Inspect Archive

decisions

Show encounter detection decisions from replay bundles.

# For each session with a replay_bundle.json
for dir in "$ARCHIVE/$DATE"/*/; do
  if [ -f "$dir/replay_bundle.json" ]; then
    python3 -c "
import json
with open('$dir/replay_bundle.json') as f:
    b = json.load(f)
checks = b.get('detection_checks', [])
split = b.get('split_decision')
print(f\"  Session: $(basename $dir | cut -c1-8)  checks={len(checks)}  split={'yes: '+split['trigger'] if split else 'no'}\")" 2>/dev/null
  fi
done

day-log

Parse the day-level orchestration log for the date.

DAY_LOG="$ARCHIVE/$DATE/day_log.jsonl"
if [ -f "$DAY_LOG" ]; then
  python3 -c "
import json
with open('$DAY_LOG') as f:
    for line in f:
        e = json.loads(line)
        print(f\"  {e.get('ts','?')[11:19]}  {e.get('event','?')}  {json.dumps({k:v for k,v in e.items() if k not in ('ts','event')}, separators=(',',':'))[:100]}\")"
fi

non-clinical

Find sessions flagged as non-clinical (SOAP generation was skipped).

for dir in "$ARCHIVE/$DATE"/*/; do
  if [ -f "$dir/metadata.json" ]; then
    python3 -c "
import json
with open('$dir/metadata.json') as f:
    m = json.load(f)
if m.get('likelyNonClinical'):
    print(f\"  {m.get('sessionId','?')[:8]}  words={m.get('wordCount','?')}  method={m.get('detectionMethod','?')}\")" 2>/dev/null
  fi
done

missing-soap

Find sessions that should have SOAP but don't.

for dir in "$ARCHIVE/$DATE"/*/; do
  if [ -f "$dir/metadata.json" ]; then
    python3 -c "
import json
with open('$dir/metadata.json') as f:
    m = json.load(f)
if not m.get('hasSoapNote') and not m.get('likelyNonClinical') and m.get('wordCount', 0) > 100:
    print(f\"  {m.get('sessionId','?')[:8]}  words={m.get('wordCount','?')}  method={m.get('detectionMethod','?')}\")" 2>/dev/null
  fi
done

sensor-events

Extract sensor-related events from day log and replay bundles.

# Sensor transitions from day log
python3 -c "
import json
with open('$ARCHIVE/$DATE/day_log.jsonl') as f:
    for line in f:
        e = json.loads(line)
        if 'sensor' in e.get('event','').lower() or 'sensor' in json.dumps(e).lower():
            print(f\"  {e.get('ts','?')[11:19]}  {e.get('event','?')}\")" 2>/dev/null

# Sensor context from replay bundles
for dir in "$ARCHIVE/$DATE"/*/; do
  if [ -f "$dir/replay_bundle.json" ]; then
    python3 -c "
import json
with open('$dir/replay_bundle.json') as f:
    b = json.load(f)
transitions = b.get('sensor_transitions', [])
if transitions:
    print(f\"  Session $(basename $dir | cut -c1-8): {len(transitions)} sensor transitions\")" 2>/dev/null
  fi
done

Report Format

After running queries, summarize:

1. Total sessions, encounter count, clinical vs non-clinical breakdown
2. Detection methods used (hybrid_llm, hybrid_sensor_confirmed, hybrid_sensor_timeout, flush, manual)
3. Any anomalies: missing SOAP, unusually short/long sessions, high merge-back count
4. For sensor queries: departure/arrival patterns and correlation with splits