/crisis-detector

name: crisis-detector
description: Identify early warning signals of potential PR crises through pattern recognition, escalation triggers, and risk assessment
license: MIT
metadata:
  author: ClawFu
  version: 1.0.0
  mcp-server: "@clawfu/mcp-skills"

Crisis Detector

> Identify early warning signs of potential crises before they escalate through pattern recognition, signal monitoring, and risk assessment.

When to Use This Skill

• Setting up early warning systems
• Assessing crisis probability
• Training teams on signals
• Building escalation criteria
• Post-crisis prevention planning

Methodology Foundation

Based on **Institute for Crisis Management research** and **Burson crisis frameworks**, combining:

• Signal identification
• Pattern recognition
• Risk assessment matrices
• Escalation protocols

What Claude Does vs What You Decide

| Claude Does | You Decide | |-------------|------------| | Identifies warning signals | Risk tolerance | | Assesses crisis probability | Response resources | | Creates detection criteria | Escalation authority | | Designs monitoring systems | Communication strategy | | Suggests response triggers | Final action calls |

Instructions

Step 1: Map Crisis Types

**Crisis Categories:**

| Category | Examples | Warning Time | |----------|----------|--------------| | **Operational** | Outage, product failure | Hours to days | | **Reputational** | Executive scandal, viral complaint | Minutes to hours | | **Legal/Regulatory** | Lawsuit, investigation | Days to weeks | | **Financial** | Earnings miss, fraud | Hours to days | | **Human** | Workplace incident, harassment | Hours to days | | **External** | Natural disaster, market crash | Variable |

Step 2: Identify Early Signals

**Signal Types:**

| Signal Type | Examples | Monitoring | |-------------|----------|------------| | **Internal** | Employee complaints, support tickets | HR, Support data | | **Customer** | Review patterns, churn spikes | CX metrics | | **Social** | Mention volume, sentiment shift | Social tools | | **Media** | Press inquiries, journalist interest | PR inbox | | **Regulatory** | Compliance notices, audit findings | Legal | | **Financial** | Payment disputes, refund requests | Finance |

Step 3: Build Detection Matrix

**Signal Strength Assessment:**

| Signal | Weak | Moderate | Strong | Critical | |--------|------|----------|--------|----------| | Volume spike | +25% | +50% | +100% | +300% | | Sentiment shift | -10% | -20% | -30% | -50% | | Media inquiries | 1 | 2-3 | 4-5 | 6+ | | Support tickets | +10% | +25% | +50% | +100% | | Social influencer | 10K | 50K | 100K | 500K+ |

Step 4: Create Escalation Triggers

**Trigger Framework:**

| Level | Signals Present | Action | |-------|-----------------|--------| | Watch | 1 moderate signal | Monitor closely | | Alert | 2+ moderate or 1 strong | Notify team | | Warning | Multiple strong signals | Assemble team | | Crisis | Any critical signal | Activate protocol |

Step 5: Document and Improve

**Post-Detection Review:**

• How early did we detect?
• What signals did we miss?
• How accurate was assessment?
• What should we add to monitoring?

Examples

Example 1: Crisis Detection System Design

**Input:**

Design a crisis detection system for:
- E-commerce company (clothing)
- 500K customers
- Active on Instagram, TikTok, Twitter
- Past issue: Shipping delays during holiday

**Output:**

## Crisis Detection System: E-Commerce Fashion

### Crisis Risk Map

| Crisis Type | Probability | Impact | Priority |
|-------------|-------------|--------|----------|
| Shipping delays | High | High | P1 |
| Product quality issue | Medium | High | P1 |
| Social media backlash | Medium | High | P1 |
| Data breach | Low | Critical | P1 |
| Influencer controversy | Medium | Medium | P2 |
| Supply chain disruption | Medium | High | P2 |
| Payment fraud | Low | Medium | P3 |

---

### Early Warning Signals

#### P1: Shipping Delays

**Leading Indicators (3-5 days before crisis):**
| Signal | Source | Threshold |
|--------|--------|-----------|
| Carrier delay reports | Logistics API | >10% delayed |
| Warehouse backlog | WMS data | >24hr processing |
| Weather events | News/weather | Storm in hub |
| "Where's my order" tickets | Support | +50% daily |

**Lagging Indicators (crisis starting):**
| Signal | Source | Threshold |
|--------|--------|-----------|
| Social mentions | Social listening | "shipping" +100% |
| Review mentions | Trustpilot/G2 | Shipping 3/5 stars |
| Refund requests | Payment system | +30% |
| Chargeback rate | Payment processor | >1% |

---

#### P1: Product Quality Issue

**Leading Indicators:**
| Signal | Source | Threshold |
|--------|--------|-----------|
| Return rate spike | Returns data | >10% on SKU |
| Quality complaints | Support tickets | 3+ same issue |
| Photo complaints | Social | "damaged", "wrong color" |
| Batch-specific issues | QC data | Same lot number |

**Lagging Indicators:**
| Signal | Source | Threshold |
|--------|--------|-----------|
| Viral unboxing | TikTok/Instagram | >10K views negative |
| Review bomb | Product pages | Multiple 1-stars |
| Media inquiry | PR inbox | Journalist question |

---

#### P1: Social Media Backlash

**Leading Indicators:**
| Signal | Source | Threshold |
|--------|--------|-----------|
| Sentiment shift | Social tools | -20% in 24hr |
| Controversial post | Your social | Negative comments >10% |
| Influencer complaint | Social | >50K follower post |
| Screenshot spreading | Twitter/Reddit | Same image 5+ times |

**Lagging Indicators:**
| Signal | Source | Threshold |
|--------|--------|-----------|
| Viral negative | Any platform | >50K engagements |
| Hashtag trending | Twitter | Brand + negative |
| Media pickup | News sites | Article published |
| Competitor amplification | Social | Competitor sharing |

---

### Detection Dashboard

┌──────────────────────────────────────────────────────────┐ │ CRISIS DETECTION DA