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Syncs the user’s existing ICP and positioning files into Nous, instead of making them re-author their ICP in a second place. The name reads like a getter, but honestly it is a sync: you read the user’s own markdown files with your file tools and push each one into the graph. Nous keeps a served copy of the prose, rebuilds the ICP scoring model from it, and remembers each file’s path so export_icp_model can later write the learned model back into the same file.
Direction is file to graph. The user’s files stay the source of truth for the prose. Nous owns the learned scoring half. The reverse direction (graph to file) is export_icp_model.

Parameters

Map granularly: one file to the single section it best fits (icp.md to ICP, positioning.md to Positioning, pricing.md to Pricing), one entry per file. If one file holds several sections under headers, split it by header into multiple entries. Keep any “not a fit” exclusions inside the ICP section, because Nous turns each stated exclusion into a hard disqualifier that caps those accounts below Not-ICP.

Returns

If there’s nothing valid to sync:

When to call it

  • During onboarding → find the project’s existing GTM files (context/, gtm/, files named icp*, positioning*, pricing*, competitors*), read them, and sync them
  • Whenever an ICP or context file changes → re-sync in the same turn, because the edit does not change the score, the exclusions, or the model until you do
To write the learned model back into the file, use export_icp_model. To sharpen the model on real outcomes, use train_icp_model.

Backed by

POST /v2/workspace/icp/import. The agent setup surface. Shares its implementation with the web GTM Context page.