Which prerequisite operations does this taskitem need?
🔍
Analysis of 1297 accepted rows revealed needs_validate_intake is always True (1297/1297).
It is a constant, not a decision surface — no model can learn from a label with zero variance.
The effective gate is two independent binary classifiers.
Gate A — prereq_resolve
needs_resolve_dependencies
"Does this task require resolving cross-task dependencies before execution?"
997 / 1297 positive76.9% base rate
Baseline accuracy @ 4000 steps 69.4%
Note: high base rate — a majority classifier would score ~77%. Baseline is learning something, but corpus is weak.
Gate B — prereq_architect
needs_architect_review
"Does this task require architect review before proceeding?"
257 / 1297 positive19.8% base rate
Baseline accuracy @ 4000 steps 75.0%
Note: a majority classifier here scores ~80%. Baseline is learning from the minority class.
Why two separate models
Each gate fires independently. A task can need both, either, or neither. Coupling them into one multiclass model creates false label structure.
What the corpus lacks
Only ~17 unique text templates across 1297 rows. The taskitem schema was not designed to carry discriminative RSA fields. Better signal requires schema changes upstream.
Next step
Pipeline decision audit (SPRINT-004): walk the full whetstone_DSL pipeline and identify every decision point before collecting more training data.