assumptions to validate in real interviews" section is the bridge to discovery — use it. References [ autonomous investigation ](../autonomous investigation/SKILL.md) (Workflow) — the governing protocol [ intelligence collection disciplines ](../intelligence collection disciplines/SKILL.md) (Component) — OSINT review mining sources and bias tradecraft [ jobs to be done ](../jobs to be done/SKILL.md) (Component) — the solution free framing themes should land in [ discovery interview prep ](../discovery interview prep/SKILL.md) (Interactive) — where the validation happens [ opportunity solution tree ](../opportunity solution tree/SKILL.md) (Interactive) — structures the opportunity hypotheses [ battle card builder ](../battle card builder/SKILL.md) (Workflow) — consumes the weak points Adapted from market intelligence/voice of customer miner prompt.md in the https://github.com/deanpeters/product manager prompts repo.

voice-of-customer-miner