Open DS

Dense data

Compact, exact and quiet, for analysts who scan alerts and compare signals all day.

  • 12 scopes
  • 78 answers
  • 376 tokens
  • 40 components
Primitives · 113The raw options every answer picks fromThe palette, type, space, corners, shadows and motion steps of Dense data: each value, and the decisions that use it.Open primitives →

Why this scheme exists

Its answers to the reasons

  • ProductRuns daily operations, one
  • PeopleDaily experts, AA
  • ContentNumbers and records, a lot
  • TasksScan and compare, constantly
  • Human factorsPeople keep their place, they learn it and recognise it
  • OutcomeTime on task, acting on the wrong record
  • BrandPrecise, quiet
  • ScenariosAt a desk, all day
  • TeamA small team, every month

This scheme, applied

One screen built only from its answers

Open alerts

Open alerts

142

Escalated

7

Model precision

96.4%

Mean time to contain

38 min

AlertAssetDetectedRisk scoreStatus
ALR-2041api-gateway-0108:3092Resolved
ALR-2042llm-inference-0209:1078Escalated
ALR-2043vpn-edge-0309:4599Breach confirmed
ALR-2044model-registry10:2031False positive
ALR-2045auth-service11:0064Investigating
ALR-2046db-primary11:3081Escalated
ALR-2047build-runner-0711:4557Resolved

12 alerts exported.

Scopes

12 scopes, each answered for this scheme

Every scope answers the same five questions: roles, how many, how defined, values, lands in.

Take this scheme

Start from Dense data.

You get 376 tokens, the variants of 40 components, and all 78 answers with their reasons. Change any answer later, and see what it moves.

  • CLIopen-ds init dense-data
  • Link/schemes/dense-data
  • AgentMCP · get_scheme("dense-data")