Context
SPEC-02 defines four priority modifiers beyond the base formula (impact*0.5 + (1-risk)*0.3 + (1-effort)*0.2):
- Recency penalty:
priority *= 0.5 for 24h after failure, decaying over a week
- Owner preference boost:
1.2x for historically accepted categories
- Owner preference penalty:
0.5x for historically rejected categories
- Staleness boost:
1.1x per week for opportunities pending >7 days, capped at 1.5x
Current Behavior
Priority score is purely the base formula. No modifiers are applied.
Expected Behavior
Apply all four modifiers using data from Cortex (experiment history, category stats, opportunity creation timestamps).
Impact
- Suboptimal opportunity ordering (not broken, just less intelligent)
- Repeats recently failed approaches without cooldown
- Doesn't learn from maintainer acceptance/rejection patterns in ordering
- Stale opportunities never get priority bumped
References
atlas-specs/02-ASSESSMENT.md — priority modifier formulasatlas/assessment/modules/base.py — priority_score property (base formula only)atlas/feedback/processor.py — _update_category_stats (data exists but unused for scoring)
Context
SPEC-02 defines four priority modifiers beyond the base formula (
impact*0.5 + (1-risk)*0.3 + (1-effort)*0.2):priority *= 0.5for 24h after failure, decaying over a week1.2xfor historically accepted categories0.5xfor historically rejected categories1.1xper week for opportunities pending >7 days, capped at1.5xCurrent Behavior
Priority score is purely the base formula. No modifiers are applied.
Expected Behavior
Apply all four modifiers using data from Cortex (experiment history, category stats, opportunity creation timestamps).
Impact
References
atlas-specs/02-ASSESSMENT.md— priority modifier formulasatlas/assessment/modules/base.py—priority_scoreproperty (base formula only)atlas/feedback/processor.py—_update_category_stats(data exists but unused for scoring)