Open-ended survey answers are the most useful field and the one nobody has time to read line by line. This pipeline groups them for you.
sonnet4 daysQdrantPythonOpenAIspaCy
Claude
64ROI
69Scale
$1.3k85Saved
ROI for
README.md
Why this subagent
Open-ended survey answers are the most useful field and the one nobody has time to read line by line. This pipeline groups them for you.
Responses are embedded and clustered in Qdrant so similar answers fall together, then each cluster gets a short theme label and a count. Themes come back ranked by how often they appear, with verbatim quotes attached so you can read the real voice behind each one.
How it runs
Used at step 01 to kick off the pipeline.
Write
Used at step 01 to kick off the pipeline.
WebFetch
Used at step 01 to kick off the pipeline.
WebSearch
Used at step 01 to kick off the pipeline.
Embed each response and cluster them in Qdrant to group answers that say the same thing.
pending
Label each cluster with a short theme and count how many responses fall under it.
pending
Return the themes ranked by frequency, each with a few verbatim quotes as evidence.
pending
Sample output
json
// Sample output
// (generated when the pipeline finishes)
Cluster open-ended survey responses into ranked themes, each with a count and representative verbatim quotes.
Unlock the rest
The full agent definition, install snippet, and starter task are gated for community members.
Members get the full `.md` agent file, the npm / pnpm install one-liners, a starter prompt that we've tuned against real runs, and the open-source repo when this automation ships there. One email, magic link, done.