community-automations/vector-anomaly

Research & Intel

PublicClaude subagent

Vector Anomaly Detection

At scale, the weird record that signals fraud or a broken feed looks identical to the millions around it. This flow finds the outliers by similarity, not rules.

sonnet1 weekPineconePythonscikit-learnKafka
ClaudeClaude
ROI for
README.md

Why this subagent

At scale, the weird record that signals fraud or a broken feed looks identical to the millions around it. This flow finds the outliers by similarity, not rules.

Each record is embedded and indexed; a KNN lookup measures how far it sits from its neighbors, and anything past the threshold gets flagged. Flagged points arrive with their nearest neighbors attached, giving a downstream agent the context to judge whether it's a real anomaly.

How it runs

    • Read

      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.

Sample output

json
// Sample output
// (generated when the pipeline finishes)

Embed each record, run KNN, and flag points whose neighbor distance exceeds the threshold, with neighbor context attached.

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.