brand reputation analyzer

    1

    Converts unstructured reviews and mentions into evidence-calibrated reputation intelligence and operational action plans.

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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIMuseMuseDotsDots+21 more

    brand reputation analyzer

    Example session with this skill installed

    Analyze these recent reviews for our 'SwiftDash' delivery app: 'Late again, third time this week' (1 star), 'Love the interface but the driver got lost' (3 stars), and 'Best app ever' (5 stars).

    • Read your context and instructions
    • Compiled the brand reputation analyzer

    I have analyzed the 3 recent feedback items for SwiftDash. The sample is small and self-selected, indicating high uncertainty for broader brand health, but it surfaces specific operational friction in fulfillment and navigation.

    Reputation Intelligence Summary

    Scope and Data Quality

    The analysis covers 3 reviews for SwiftDash. Sources include app store/marketplace feedback. The sample size is extremely low, meaning patterns are preliminary and should be treated as isolated or emerging rather than corroborated.

    Executive Readout

    Positive sentiment is driven by UI design and general brand affinity. However, significant negative sentiment exists regarding delivery reliability and driver navigation. A high-intensity complaint regarding repeated lateness suggests a potential operational failure in the fulfillment chain that requires immediate investigation despite the low sample volume.

    Signal Normalization

    SignalNormalized topicSentimentIntensityEvidence levelReferences
    "Late again, third time this week"Delivery LatencyNegativeHighIsolated (Recurring for user)Review 1
    "Love the interface"App UI/UXPositiveModerateIsolatedReview 2
    "Driver got lost"Driver NavigationNegativeModerateIsolatedReview 2
    "Best app ever"General SatisfactionPositiveHighIsolatedReview 3

    Complaints and Operational Patterns

    PriorityThemeEvidenceCustomer impactLikely operational areaConfidence
    1Delivery ReliabilitySingle report of 3x latencies in one week.High (Churn risk)Logistics / FulfillmentLow
    2Navigation AccuracyReport of driver getting lost.ModerateGIS / Routing APILow

    Emerging Risks

    Risk: Delivery Consistency.

    Status: Watch.

    Trigger: User reported 3 incidents in one week.

    Consequence: Rapid churn if the "Swift" brand promise is violated.

    Action: Audit courier transit times for that user's region.

    Recommended Actions

    1. Immediate response: Contact the user from Review 1 to investigate the specific route delays.

    Operational improvement: Review driver-side navigation handoff to ensure addresses are passing correctly to maps.
    3.

    Evidence-gathering: Monitor "latency" keywords over the next 14 days to see if this is a localized or systemic spike.

    Monitoring Plan

    • Metric: Mentions of "late" or "lost" per 100 reviews.
    • Threshold: Escalate to Ops if "late" mentions exceed 5% of weekly feedback.
    • Cadence: Weekly review of negative sentiment topics.

    Next steps

    • Provide a larger CSV export of reviews from the last 30 days to validate the latency trend.
    • Check if Review 1 and Review 2 originated from the same geographic zone.

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

    About this skill

    The problem

    Unstructured feedback from reviews, social media, and support tickets is often noisy, repetitive, and contradictory. Teams struggle to distinguish isolated anecdotes from systemic operational failures, leading to misprioritized fixes and reactive brand management.

    What it does

    • Normalizes messy feedback by standardizing synonyms and separating multi-issue comments into distinct topics.
    • Classifies sentiment and intensity at the topic level to capture nuance in mixed reviews.
    • Weights evidence by frequency, recency, and source quality to separate noise from corroborated patterns.
    • Identifies emerging risks and operational bottlenecks before they escalate into reputational crises.
    • Generates prioritized action plans categorized by immediate response, product improvements, and monitoring thresholds.

    Why this beats prompting it yourself

    Generic prompts often suffer from "averaging bias" or hallucinate trends from small samples. This skill uses a rigorous evidence-calibration framework that explicitly flags sampling bias and requires corroboration across multiple dimensions before labeling a pattern as strong.

    Use cases

    • Audit product launches by tracking emerging complaints across app stores and social mentions.
    • Monitor hospitality or retail service quality across different regions and segments.
    • Analyze competitor sentiment to identify gaps in their customer experience or reliability.
    • Convert high volumes of support transcripts into structured operational intelligence for product teams.

    Known limitations

    Requires qualitative feedback as input. Cannot infer total customer prevalence without a known denominator for the data sample.

    Requires qualitative text input. Cannot determine statistical prevalence in a total customer base without external volume data. Does not provide legal or factual determinations beyond provided text.

    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

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    3. 3

      Ask for what you need

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

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    Listed2 months ago

    What's inside

    Frequently Asked Questions

    Unlike basic sentiment tools that provide simple scores, this skill uses an evidence-calibration framework to filter out 'noise' and identify corroborated operational patterns, turning messy reviews into specific, prioritized action plans.

    This skill is designed to work with any large language model (LLM) that supports advanced reasoning and structured data processing; it includes a configuration guide for seamless integration with your existing AI agent workflow.

    You will receive the complete skill configuration, including the proprietary evidence-calibration logic, data normalization protocols, and template-ready output schemas for operational reporting.

    No coding is required; you simply import the skill into your agent's environment and map your feedback sources (like CSVs or API feeds) to the skill’s input fields to begin generating intelligence.

    The skill performs best with qualitative text data, such as customer reviews, social mentions, and support tickets, though it requires a representative sample to accurately identify systemic trends rather than isolated incidents.

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