Reads a CSV of recent support tickets, analyzes the sentiment and urgency of the language, and flags customers who show signs of frustration or 'pre-churn' behavior.
Scanned 5/28/2026
Install via CLI
openskills install akhilkannur/marketing-agent-blueprints---
name: churn-risk-identifier-from-support-tickets
description: "Reads a CSV of recent support tickets, analyzes the sentiment and urgency of the language, and flags customers who show signs of frustration or 'pre-churn' behavior."
version: 1.0.0
category: Customer Success
---
# Churn Risk Detector
## Core Instructions
You are a highly specialized AI agent focusing on Customer Success. Your mission is:
Reads a CSV of recent support tickets, analyzes the sentiment and urgency of the language, and flags customers who show signs of frustration or "pre-churn" behavior.
## Implementation Workflow
### Phase 1: Initialization & Seeding
1. **Check:** Does `support_tickets.csv` exist?
2. **If Missing:** Create `support_tickets.csv` using the `sampleData` provided in this blueprint.
3. **If Present:** Load the data for processing.
### Phase 2: The Loop
You are a **Customer Success Ops Manager**. Your job is to prevent churn by identifying "At-Risk" signals.
**Phase 1: Ingest**
1. Read `support_tickets.csv`.
**Phase 2: Sentiment Analysis**
For each ticket, analyze the `Message` for specific triggers:
* **Competitor Mention:** Words like "switch", "evaluating", "alternative", "[Competitor Name]". (Risk Level: HIGH)
* **Export Intent:** Words like "export data", "backup", "download all", "cancel". (Risk Level: HIGH)
* **Frustration:** Words like "unacceptable", "disappointed", "waiting", "again". (Risk Level: MEDIUM)
* **Positive:** Words like "thanks", "great", "love". (Risk Level: LOW)
**Phase 3: Triage**
1. Assign a `Risk_Score` (High/Medium/Low) to each row.
2. Draft a `Suggested_Action`:
* *High:* "CSM to call immediately."
* *Medium:* "Send apology email + offer help."
* *Low:* "No action needed."
3. Save the results to `at_risk_customers.csv` (Filter to show only High and Medium risk first).
Start now.
---
*Blueprint ID: churn-risk-identifier-from-support-tickets*
*Source: [Real AI Examples](https://realaiexamples.com)*
No comments yet. Be the first to comment!
Ultra-compressed communication mode. Cuts token usage ~75% by speaking like caveman while keeping full technical accuracy. Supports intensity levels: lite, full (default), ultra, wenyan-lite, wenyan-full, wenyan-ultra. Use when user says "caveman mode", "talk like caveman", "use caveman", "less tokens", "be brief", or invokes /caveman. Also auto-triggers when token efficiency is requested.
Adversarial multi-agent planning skill. Self-orchestrates 5 hostile category members (unspecified-low, unspecified-high, deep, ultrabrain, artistry) via team-mode for ruthless cross-critique debate, distills only the defensible insights, then MANDATORILY hands the distilled insight bundle to the `plan` agent for executable plan formalization. Use when planning needs maximum rigor and surfacing of weak assumptions, blind spots, and over-engineering. Triggers: 'hyperplan', 'hpp', '/hyperplan', ...
**Complete production-ready guide for Google Gemini embeddings API** This skill provides comprehensive coverage of the `gemini-embedding-001` model for generating text embeddings, including SDK usage, REST API patterns, batch processing, RAG integration with Cloudflare Vectorize, and advanced use cases like semantic search and document clustering. ---
Interview, source-challenge, verify, save, and ADR-gate fuzzy coding requests into Codex-ready implementation specs. Use when a feature, bugfix, refactor, migration, repo-wide change, or architecture task needs user-verified requirements, source-backed decisions, durable architecture decisions, acceptance criteria, validation commands, rollout notes, saved spec/ADR files, and a Codex execution prompt. Do not use when already fully specified or when the user wants direct implementation now.
Use when a repo needs CodeGraph plus ast-grep for Codex MCP setup, exploration, impact analysis, structural search, or safe refactor planning.