customer interview insight synthesizer

    1

    Transform messy interview transcripts into traceable, structured research artifacts with de-identified evidence IDs.

    $5

    Secure checkout via Stripe

    Money-back guarantee

    Converts to your local currency at checkout

    Security scanned

    Works with the AI Agents you already use

    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    customer interview insight synthesizer

    Example session with this skill installed

    Analyze these three interview transcripts for our new project management tool. Focus on how they handle task prioritization and identify any gaps in our current understanding.

    • Read your context and instructions
    • Compiled the customer interview insight

    I have processed the transcripts for P01, P02, and P03. I have de-identified the participants, categorized 14 unique evidence units, and mapped three primary themes regarding task prioritization.

    User Research Synthesis: Project Management Tool

    Participant Overview

    Evidence IDSegmentSourceNotes
    P01Engineering Leadtranscript_01.txtFocuses on sprint velocity
    P02Product Managertranscript_02.txtFocuses on stakeholder alignment
    P03Freelance Designertranscript_03.txtFocuses on deadline visualization

    Evidence Map

    ThemeEvidence IDsStrength
    Manual WorkaroundsE001, E005, E009STRONG
    Visual Hierarchy NeedsE002, E011MODERATE
    Notification FatigueE004, E007, E012STRONG

    Key Findings (Selected)

    Behavior (E001, E005): P01 and P02 both maintain a separate "Urgent" spreadsheet outside the tool because the internal priority flags are "too noisy."

    • Opinion (E009): P03 stated that "color-coding is more important than a numerical priority score."

    Interpretation: Users distrust the tool's built-in priority logic, leading to data fragmentation in external sheets.

    Contradictions

    • P01 vs P03: P01 requires strict stack-ranking (E003), while P03 prefers fluid, visual grouping (E010).

    Opportunity Hypotheses

    H01: IF we implement a drag-and-drop "Custom Sort" view, THEN users like P01 and P02 will stop using external spreadsheets BECAUSE they currently lack a way to override system-generated priorities (E001, E005).

    Gap Register

    • G01: We lack evidence on how users prioritize tasks across multiple concurrent projects.
    • G02: No data on how automated priority triggers (e.g., Jira sync) affect user trust.

    Next steps

    1. Review the Opportunity Hypotheses with the engineering team to assess feasibility of H01.
    2. Schedule two additional interviews with Agency Owners to address G01.
    3. Archive the Raw Evidence Index in the project repository for future audit.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    The problem

    User research often results in messy transcripts and vague summaries that blur the line between what users actually did and what the researcher thinks they meant. This lack of rigor leads to product decisions based on misinterpreted opinions or "cherry-picked" quotes rather than traceable behavioral data.

    What it does

    • Extracts raw interview data into a structured registry with stable, de-identified evidence IDs.
    • Categorizes every finding strictly as BEHAVIOR, OPINION, or INTERPRETATION to prevent analytical bias.
    • Maps contradictions and negative cases where participants disagree, avoiding false consensus.
    • Generates testable opportunity hypotheses directly linked to specific evidence IDs for full auditability.
    • Maintains a formal Gap Register to track unknowns and underrepresented segments.

    Why this beats prompting it yourself

    Generic prompts usually summarize away the nuances, losing the raw evidence needed for defensible product specs. This skill enforces a systematic research methodology that separates observation from inference, ensuring you don't over-quantify qualitative data or ignore outliers that could invalidate your strategy.

    Use cases

    • Synthesizing 10+ hours of discovery call transcripts into a prioritized opportunity backlog.
    • Auditing customer feedback to identify where different user segments have conflicting needs.
    • Building a traceable research repository where every product requirement can be tracked back to a specific user quote or action.
    • Validating internal assumptions by explicitly looking for negative cases in existing interview notes.

    Known limitations

    Does not perform statistical significance testing. Quantification is restricted to descriptive counts of the provided sample.

    Quantification is restricted to descriptive counts of the provided sample. Does not perform statistical significance testing.

    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before any payment.

    Reviews

    No reviews yet

    Be one of the first to try it. Every listed skill goes through the trust checks below.

    Security scanned

    Checked by our 8-point scan before listing

    Fresh listing

    Recently published to Agensi

    Money-back guarantee

    Doesn't do what its page promises? You get your money back.

    Trust & safety

    Security scan

    Security scan passed 2 months ago

    Permissions

    No special permissions declared or detected

    Listed2 months ago

    What's inside

    Frequently Asked Questions

    This skill uses a rigorous methodology to separate raw BEHAVIOR from OPINION and assigns de-identified evidence IDs to every insight, ensuring every product decision is backed by a traceable audit trail rather than vague summaries.

    The skill works with any standard text-based interview transcript or discovery call notes; simply upload your raw text files to the agent to begin the extraction and categorization process.

    Yes, the skill is designed to analyze multiple sessions simultaneously to identify patterns, map contradictions between participants, and flag negative cases where user needs conflict.

    You receive the full skill configuration for your AI agent, including the proprietary logic for evidence ID tagging, the Gap Register framework, and the three-tier categorization system.

    Buyers receive lifetime access to the current version of the skill, and any future logic optimizations or compatibility updates are automatically available through your marketplace dashboard.

    While the skill excels at identifying patterns and frequency of feedback, it is a qualitative tool and does not perform statistical significance testing or complex quantitative modeling.

    New in Agents & Orchestration