| name | inspect-population |
| description | Use when reading what an Eve run evolved — its best solvers/optimizers, their code, and how the population improved |
Inspect a run's population
Read what a run actually evolved: the best candidates, their code, and how scores progressed. Source of truth is the run's lineage DBs + artifacts (local; no wandb needed). run_root layout is in docs/using-eve.md.
The candidates
Each run has <run_root>/solver_lineage.db and <run_root>/optimizer_lineage.db. The eve populations live in the eve_population_entries table, one row per candidate:
sqlite3 <run_root>/solver_lineage.db \
"SELECT entry_id, created_at FROM eve_population_entries ORDER BY created_at;"
Swap in optimizer_lineage.db for optimizers. (Note: the DB also has generic nodes / edges tables, but the eve loop does not populate them, do not query those.)
Scores (rank best-first)
Scores are not stored inline in the DB; each entry references a flat score artifact ({status, score, summary}) at artifacts/<run-id>_solver/state/<...>__<entry_id>_score.yaml. Rank candidates best-first:
for f in <run_root>/artifacts/*_solver/state/*_score.yaml; do
s=$(awk -F': ' '/^score:/{print $2; exit}' "$f")
printf '%s\t%s\n' "$s" "$(basename "$f")"
done | sort -rn | head
The filename carries the <entry_id>; top line = best so far. (The same flat score is also at each candidate's <workspace>/logs/evaluate/score.yaml. Do not parse the workspace-root score.yaml, that one is a different, nested phase-2 record, not the flat score.)
Smoke runs typically show score: 0.0 with a status: error summary; that is the known circle_packing smoke payload error, not a skill problem.
Read a candidate's evolved code
The agent's written solution is under that candidate's workspace: <run_root>/solver_workspaces/<...>/solver/. The archived copy is also stored inline as JSON in the entry's files_ref_json artifact under <run_root>/artifacts/<run-id>_solver/state/.
Improvement over the run
Workspace directories are named ..._step_<n>_...; compare the best score.yaml per step to see the trajectory. The optimizer log trees under <run_root>/artifacts/<run-id>_optimizer/ show how the guidance evolved.
Notes
- Completed-iteration count:
grep -c "Phase 3: updated Elo" <run_root>/runner.log.
- To understand why a candidate behaved a certain way (not just its score/code), read its agent session:
../debug-agent-session/SKILL.md.
