If You Already Know One Skill, the Postings Tell You Which One to Learn Next.

Share is the percentage of postings requiring the seed skill that also require the partner skill. Lift is how much more often the partner appears in those postings than across the whole field. Both matter: a skill at 12x lift that appears in 2% of postings is distinctive and not worth prioritising.

Source: Seeker job corpus, 231,147 active job postings, snapshot 2026-07-21. seekerscore.com/insights/skills-that-travel-together-2026

Why lift alone gives bad advice

Rank purely by how distinctive a pairing is and you get recommendations nobody should act on. FastAPI is 3.4 times more common in Python postings than in engineering generally, which sounds compelling until you see that it appears in 3.5% of them. Learning it makes you eligible for a narrow slice. Rank purely by share and you get the opposite failure: SQL appears in 29.2% of Python postings but only 1.9x the baseline, meaning it is common everywhere and tells you nothing about Python specifically. The pairings worth acting on score on both, and TypeScript in React postings is the clearest example in the corpus: 48.8% share and 6.2x lift together.

The pairings that hold up

React is the most decisive. TypeScript at 48.8% and 6.2x, Node.js at 34.0% and 5.6x, JavaScript at 39.4% and 4.3x. If you write React professionally and do not write TypeScript, that is the single clearest gap in this dataset. AWS points sideways rather than deeper: Azure at 41.1% and Google Cloud at 36.7%, both ahead of Kubernetes. In data and analytics, SQL pairs with the visualisation and pipeline layer rather than with more database work: Tableau 24.7%, ETL 24.6%, Snowflake 16.9%. Finance is the one that surprises people. Excel-requiring finance postings pair most distinctively with PowerPoint at 3.6x and Word at 3.4x. The Office suite travels as a unit, and the shares are modest but the lift is among the highest measured.

Python is the exception, and that is informative

Python produces the weakest pairings of any seed we measured. Its top partners are machine learning at 36.1%, SQL at 29.2% and Java at 25.3%, all at roughly 1.9x lift. Nothing stands out. The reason is visible elsewhere in our data: Python is not one job. It appears in 53.4% of ML and AI postings, 66.9% of data engineering postings, 45.2% of DevOps postings and 42.2% of embedded postings. A skill spread that evenly across unrelated specialties has no single natural next step, because the answer depends on which specialty you are actually in. If Python is your starting point, the useful question is not what pairs with Python but which specialty you are aiming at.

How we measured it (and what we did not claim)

For each seed skill we took every active posting in the relevant field whose extracted skills include it, then counted how often each other skill appears alongside. Sample sizes: React 7,222 postings, Python 21,355, AWS 19,015, SQL in data and analytics 8,800, Excel in finance 8,183. Lift compares the pairing rate against the rate across the whole field, and we apply a volume floor so a skill appearing in a handful of postings cannot top the list on ratio alone. Skills come from our extraction pipeline rather than a hand-read of each posting, so anything an employer implied without naming is not counted and every share is a floor. One known contaminant survives the lift filter: generic phrases such as attention to detail scored highly in finance and were removed by hand, which means the published list is curated rather than purely mechanical. This measures which skills appear together in postings. It does not measure which combinations get people hired, and it does not establish that learning the partner skill causes an offer.

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