effectiveness
Measure how often your pattern suggestions are confirmed by later observations, revealing which patterns are genuinely useful guidance versus noise.
Instructions
Measure how often suggested patterns were reinforced by a later observe().
Each suggest() call logs the patterns it returned; each observe()
confirms the most recent unconfirmed suggestion for that pattern.
A high confirmation rate means the pattern is genuinely useful
guidance; a low rate means it is noise worth pruning via
alias_pattern() merges or gc() decay. Read-only.
For a raw observation-velocity view use trending(days). For
promotion/level distribution use stats().
Args:
days: Look-back window in days. Default 30. Shorter (7)
surfaces recent drift; longer (90) measures long-term
value.
Returns:
{"patterns": [<row>, ...],
"summary": {"total_suggested": int, "total_confirmed": int,
"overall_rate": float, "period_days": int}}
Each <row>: {"pattern": str, "suggested": int,
"confirmed": int, "rate": float in [0.0, 1.0] rounded to
3 decimals}. Rows ordered by confirmed desc, then suggested
desc. "overall_rate" is total_confirmed / total_suggested
(0.0 when no suggestions in window). Empty "patterns" list
means no suggest() calls happened in the window — not an
error.
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| days | No |
Output Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||