Job Posting Technology Stack Change Intelligence

Use this Actor to monitor job posting technology stack change changes and return decision-ready change signals. It is designed for repeatable human, API, Apify AI, and MCP-driven workflows.

Detect technology-stack demand changes from recurring job-posting datasets by employer, role and skill.

Why use this Actor

Job scrapers expose descriptions and skills but do not directly show which technologies are accelerating or fading inside an employer. This Actor sits above raw extraction: supply a current dataset, optionally add a previous snapshot, and receive an aggregated report built for recurring monitoring and AI-agent workflows.

Key features

• Technology demand deltas by employer.

• Reusable current-vs-previous job snapshots.

• Emerging-stack and declining-stack actions.

• Data-first analysis over upstream job datasets.

• Reads inline JSON rows or Apify Dataset IDs with limited READ permission.

• Writes one auditable report to the default Dataset and INTELLIGENCE_REPORT.

Example

Use the prefilled example or replace currentItems with rows from an upstream Actor. On recurring runs, provide the prior period in previousItems or previousDatasetId. The Actor normalizes common aliases, compares snapshots, ranks the strongest entity changes and emits agentAction.

Use cases

• developer-market research.
• vendor intelligence.
• workforce planning.

Pricing

One primary pay-per-event outcome: one decision-ready intelligence report. Base price USD 0.012 before Apify tier discounts. The 256 MB data-first architecture is designed for strong unit economics.

Limitations

• Analyzes supplied public or appropriately licensed data and does not bypass restricted sources.
• Scores are decision-support signals, not predictions or guarantees.
• Keep stable identifiers across snapshots for best change detection.
• Zero-direct-competition status is rechecked before publication because the Store changes continuously.

Workflow

upstream dataset -> current snapshot -> optional previous snapshot -> normalization -> entity aggregation -> change scoring -> ranked signals -> agentAction.

Input

Provide currentItems as normalized JSON rows or select a cloud Dataset with currentDatasetId. For change analysis, add previousItems or previousDatasetId. The Actor reads Datasets with limited READ permission. Keep identifiers stable across periods and normalize source fields where possible. The prefilled example is intentionally small so the first run can be validated before scaling.

Output

The default Dataset receives one decision-ready intelligence report with record counts, snapshot additions and removals, ranked entity signals, secondary-dimension breadth, a bounded signal score, and agentAction. The same report is stored as INTELLIGENCE_REPORT for downstream automation. Results are designed for Tasks, schedules, webhooks, dashboards, and agent workflows rather than as a replacement for source evidence.