Status Page Incident Trigger Scorer

Turn imported status-page incident rows into deterministic impact, support, GTM, and vendor-risk trigger briefs.

What it does

This Actor reads incident rows you already have from sources like Statuspage, Instatus, BetterStack, vendor RSS/API exports, existing status-page monitors, spreadsheets, or manual research. It does not scrape or monitor those sources.

For each deduped incident row, it produces:

• normalized vendor, source, title, incident URL, lifecycle status, severity, timestamps, duration, and affected components;
• deterministic impact score and urgency level;
• trigger types for support, customer success, GTM, competitive intelligence, maintenance change, vendor-risk, or security review;
• matched critical components;
• evidence snippets;
• customer impact brief;
• support playbook;
• cautious sales/CS trigger angle;
• warnings and risk flags for unsupported interpretation.

Input

This MVP supports items mode only.

Required:

• items: non-empty array of status incident-like row objects.

Optional:

• yourProduct: product, service, or offer profile for response angles.
• targetVendors: vendor, competitor, or dependency names to prioritize.
• targetSegments: customer roles, teams, or ICP terms for briefs.
• criticalComponents: component keywords to treat as high impact.
• maxItems: maximum rows to process. Default: 500.
• scrapedAt: reference timestamp for deterministic metadata.

Common row fields:

• vendorName, provider, vendor, serviceName, service, companyName, company, productName, dependency
• source, platform, statusPage, statusPageUrl
• severity, impact, status, state, incidentStatus
• incidentTitle, title, name, summary
• description, body, message, details, updates
• components, affectedComponents, services, component
• startedAt, startsAt, createdAt, startTime
• resolvedAt, endsAt, endTime, closedAt, resolutionTime
• durationMinutes, duration
• incidentUrl, url, sourceUrl

Example input:

{
  "items": [
    {
      "vendorName": "Acme Payments",
      "source": "Statuspage",
      "severity": "major outage",
      "status": "resolved",
      "incidentTitle": "Checkout API outage",
      "description": "Checkout API returned elevated 500 errors. Payments and webhooks were delayed for enterprise customers.",
      "components": ["Checkout API", "Webhooks"],
      "startedAt": "2026-07-06T10:00:00.000Z",
      "resolvedAt": "2026-07-06T12:45:00.000Z",
      "incidentUrl": "https://status.example/incidents/acme-checkout-api"
    }
  ],
  "yourProduct": "Reliable payment orchestration platform for SaaS teams",
  "targetVendors": ["Acme Payments"],
  "targetSegments": ["enterprise SaaS", "customer success"],
  "criticalComponents": ["checkout", "payments", "webhooks", "api"]
}

Output

The Actor pushes one dataset item per deduped incident evidence row. The default dataset is documented by .actor/dataset_schema.json for the Store/API output tab.

Key output fields:

• vendorName
• severity
• incidentStatus
• impactScore
• urgencyLevel
• triggerTypes
• affectedComponents
• criticalComponentsMatched
• evidenceSnippets
• customerImpactBrief
• supportPlaybook
• salesTriggerAngle
• riskFlags
• warnings

Scoring

The impact score is deterministic. It increases when imported incident evidence contains major outage/degraded-performance language, active unresolved status, long duration, critical components, customer-impact language, security/compliance terms, and target vendor matches. A high score means the row may be useful for support triage, customer communication, GTM planning, or vendor-risk review. It does not prove buyer intent, churn, SLA breach, systemic unreliability, or legal/compliance truth.

MVP limitations

• It does not scrape status pages.
• It does not monitor uptime or poll vendor pages.
• It does not call Statuspage, Instatus, BetterStack, vendor APIs, RSS feeds, or Apify cloud in MVP.
• It does not bypass logins, private status pages, subscriptions, paywalls, or robots rules.
• It does not use AI/LLM.
• It does not claim that one incident proves a vendor is unreliable.
• It does not guarantee sales outcomes, response rates, buyer intent, churn, SLA breach, legal compliance, or factual completeness.
• It supports deterministic top-level row fields only in this version.

Running it

In Apify Console, start with the prefilled input in Apify Console or paste your own incident rows using the example input shape above. The prefilled sample produces a non-empty dataset for Store QA.

For local source checkout verification:

npm install
npm test
apify validate-schema

To run locally without Apify storage, save input as input.json and run:

$node src/main.js --input input.json --output output.json