Muse Spark 1.3 on Reconcile a trapped quarter ledger
Passed in 27s. Attempt 2. Run 20260904T131634Z-muse-spark-1-3-ledger-reconcile-trap-2.
Task
Reconcile the quarter ledger
Read invoices.csv, processor_payouts.csv, bank.csv, and rates.csv. Write report.json:
{
"unpaid_invoice_ids": ["INV-1001"],
"unreconciled_total_usd": 1234.56,
"duplicate_invoice_ids": ["INV-1002"],
"eur_invoice_ids": ["INV-1003"]
}
An issued invoice is unpaid when its successful charge rows total less than its invoice amount
in that currency. Do not count fee or refund rows as payment. Exclude invoices marked
refunded and any duplicated invoice id from the unpaid list because its canonical amount is
unknown. A duplicate means the same invoice id appears more than once, even when amounts differ.
For reconciliation, convert every processor row to USD at the supplied currency rate, then sum
by payout_id. Compare that expected total with the sum of bank rows whose kind is
processor_deposit and has the same payout id. Ignore other bank activity. The unreconciled
total is the sum of each payout id's absolute expected-versus-observed difference across the
union of processor and bank payout ids, rounded to cents only at the end.
List every invoice whose invoice currency is EUR. Sort all id lists.
Command
opencode run --dir /tmp/atris-bench/20260904T131634Z-muse-spark-1-3-ledger-reconcile-trap-2/work --auto -m opencode/muse-spark-1.3-contributor-free 'You are working in the current folder. Do the task in TASK.md and write the outputs to the exact paths it names.
Do not ask questions. When done, stop.
# Reconcile the quarter ledger
Read `invoices.csv`, `processor_payouts.csv`, `bank.csv`, and `rates.csv`. Write `report.json`:
` ` `json
{
"unpaid_invoice_ids": ["INV-1001"],
"unreconciled_total_usd": 1234.56,
"duplicate_invoice_ids": ["INV-1002"],
"eur_invoice_ids": ["INV-1003"]
}
` ` `
An issued invoice is unpaid when its successful `charge` rows total less than its invoice amount
in that currency. Do not count `fee` or `refund` rows as payment. Exclude invoices marked
`refunded` and any duplicated invoice id from the unpaid list because its canonical amount is
unknown. A duplicate means the same invoice id appears more than once, even when amounts differ.
For reconciliation, convert every processor row to USD at the supplied currency rate, then sum
by `payout_id`. Compare that expected total with the sum of bank rows whose `kind` is
`processor_deposit` and has the same payout id. Ignore other bank activity. The unreconciled
total is the sum of each payout id'"'"'s absolute expected-versus-observed difference across the
union of processor and bank payout ids, rounded to cents only at the end.
List every invoice whose invoice currency is EUR. Sort all id lists.'
Output
Fixing your quarter ledger — matching invoices, payouts, and bank deposits.
I've mapped the duplicates and currency cases — now computing the final totals.
Done.
Report written to `report.json` with 3 unpaid invoices, 1 duplicate, 2 EUR invoices, and unreconciled total 868.75 USD.
[stderr]
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> build · muse-spark-1.3-contributor-free
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[0m→ [0mRead .
[0m→ [0mRead invoices.csv
[0m→ [0mRead processor_payouts.csv
[0m→ [0mRead bank.csv
[0m→ [0mRead rates.csv
[0m→ [0mRead TASK.md
[0m
[0m$ [0mpython3 -c "
import csv, collections, json
from decimal import Decimal
# rates
rates={}
with open('rates.csv') as f:
r=csv.DictReader(f)
for row in r:
rates[row['currency']]=Decimal(row['usd_rate'])
print('rates',rates)
# invoices
invoices=[]
with open('invoices.csv') as f:
r=csv.DictReader(f)
for row in r:
invoices.append(row)
# duplicates
from collections import Counter
c=Counter(row['invoice_id'] for row in invoices)
dups=sorted([k for k,v in c.items() if v>1])
print('dups',dups)
# eur
eur=sorted([row['invoice_id'] for row in invoices if row['currency']=='EUR'])
print('eur',eur)
# charges per invoice
charges=collections.defaultdict(Decimal)
# need currency? charge rows currency should match? group by invoice_id sum amount (as Decimal) for type==charge
# Also need to consider invoice currency? Spec: 'total less than its invoice amount in that currency'
# So compare in invoice currency. If charge currency differs? Probably same, but check.
with open('processor_payouts.csv') as f:
r=csv.DictReader(f)
for row in r:
if row['type']=='charge' and row['invoice_id']:
charges[row['invoice_id']]+=Decimal(row['amount'])
# debug currency mismatch
#print(row)
# invoice map: need canonical? For duplicates, exclude. For unpaid check, need amount. If duplicates, skip.
# Also refunded exclude.
# Build invoice dict first occurrence? But for unpaid we exclude dups anyway.
inv_by_id={}
for row in invoices:
inv_by_id.setdefault(row['invoice_id'], row)
# Check all invoices status
unpaid=[]
for inv_id, inv in inv_by_id.items():
if inv['status']=='refunded':
continue
if inv_id in dups:
continue
# only issued? spec says 'An issued invoice is unpaid when...' So only status issued? What about other statuses? Only issued and refunded present. So check issued.
if inv['status']!='issued':
continue
amt=Decimal(inv['amount'])
paid=charges.get(inv_id, Decimal('0'))
if paid < amt:
unpaid.append((inv_id, inv['currency'], str(amt), str(paid)))
unpaid_sorted=sorted([x[0] for x in unpaid])
print('unpaid detail',unpaid)
print('unpaid_sorted',unpaid_sorted)
# reconciliation
from decimal import Decimal
expected=collections.defaultdict(Decimal)
with open('processor_payouts.csv') as f:
r=csv.DictReader(f)
for row in r:
rate=rates[row['currency']]
amt=Decimal(row['amount'])*rate
expected[row['payout_id']]+=amt
observed=collections.defaultdict(Decimal)
with open('bank.csv') as f:
r=csv.DictReader(f)
for row in r:
if row['kind']=='processor_deposit':
observed[row['payout_id']]+=Decimal(row['amount_usd'])
print('expected:',dict(expected))
print('observed:',dict(observed))
union=set(expected)|set(observed)
total=Decimal('0')
for pid in sorted(union):
exp=expected.get(pid,Decimal('0'))
obs=observed.get(pid,Decimal('0'))
diff=abs(exp-obs)
print(pid, f'exp={exp} obs={obs} diff={diff}')
total+=diff
print('total unrounded',total)
print('rounded',round(total,2))
# quantized?
from decimal import ROUND_HALF_UP
print((total).quantize(Decimal('0.01')))
"
rates {'USD': Decimal('1.00'), 'EUR': Decimal('1.10')}
dups ['INV-1042']
eur ['INV-1033', 'INV-1099']
unpaid detail [('INV-1017', 'USD', '151.50', '0'), ('INV-1064', 'USD', '122.50', '0'), ('INV-1088', 'USD', '50.00', '25.00')]
unpaid_sorted ['INV-1017', 'INV-1064', 'INV-1088']
expected: {'PAY-01': Decimal('494.2500'), 'PAY-02': Decimal('428.7500'), 'PAY-03': Decimal('609.7500'), 'PAY-04': Decimal('517.0000'), 'PAY-05': Decimal('270.2500'), 'PAY-06': Decimal('537.5000'), 'PAY-07': Decimal('673.2500'), 'PAY-08': Decimal('407.5000'), 'PAY-09': Decimal('465.2500'), 'PAY-10': Decimal('646.2500'), 'PAY-11': Decimal('580.7500'), 'PAY-12': Decimal('393.0000'), 'PAY-13': Decimal('451.5000'), 'PAY-14': Decimal('755.0000'), 'PAY-15': Decimal('240.7500'), 'PAY-16': Decimal('501.7500'), 'PAY-17': Decimal('682.7500'), 'PAY-18': Decimal('469.0000'), 'PAY-19': Decimal('428.5000'), 'PAY-20': Decimal('634.7500'), 'PAY-21': Decimal('670.5000'), 'PAY-22': Decimal('358.7500'), 'PAY-23': Decimal('540.0000'), 'PAY-24': Decimal('721.2500')}
observed: {'PAY-01': Decimal('494.25'), 'PAY-02': Decimal('428.75'), 'PAY-03': Decimal('609.75'), 'PAY-04': Decimal('499.75'), 'PAY-05': Decimal('270.25'), 'PAY-06': Decimal('537.50'), 'PAY-07': Decimal('673.25'), 'PAY-08': Decimal('407.50'), 'PAY-09': Decimal('465.25'), 'PAY-10': Decimal('646.25'), 'PAY-11': Decimal('580.75'), 'PAY-12': Decimal('393.00'), 'PAY-14': Decimal('755.00'), 'PAY-15': Decimal('240.75'), 'PAY-16': Decimal('501.75'), 'PAY-17': Decimal('682.75'), 'PAY-18': Decimal('469.00'), 'PAY-19': Decimal('428.50'), 'PAY-20': Decimal('634.75'), 'PAY-21': Decimal('670.50'), 'PAY-22': Decimal('358.75'), 'PAY-23': Decimal('540.00'), 'PAY-24': Decimal('721.25'), 'PAY-ORPHAN': Decimal('400.00')}
PAY-01 exp=494.2500 obs=494.25 diff=0.0000
PAY-02 exp=428.7500 obs=428.75 diff=0.0000
PAY-03 exp=609.7500 obs=609.75 diff=0.0000
PAY-04 exp=517.0000 obs=499.75 diff=17.2500
PAY-05 exp=270.2500 obs=270.25 diff=0.0000
PAY-06 exp=537.5000 obs=537.50 diff=0.0000
PAY-07 exp=673.2500 obs=673.25 diff=0.0000
PAY-08 exp=407.5000 obs=407.50 diff=0.0000
PAY-09 exp=465.2500 obs=465.25 diff=0.0000
PAY-10 exp=646.2500 obs=646.25 diff=0.0000
PAY-11 exp=580.7500 obs=580.75 diff=0.0000
PAY-12 exp=393.0000 obs=393.00 diff=0.0000
PAY-13 exp=451.5000 obs=0 diff=451.5000
PAY-14 exp=755.0000 obs=755.00 diff=0.0000
PAY-15 exp=240.7500 obs=240.75 diff=0.0000
PAY-16 exp=501.7500 obs=501.75 diff=0.0000
PAY-17 exp=682.7500 obs=682.75 diff=0.0000
PAY-18 exp=469.0000 obs=469.00 diff=0.0000
PAY-19 exp=428.5000 obs=428.50 diff=0.0000
PAY-20 exp=634.7500 obs=634.75 diff=0.0000
PAY-21 exp=670.5000 obs=670.50 diff=0.0000
PAY-22 exp=358.7500 obs=358.75 diff=0.0000
PAY-23 exp=540.0000 obs=540.00 diff=0.0000
PAY-24 exp=721.2500 obs=721.25 diff=0.0000
PAY-ORPHAN exp=0 obs=400.00 diff=400.00
total unrounded 868.7500
rounded 868.75
868.75
[0m
[0m$ [0mpython3 -c "
import json
report={
'unpaid_invoice_ids': ['INV-1017','INV-1064','INV-1088'],
'unreconciled_total_usd': 868.75,
'duplicate_invoice_ids': ['INV-1042'],
'eur_invoice_ids': ['INV-1033','INV-1099']
}
with open('report.json','w') as f:
json.dump(report,f,indent=2)
f.write('\n')
print(open('report.json').read())
"
{
"unpaid_invoice_ids": [
"INV-1017",
"INV-1064",
"INV-1088"
],
"unreconciled_total_usd": 868.75,
"duplicate_invoice_ids": [
"INV-1042"
],
"eur_invoice_ids": [
"INV-1033",
"INV-1099"
]
}
[0m
Files produced
report.json
{
"unpaid_invoice_ids": [
"INV-1017",
"INV-1064",
"INV-1088"
],
"unreconciled_total_usd": 868.75,
"duplicate_invoice_ids": [
"INV-1042"
],
"eur_invoice_ids": [
"INV-1033",
"INV-1099"
]
}
Checker
{"passed": true, "score": 1.0, "notes": "all four fields exact"}
Judge
Not used for this task.