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Muse
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Grok
Grok Bot+18 agentsCustomer Complaint Root-Cause Skill
Analyze customer complaint history to identify root causes and generate prioritized corrective action plans.
$9.99
Customer Complaint Root-Cause Skill
Example session with this skill installed
Analyze these 50 complaints from the Q3 billing cycle. Identify the top 3 themes and suggest a corrective action plan for the most frequent issue.
- Read your context and instructions
- Compiled the customer complaint root-cause
I have analyzed the 50 billing complaints from Q3. Data normalization identified 48 unique incidents and 2 duplicates. The analysis shows a high concentration of issues related to "Subscription Overcharges" following the August pricing update.
Complaint Analysis Report: Q3 Billing
| Theme | Count | % of Total | Severity | Status |
|---|---|---|---|---|
| Subscription Overcharge | 22 | 44% | High | Recurring |
| Refund Delay | 12 | 24% | Medium | Improving |
| Failed Promo Code | 9 | 18% | Low | Stable |
| Other / Ambiguous | 7 | 14% | N/A | - |
Root-Cause Hypothesis: Subscription Overcharges
- Symptom: Customers report being billed the "Pro" rate despite downgrading to "Basic."
- Likely Process Stage: Payment and Billing (specifically the plan-migration webhook).
Potential Root Cause: A synchronization lag between the UI downgrade event and the billing engine subscription state.
Corrective and Preventive Action Plan
| Issue | Evidence | Corrective Action | Preventive Action | Success Measure |
|---|---|---|---|---|
| Plan Mismatch | 22 logs show state drift | Manual credit adjustment for affected IDs | Implement idempotent webhook retries | Zero drift in weekly audit |
| Refund Latency | 12 complaints > 14 days | Batch process pending refunds | Automate refund triggers on plan cancellation | < 3 days avg resolution |
Next steps
- Audit the plan-migration webhook logs to confirm the state drift hypothesis.
- Verify the count of customers who downgraded in August but were billed at the higher rate.
- Apply the suggested credit adjustments to the identified 22 records.
Connects securely to your tools. The creator never sees your data.
About this skill
High complaint volumes hide the signals you need to fix your operations. This skill analyzes customer complaint history to isolate recurring patterns, map issues to specific process failures, and build a prioritized corrective action plan.
What it does
- Data Normalization standardizes inconsistent complaint categories and terminology across large datasets.
- Theme Classification groups records into a transparent taxonomy covering quality, fulfillment, billing, and support.
- Pattern Detection identifies trends by SKU, channel, business unit, and time period to find concentrations.
- Root-Cause Investigation applies structured methods like the 5 Whys and Fishbone analysis to separate symptoms from causes.
- Action Planning generates a table of immediate containment, corrective, and preventive actions with success measures.
How it works
- Supply complaint history via CSV, spreadsheet, or text, including fields like issue description, product, and resolution status.
- The skill standardizes the records and identifies duplicates or data gaps in the set.
- It performs a Pareto analysis and pattern breakdown to rank issues by severity and frequency.
- You receive a structured report with evidence-based hypotheses and a prioritized action list.
Frameworks & tools
This skill uses structured analysis methodologies including 5 Whys, Ishikawa (Fishbone) diagrams, and Pareto concentration analysis. It works with any structured text or tabular complaint data (CSV, XLSX).
Why this beats prompting it yourself
Generic prompts often confuse customer symptoms with actual root causes or hallucinate defect rates without proper denominators. This skill enforces a strict investigation workflow that labels hypotheses clearly and prioritizes safety and regulatory risks over simple frequency counts.
Use cases
- Identify which stage of the fulfillment process is causing repeat delivery delays.
- Connect specific product defect complaints to batch numbers or software versions.
- Audit customer support logs to find communication gaps causing refund requests.
- Generate a prioritized list of operational improvements for quarterly reviews.
Known limitations
Does not independently prove legal liability or replace formal regulatory safety investigations. Requires existing complaint data to function.
Requires structured data for statistical accuracy. Cannot determine defect rates without sales volume data. Does not replace formal safety or legal audits.
How to use
- 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
Sign in to Agensi
Your agent shows a sign-in link. Sign in and click Allow.
- 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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