Voice Of Customer Miner Agent Skill

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

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).

• 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 instead; you need *your* users'

voice on a private area → mine your own tickets and research; statistical confidence required →

this is qualitative theming.

Application

• Credit inline context, then ask only the unanswered questions (max 3):
• Whose customer voice — yours, a competitor's, or a set?
• What decision should this inform?
• Any specific theme to focus on, or open sweep?
• 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.

• 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.

• 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.

Final Step (offer exactly 4 options)

• Generate discovery interview questions from the top theme (discovery-interview-prep)
• Feed the weaknesses into a competitive battle card (battle-card-builder)
• Build an opportunity solution tree from the top hypothesis (opportunity-solution-tree)
• 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 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

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 (Workflow) — the governing protocol
• intelligence-collection-disciplines (Component) — OSINT review-mining sources and bias tradecraft
• jobs-to-be-done (Component) — the solution-free framing themes should land in
• discovery-interview-prep (Interactive) — where the validation happens
• opportunity-solution-tree (Interactive) — structures the opportunity hypotheses
• battle-card-builder (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.