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tasks/0001_840_1840182_qa_4/task.toml

@@ -15,6 +15,7 @@ gold_answer = "Pinot Noir"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 1
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0001_878_1878746_qa_4/task.toml

@@ -15,6 +15,7 @@ gold_answer = "320"
 reward_mode_initial = "numeric"
 package_tier = 0
 difficulty_level = 1
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0001_990_1990794_qa_2/task.toml

@@ -15,6 +15,7 @@ gold_answer = "Medium"
 reward_mode_initial = "exact_short"
 package_tier = 0
 difficulty_level = 1
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0039_815_39815076_qa_3/task.toml

@@ -15,6 +15,7 @@ gold_answer = "auto"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 4
+difficulty_tier = "hard"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0041_652_41652253_qa_1/task.toml

@@ -15,6 +15,7 @@ gold_answer = "0.664159783972475"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0042_124_42124908_qa_1/task.toml

@@ -15,6 +15,7 @@ gold_answer = "517666.39"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 4
+difficulty_tier = "hard"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0045_006_45006527_qa_2/task.toml

@@ -15,6 +15,7 @@ gold_answer = "0.466581"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0057_778_57778333_qa_3/task.toml

@@ -15,6 +15,7 @@ gold_answer = "5324.25"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 1
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0060_240_60240221_qa_3/task.toml

@@ -15,6 +15,7 @@ gold_answer = "1220"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0061_109_61109875_qa_1/task.toml

@@ -15,6 +15,7 @@ gold_answer = "434"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0062_755_62755543_qa_2/task.toml

@@ -15,6 +15,7 @@ gold_answer = "374"
 reward_mode_initial = "numeric"
 package_tier = 2
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0067_241_67241146_qa_2/task.toml

@@ -15,6 +15,7 @@ gold_answer = "17"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 1
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0067_290_67290658_qa_1/task.toml

@@ -15,6 +15,7 @@ gold_answer = "yes"
 reward_mode_initial = "exact_bool"
 package_tier = 1
 difficulty_level = 4
+difficulty_tier = "hard"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0073_939_73939724_qa_2/task.toml

@@ -15,6 +15,7 @@ gold_answer = "J"
 reward_mode_initial = "exact_short"
 package_tier = 2
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0074_739_74739274_qa_3/task.toml

@@ -15,6 +15,7 @@ gold_answer = "16.68"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 3
+difficulty_tier = "medium"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0075_210_75210599_qa_3/task.toml

@@ -15,6 +15,7 @@ gold_answer = "2"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 4
+difficulty_tier = "hard"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0077_133_77133974_qa_4/task.toml

@@ -15,6 +15,7 @@ gold_answer = "COMPUTATIONAL"
 reward_mode_initial = "exact_short"
 package_tier = 0
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0080_564_80564946_qa_4/instruction.md

@@ -0,0 +1,15 @@
+You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
+
+Files (in /home/user/input, no subfolders):
+- car_evaluation.csv
+
+Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
+
+Question:
+What is the F1 score on the test set for the Decision Tree model?
+
+Work it out step by step — inspect the data first (head, shape, dtypes), then compute.
+
+Answer with a single clean value: a bare number (no commas or units, e.g. 95293), a short label, yes/no, or a comma-separated list. Keep decimal precision. If there's no applicable answer, write: Not Applicable
+
+Write only that value to /workdir/answer.txt (e.g. `echo -n "<value>" > /workdir/answer.txt`), then stop.

tasks/0080_564_80564946_qa_4/task.toml

@@ -0,0 +1,64 @@
+schema_version = "1.2"
+artifacts = []
+
+[task]
+name = "data-agent-eval-v1/0080_564_80564946_qa_4"
+description = "What is the F1 score on the test set for the Decision Tree model?"
+authors = []
+keywords = ["data-agent", "data-analysis", "kaggle"]
+
+[metadata]
+source_dataset = "jupyter-agent/jupyter-agent-dataset"
+source_row_id = "0080/564/80564946.ipynb_qa_4"
+kaggle_dataset_name = "elikplim/car-evaluation-data-set"
+gold_answer = "0.92"
+reward_mode_initial = "numeric"
+package_tier = 1
+difficulty_level = 4
+difficulty_tier = "hard"
+
+[environment]
+build_timeout_sec = 600.0
+os = "linux"
+cpus = 1
+memory_mb = 1024
+storage_mb = 5120
+gpus = 0
+allow_internet = true
+mcp_servers = []
+
+# Pre-agent hook: Harbor runs the command AFTER container start and BEFORE the
+# agent setup begins. We use it to pull this task's bucket prefix into
+# /home/user/input/. See environment/pull_bucket.py.
+[environment.healthcheck]
+command = "python3 /opt/pull_bucket.py && [ -n \"$(ls /home/user/input)\" ]"
+interval_sec = 2.0
+timeout_sec = 180.0
+start_period_sec = 5.0
+start_interval_sec = 2.0
+retries = 30
+
+[environment.env]
+HF_BUCKET = "AdithyaSK/jupyter-agent-kaggle-all"
+BUCKET_PREFIX = "elikplim__car-evaluation-data-set"
+HF_TOKEN = "${HF_TOKEN}"
+KAGGLE_DATASET_NAME = "elikplim/car-evaluation-data-set"
+
+[verifier]
+timeout_sec = 120.0
+
+[verifier.env]
+EXPECTED_ANSWER = "0.9754"
+QUESTION = "What is the F1 score on the test set for the Decision Tree model?"
+REWARD_MODE = "numeric"
+ATOL = "0.08"
+RTOL = "0.01"
+
+[agent]
+# Capped at 600s (10 min) to kill the long-tail stuck-agent cases without
+# cutting off legitimate complex trials. Median Phase B trial is 60-120s;
+# legitimate L4/L5 tasks can hit 200-300s; anything past 600s is almost
+# certainly a stuck agent loop.
+timeout_sec = 600.0
+
+[solution.env]

tasks/0083_022_83022280_qa_3/task.toml

@@ -15,6 +15,7 @@ gold_answer = "20"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0095_666_95666563_qa_5/task.toml

@@ -15,6 +15,7 @@ gold_answer = "13.4"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0097_409_97409368_qa_2/task.toml

@@ -15,6 +15,7 @@ gold_answer = "2"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 2
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0100_171_100171054_qa_4/task.toml

@@ -15,6 +15,7 @@ gold_answer = "0.602"
 reward_mode_initial = "numeric"
 package_tier = 1
 difficulty_level = 4
+difficulty_tier = "hard"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0116_992_116992800_qa_2/task.toml

@@ -15,6 +15,7 @@ gold_answer = "no"
 reward_mode_initial = "exact_bool"
 package_tier = 2
 difficulty_level = 1
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0

tasks/0118_014_118014024_qa_4/instruction.md

@@ -0,0 +1,15 @@
+You are a data-analysis agent working in a sandbox. Use your code-execution tool to inspect the files and compute the answer.
+
+Files (in /home/user/input, no subfolders):
+- (see /home/user/input)
+
+Installed: pandas, numpy, matplotlib, seaborn, scipy, scikit-learn, statsmodels, tabulate, sqlite3, plotly (pip install more if needed).
+
+Question:
+What is the median BMI value in the Pima Indians Diabetes dataset?
+
+Work it out step by step — inspect the data first (head, shape, dtypes), then compute.
+
+Answer with a single clean value: a bare number (no commas or units, e.g. 95293), a short label, yes/no, or a comma-separated list. Keep decimal precision. If there's no applicable answer, write: Not Applicable
+
+Write only that value to /workdir/answer.txt (e.g. `echo -n "<value>" > /workdir/answer.txt`), then stop.

tasks/0118_014_118014024_qa_4/task.toml

@@ -0,0 +1,64 @@
+schema_version = "1.2"
+artifacts = []
+
+[task]
+name = "data-agent-eval-v1/0118_014_118014024_qa_4"
+description = "What is the median BMI value in the Pima Indians Diabetes dataset?"
+authors = []
+keywords = ["data-agent", "data-analysis", "kaggle"]
+
+[metadata]
+source_dataset = "jupyter-agent/jupyter-agent-dataset"
+source_row_id = "0118/014/118014024.ipynb_qa_4"
+kaggle_dataset_name = "uciml/pima-indians-diabetes-database"
+gold_answer = "32.0"
+reward_mode_initial = "numeric"
+package_tier = 0
+difficulty_level = 1
+difficulty_tier = "easy"
+
+[environment]
+build_timeout_sec = 600.0
+os = "linux"
+cpus = 1
+memory_mb = 1024
+storage_mb = 5120
+gpus = 0
+allow_internet = true
+mcp_servers = []
+
+# Pre-agent hook: Harbor runs the command AFTER container start and BEFORE the
+# agent setup begins. We use it to pull this task's bucket prefix into
+# /home/user/input/. See environment/pull_bucket.py.
+[environment.healthcheck]
+command = "python3 /opt/pull_bucket.py && [ -n \"$(ls /home/user/input)\" ]"
+interval_sec = 2.0
+timeout_sec = 180.0
+start_period_sec = 5.0
+start_interval_sec = 2.0
+retries = 30
+
+[environment.env]
+HF_BUCKET = "AdithyaSK/jupyter-agent-kaggle-all"
+BUCKET_PREFIX = "uciml__pima-indians-diabetes-database"
+HF_TOKEN = "${HF_TOKEN}"
+KAGGLE_DATASET_NAME = "uciml/pima-indians-diabetes-database"
+
+[verifier]
+timeout_sec = 120.0
+
+[verifier.env]
+EXPECTED_ANSWER = "32.0"
+QUESTION = "What is the median BMI value in the Pima Indians Diabetes dataset?"
+REWARD_MODE = "numeric"
+ATOL = "0.05"
+RTOL = "0.01"
+
+[agent]
+# Capped at 600s (10 min) to kill the long-tail stuck-agent cases without
+# cutting off legitimate complex trials. Median Phase B trial is 60-120s;
+# legitimate L4/L5 tasks can hit 200-300s; anything past 600s is almost
+# certainly a stuck agent loop.
+timeout_sec = 600.0
+
+[solution.env]

tasks/0131_881_131881806_qa_4/task.toml

@@ -15,6 +15,7 @@ gold_answer = "AUTOMATIC"
 reward_mode_initial = "exact_short"
 package_tier = 1
 difficulty_level = 1
+difficulty_tier = "easy"
 
 [environment]
 build_timeout_sec = 600.0