Build an npm Package Intelligence Dataset

Software-ecosystem intelligence can support product research, partnership discovery, competitive mapping, and dependency strategy. A durable dataset needs more than a one-time package list: it needs stable queries, exact watchlists, snapshots, and provenance. The npm Package Scraper provides the collection layer.

Separate discovery from monitoring

Use search queries to discover a changing landscape and exact names to monitor a fixed cohort.

{
  "queries": ["workflow automation", "AI agent", "web scraping"],
  "packageNames": ["apify", "crawlee", "playwright"],
  "maxResultsPerQuery": 100
}

For strict query-membership analysis, run each search independently because a combined run deduplicates packages by name. Store a query-to-package bridge, then maintain one canonical package table keyed by name.

Design for changing evidence

Create a snapshot table with name, version, description, metrics, maintainers, project links, query, and collection time. Mark whether a row came from exact lookup or discovery based on field availability. Preserve nulls and avoid treating a missing search metric as zero.

Schedule searches monthly or quarterly for market structure and exact lookups more frequently for release monitoring. Validate expected counts before loading. A failed run must not erase the previous snapshot.

Derive trends only from comparable queries and caps. Rank movement can reflect competing packages entering the result set, not just changes to one package. Review repositories and licenses before using maintainer or project links for outreach.

The resulting dataset is strongest when it remains traceable: every row should identify its query, run, source URL, and time. Add separate advisory, repository-activity, and company-enrichment sources as linked evidence rather than pretending npm registry fields answer every product or risk question.