MARKET RESEARCH
Tech-Stack Shift Miner
Parses the skills and tools named in a competitor's engineering job descriptions, detects when their required stack changes, and logs platform migrations to a BigQuery trend table.
How it runs
The automated pipeline, trigger to output.
- TriggerWeekly schedule fires
- ActionScrape engineering job descriptionsApify
- ActionExtract normalized technology listOpenAI
- LogicAggregate and diff stack vs prior week
- OutputWrite stack-trend record to warehouseBigQuery
What it does
Job descriptions quietly broadcast a company's technical bets. This workflow pulls a competitor's engineering postings, extracts the named languages, frameworks, cloud providers, and tools from each description, and tracks how that skill mix shifts over time. A sudden appearance of, say, Rust or a new cloud vendor flags a platform migration or new architecture before it ships.
When to use it
Use it when a rival's technical direction is competitively meaningful — you sell developer tooling, compete on performance, or want to anticipate their roadmap. Best run on companies posting enough engineering roles to give a stable signal.
How it works
- 1A weekly schedule fires the run.
- 2Apify scrapes the competitor's engineering job descriptions.
- 3OpenAI extracts a normalized list of technologies and tools from each description.
- 4A logic step aggregates technology frequencies and compares them to the prior week's mix to flag newly introduced or dropped tools.
- 5The per-technology counts and detected shifts are written to a BigQuery table as a longitudinal stack-trend record for analysis and dashboards.
Set it up
What you configure once, before turning it on.
- 1Connect ApifyActors, scrapers, datasets.
- 2Connect OpenAIModels, embeddings, files.
- 3Connect BigQueryDatasets, queries, schemas.
- 4Set each agent's modelWe leave models unset so you pick the tier — fast + cheap, or top-quality.
- 5Tune it to your dataEdit the prompts, filters, and field mappings so it matches how your team works.
- 6Test, then turn it onRun once against a sample, confirm the output, then enable the trigger.
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Run this workflow in your colony.
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