Conversation Data Quality Analyzer

Analyze customer conversation notes or transcripts after a meeting to classify every statement as fact, compliment, fluff, or idea — separating real signal from noise. Use this skill whenever the user wants to review interview notes, check whether a customer call produced reliable data, figure out if enthusiastic feedback was genuine interest or polite lies, identify bad data patterns in a transcript, audit whether a conversation that "went great" actually produced usable facts, or suspects they are collecting compliments instead of validated evidence — even if they don't mention "data quality" or "bad data." Do NOT use this skill to write or improve questions before a conversation (use conversation-question-designer) or to evaluate whether a meeting produced real commitment signals like time, reputation, or money (use commitment-signal-evaluator).

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Install

Claude CodeClawhHubnpxCursorCodexGemini CLI

1. Add marketplace

› /plugin marketplace add bookforge-ai/bookforge-skills

2. Install plugin

› /plugin install the-mom-test@bookforge-skills

3. Use the skill

› /conversation-data-quality-analyzer

CC-BY-SA · Open sourceGitHub

Source Book

The Mom Test

Rob Fitzpatrick

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Customer Discoveryhybrid

Commitment Signal Evaluator

Evaluate whether a customer meeting produced real interest or just polite enthusiasm by classifying commitment signals into time, reputation, and money currencies. Use this skill after any customer conversation, product demo, sales call, or pitch where the user wants to know if the meeting actually advanced the deal, asks "was that a good meeting" or "how did it go" or "did that go well," says a meeting "went great" but nothing happened afterward, received enthusiastic feedback and wants to know if it is real, wonders whether someone is actually interested or just being polite, heard "I would definitely buy that" and wants to know if it means anything, wants to distinguish real leads from false-positive prospects (zombie leads), needs to score a pipeline of prospects for conversion likelihood, wants to detect polite rejections disguised as enthusiasm (compliment-stall pattern), or wants to identify early evangelists in their prospect pool — even if they don't explicitly mention "commitment signals" or "meeting evaluation." This skill evaluates meeting OUTCOMES and prospect INTEREST, not conversation data quality (use conversation-data-quality-analyzer) or conversation logistics (use conversation-format-selector).

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Conversation Format Selector

Choose the right conversation format — casual chat, scheduled meeting, or phone/video call — for a customer discovery interaction. Use this skill whenever the user is deciding between a formal meeting and a casual conversation, wondering how long customer interviews should be, unsure whether to meet in person or do a phone or video call, preparing to talk to potential customers at a conference or event or meetup, asking how to approach someone at a networking event or industry gathering, spending too much time scheduling formal meetings and not getting enough conversations done (the meeting anti-pattern), defaulting to hour-long Zoom calls for every customer interaction, planning the logistics or setting or duration of a customer conversation, or wondering whether a conversation should be structured or informal — even if they don't explicitly mention "format" or "meeting type." This skill is about HOW to have the conversation (format, duration, setting, formality level), not about finding people to talk to (use conversation-sourcing-planner) or what questions to ask (use conversation-question-designer).

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Structure the before-and-after process around customer conversations so learning actually reaches the whole team. Use this skill when the user needs to prepare a team for a batch of customer conversations, set up pre-conversation learning goals, create a note-taking system for customer interviews, review and categorize conversation notes using signal symbols, run a post-conversation team review, share customer insights across the team, diagnose whether conversations are producing real learning or just going through the motions, fix a learning bottleneck where one person hoards all customer insights, their team keeps having conversations but nothing changes or plans never update, or a co-founder or teammate is out of the loop on customer feedback — even if they don't explicitly say "learning process" or "team review." Do NOT use for analyzing a specific transcript for data quality (use conversation-data-quality-analyzer) or evaluating whether a prospect gave a real commitment (use commitment-signal-evaluator).

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Write, rewrite, or audit customer interview questions so they extract honest behavioral data instead of false validation. Use this skill whenever the user needs to prepare a conversation script for customer discovery, draft questions for an upcoming interview, fix questions that keep producing useless or vague answers, rewrite biased or leading questions into past-focused behavior-revealing ones, check whether their interview questions will trigger compliments instead of facts, or build a question list from learning goals — even if they don't mention "question design" or "The Mom Test." Do NOT use this skill to analyze conversation notes after a meeting (use conversation-data-quality-analyzer) or to decide which questions matter most strategically (use question-importance-prioritizer).

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