Token导航 LogoToken导航TokenDH.com
研究检索需要联网github未标认证来源可访问许可证需确认审计通过

crisis-detector危机探测器

Agent Skill

crisis-detector 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

2,081

周安装

85

GitHub Stars

85

下载量

666
CodexClaudeCursorGemini CLI

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

unknown

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:crisis-detector(危机探测器)
来源仓库:https://github.com/guia-matthieu/clawfu-skills
仓库路径:skills/crisis-detector
安装命令:
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill crisis-detector
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/guia-matthieu/clawfu-skills --skill crisis-detector

简介

crisis-detector 用于查找、检索和筛选相关信息。

  • 适合在 Codex、Claude、Cursor、Gemini CLI 中根据关键词、任务场景或来源线索快速定位候选结果。
  • 通过 npx skills add 命令从指定 GitHub 仓库安装并使用。
  • 安装前需确认权限范围和维护状态,注意是否会触发联网或文件读写操作。
  • 建议结合原始 README 核验具体用法和功能边界。

SKILL.md

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 DoesYou Decide
Identifies warning signalsRisk tolerance
Assesses crisis probabilityResponse resources
Creates detection criteriaEscalation authority
Designs monitoring systemsCommunication strategy
Suggests response triggersFinal action calls

Instructions

Step 1: Map Crisis Types

Crisis Categories:

CategoryExamplesWarning Time
OperationalOutage, product failureHours to days
ReputationalExecutive scandal, viral complaintMinutes to hours
Legal/RegulatoryLawsuit, investigationDays to weeks
FinancialEarnings miss, fraudHours to days
HumanWorkplace incident, harassmentHours to days
ExternalNatural disaster, market crashVariable

Step 2: Identify Early Signals

Signal Types:

Signal TypeExamplesMonitoring
InternalEmployee complaints, support ticketsHR, Support data
CustomerReview patterns, churn spikesCX metrics
SocialMention volume, sentiment shiftSocial tools
MediaPress inquiries, journalist interestPR inbox
RegulatoryCompliance notices, audit findingsLegal
FinancialPayment disputes, refund requestsFinance

Step 3: Build Detection Matrix

Signal Strength Assessment:

SignalWeakModerateStrongCritical
Volume spike+25%+50%+100%+300%
Sentiment shift-10%-20%-30%-50%
Media inquiries12-34-56+
Support tickets+10%+25%+50%+100%
Social influencer10K50K100K500K+

Step 4: Create Escalation Triggers

Trigger Framework:

LevelSignals PresentAction
Watch1 moderate signalMonitor closely
Alert2+ moderate or 1 strongNotify team
WarningMultiple strong signalsAssemble team
CrisisAny critical signalActivate 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 DASHBOARD 🟢 NORMAL │ ├──────────────────────────────────────────────────────────┤ │ │ │ SHIPPING STATUS 🟢 Normal │ │ ├─ Carrier delays: 3% (threshold: 10%) │ │ ├─ Backlog: 4 hours (threshold: 24hr) │ │ └─ "Where's my order": 45 (baseline: 50) │ │ │ │ PRODUCT QUALITY 🟢 Normal │ │ ├─ Return rate: 5.2% (threshold: 10%) │ │ ├─ Quality tickets: 2 (threshold: 3+ same) │ │ └─ Photo complaints: 1 (threshold: 5) │ │ │ │ SOCIAL SENTIMENT 🟡 Watch │ │ ├─ Sentiment: -15% (threshold: -20%) │ │ ├─ Volume: +40% (threshold: +50%) │ │ └─ Influencer: None flagged │ │ │ │ SECURITY 🟢 Normal │ │ ├─ Login anomalies: Normal │ │ └─ Payment fraud: 0.3% │ │ │ └──────────────────────────────────────────────────────────┘

---

### Escalation Matrix

| Level | Criteria | Notification | Response |
|-------|----------|--------------|----------|
| 🟢 Normal | All metrics in range | None | Standard ops |
| 🟡 Watch | 1 metric at threshold | Slack alert | Increased monitoring |
| 🟠 Alert | 2+ metrics or 1 exceeded | Team notification | Standby team |
| 🔴 Crisis | Critical threshold breached | All-hands alert | Activate protocol |

---

### Alert Routing

| Signal Type | Primary | Backup | Escalation |
|-------------|---------|--------|------------|
| Shipping | Operations | COO | CEO |
| Product quality | Product | VP Ops | CEO |
| Social media | Marketing | CMO | CEO |
| Security | IT | CTO | CEO + Legal |
| Legal/PR | Legal | CEO | Board |

---

### Holiday Season Adjustments

During high-volume periods (Nov 15 - Dec 31):

| Metric | Normal Threshold | Holiday Threshold |
|--------|------------------|-------------------|
| Carrier delays | 10% | 15% |
| Support volume | +50% | +100% |
| Social volume | +50% | +75% |
| Response time | 4 hours | 8 hours |

**Rationale:** Higher baseline expected; adjust to avoid false alerts
while maintaining genuine crisis detection.

Example 2: Real-Time Crisis Assessment

Input:

Assess this situation:
- Support tickets up 80% in 2 hours
- Twitter mentions up 150%
- Hashtag #[Brand]Scam trending locally
- 3 refund requests mentioning "charged twice"
- No known system issues

Output:

## Crisis Assessment: Potential Payment Issue

### Alert Level: 🔴 CRISIS

---

### Signal Analysis

| Signal | Value | Normal | Status |
|--------|-------|--------|--------|
| Support spike | +80% | <25% | CRITICAL |
| Social spike | +150% | <50% | CRITICAL |
| Trending hashtag | Yes | No | CRITICAL |
| Duplicate charge reports | 3 | 0 | CONCERNING |

---

### Probable Cause

**Most likely: Payment processing error**

Evidence:
1. Multiple "charged twice" complaints
2. No known system issues rules out outage
3. Sudden spike suggests batch problem
4. #Scam hashtag = customers think fraud

---

### Immediate Actions

| Priority | Action | Owner | Timeline |
|----------|--------|-------|----------|
| 1 | Check payment processor logs | Engineering | NOW |
| 2 | Identify affected transactions | Finance | 30 min |
| 3 | Prepare holding statement | Comms | 15 min |
| 4 | Alert customer service team | CX Lead | NOW |
| 5 | Monitor hashtag spread | Social | Ongoing |

---

### Holding Statement (Draft)

We're aware some customers may have experienced duplicate charges. Our team is investigating urgently. If you've been affected, please DM us or email [support] - we'll make this right immediately.

Updates to follow shortly.

---

### Escalation Path

**Now:** CTO + CFO + CMO notified
**+30 min:** CEO briefing if not resolved
**+1 hour:** External statement if ongoing

---

### Crisis Trajectory

**If unaddressed (next 2-4 hours):**
- Hashtag goes national
- Media inquiries begin
- Trust pilot review bomb
- Social influencers amplify

**If addressed quickly (next 1-2 hours):**
- Contain to affected customers
- Flip narrative to "responsive company"
- Prevent media escalation
- Build goodwill through fast resolution

---

### Resolution Checklist

- [ ] Root cause identified
- [ ] Affected customers identified
- [ ] Refunds initiated
- [ ] Proactive communication sent
- [ ] Social response deployed
- [ ] Hashtag monitoring active
- [ ] Post-incident review scheduled

Skill Boundaries

What This Skill Does Well

  • Identifying early warning signals
  • Creating detection frameworks
  • Assessing crisis probability
  • Designing escalation systems

What This Skill Cannot Do

  • Access your actual systems
  • Monitor in real-time
  • Make response decisions
  • Know your specific thresholds

Iteration Guide

Follow-up Prompts:

  • "Design detection for [specific crisis type]"
  • "Create escalation protocol for [scenario]"
  • "What signals should we add for [risk]?"
  • "How do we prevent [past crisis] from recurring?"

References

  • Institute for Crisis Management
  • Burson Crisis Playbook
  • Harvard Business Review Crisis Research
  • Edelman Trust Barometer

Related Skills

  • social-listening - Monitoring systems
  • response-coordinator - Crisis response
  • reputation-recovery - Post-crisis rebuild

Skill Metadata

  • Domain: Crisis
  • Complexity: Intermediate-Advanced
  • Mode: centaur
  • Time to Value: 2-4 hours for system design
  • Prerequisites: Access to metrics, stakeholder alignment

适合场景

01

用户想查找某类 Agent Skill 时

02

需要根据任务场景推荐可安装能力包时

03

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

Codex

37.88%
按下载量换算252

Claude

29.38%
按下载量换算196

Cursor

18.03%
按下载量换算120

Gemini CLI

8.3%
按下载量换算55

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

通过

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills