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Apply social network analysis concepts including nodes, ties, centrality, structural holes, and strong/weak ties to map and analyze relationship structures.
Passed 21 of 23 test cases with the skill, and 21 of 23 without it.
1 fixed20 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
A test case the skill fixed. Same model, same prompt, with and without the skill.
We gathered co-authorship and joint grant application records for 30 researchers across five academic faculties to identify key collaborators and information bottlenecks. Please analyze this collaboration network. Provide a full analysis structured with an overall header, a section defining the network parameters (participant count, tie baseline, data origin), a core metrics summary table listing individual nodes, degree counts, betweenness measures, and assigned roles, followed by structural observations regarding clusters, cross-cutting bridges, structural holes, and unlinked nodes, ending with prioritized actionable insights.
| Checked | Without skill | With skill |
|---|---|---|
| Identifies information bottlenecks based on betweenness centrality values. | Passed | Passed |
| Pairs each actionable recommendation explicitly with its corresponding empirical network finding. | Missed | Passed |
The instructions your model reads when the skill loads.
Social network analysis examines relationships (ties) between actors (nodes) to reveal structure invisible in org charts. It identifies who really holds influence, where information bottlenecks exist, and how ideas spread through a community.
IRON LAW: Structure Determines Influence, Not Just Position
A mid-level manager who bridges two disconnected departments may have more
real influence than a VP who sits in a dense, well-connected cluster.
Network position (centrality, brokerage) determines influence more than
formal hierarchy.
| Concept | Definition | Why It Matters |
|---|---|---|
| Node | An actor (person, org, entity) | Who's in the network |
| Tie | A relationship between nodes | How nodes are connected |
| Strong tie | Frequent, emotional, reciprocal relationship | Trust, support, reliable info |
| Weak tie (Granovetter) | Infrequent, casual, bridging relationship | Access to NEW information and opportunities |
| Degree centrality | Number of direct connections | Popularity, activity |
| Betweenness centrality | How often a node sits on shortest paths between others | Brokerage, gatekeeping, information control |
| Closeness centrality | Average distance to all other nodes | Speed of information reach |
| Structural hole (Burt) | Gap between two clusters, bridged by a broker | Source of competitive advantage — the broker controls information flow |
test cases passed, vs 21 / 23 without
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