diff --git a/pipelines.py b/pipelines.py
index eacae19..6d633c6 100644
--- a/pipelines.py
+++ b/pipelines.py
@@ -1189,6 +1189,120 @@ def recreate_reg_data(term="fa25"):
         writer.writerow(header_row)
         writer.writerows(table)
 
+def cvc_report(input_csv="cache/cvc_fa25.csv", out_dir="cache"):
+    import os, re
+    import pandas as pd
+
+    os.makedirs(out_dir, exist_ok=True)
+
+    # --- Load & normalize ---
+    df = pd.read_csv(input_csv, dtype=str).fillna("")
+    # Trim header/values
+    df.columns = [c.strip() for c in df.columns]
+    for c in ["Status","Home College name","Course","CCCID","Created On"]:
+        if c in df.columns:
+            df[c] = df[c].astype(str).str.strip()
+
+    # Parse dates for sorting (keep originals)
+    def try_parse(s):
+        # tolerate 'YYYY-MM-DD hh:mm:ss PDT' and blanks
+        try:
+            return pd.to_datetime(s.replace(" PDT","").replace(" PST",""), errors="coerce")
+        except Exception:
+            return pd.NaT
+    df["_created"] = df["Created On"].apply(try_parse)
+
+    # Sort by "Created On" then "CCCID"
+    df = df.sort_values(by=["_created","CCCID"], kind="mergesort")
+
+    # Group by CCCID (for student-level view)
+    # Define gc_student per row, then roll up per student (1 if any row shows Gavilan as Home)
+    df["gc_student"] = (df["Home College name"] == "Gavilan College").astype(int)
+
+    # Normalize status and success flag
+    status_norm = df["Status"].str.lower().str.strip()
+    df["_status"] = status_norm
+    df["success"] = (df["_status"] == "validated").astype(int)
+
+    # Department code = leading letters before first number in "Course"
+    def dept_from_course(s):
+        # e.g., "CD32 - Intro..." -> "CD"
+        m = re.match(r"\s*([A-Za-z]+)", s or "")
+        return m.group(1).upper() if m else "UNKNOWN"
+    df["dept"] = df["Course"].apply(dept_from_course)
+
+    # --- Student-level counts (unique CCCID) ---
+    # For each student, take gc_student = max over their rows
+    if "CCCID" not in df.columns or df["CCCID"].eq("").all():
+        # Fallback: if CCCID missing, treat each row as a unique student
+        stu = df.assign(_stu_id=df.index.astype(str)).groupby("_stu_id", as_index=False)["gc_student"].max()
+    else:
+        stu = df[df["CCCID"]!=""].groupby("CCCID", as_index=False)["gc_student"].max()
+
+    students_by_gc = stu.groupby("gc_student", as_index=False).size().rename(columns={"size":"count_students"})
+    students_by_gc.to_csv(os.path.join(out_dir, "students_by_gc.csv"), index=False)
+
+    # --- Outcome tallies by gc_student (row-level: sections/attempts) ---
+    outcomes_by_gc = df.groupby(["gc_student","success"], as_index=False).size().rename(columns={"size":"count_rows"})
+    outcomes_by_gc.to_csv(os.path.join(out_dir, "outcomes_by_gc.csv"), index=False)
+
+    # --- Department tallies, split by success and gc_student (row-level) ---
+    dept_by_gc_success = (
+        df.groupby(["gc_student","success","dept"], as_index=False)
+          .size().rename(columns={"size":"count_rows"})
+          .sort_values(["gc_student","success","dept"])
+    )
+    dept_by_gc_success.to_csv(os.path.join(out_dir, "dept_by_gc_success.csv"), index=False)
+
+    # --- Unsuccessful reasons (status) by gc_student ---
+    reasons_by_gc = (
+        df[df["success"] == 0]
+        .assign(reason=df["_status"].replace("", "unknown"))
+        .groupby(["gc_student","reason"], as_index=False)
+        .size().rename(columns={"size":"count_rows"})
+        .sort_values(["gc_student","reason"])
+    )
+    reasons_by_gc.to_csv(os.path.join(out_dir, "reasons_by_gc.csv"), index=False)
+
+    # Optional: quick prints for sanity
+    print("Wrote:")
+    for name in ["students_by_gc.csv","outcomes_by_gc.csv","dept_by_gc_success.csv","reasons_by_gc.csv"]:
+        print(" -", os.path.join(out_dir, name))
+
+    # Write to one Excel file with multiple sheets
+    output_xlsx = "cache/csv_fa25.xlsx"
+    with pd.ExcelWriter(output_xlsx, engine="openpyxl") as writer:
+        students_by_gc.to_excel(writer, sheet_name="students_by_gc", index=False)
+        outcomes_by_gc.to_excel(writer, sheet_name="outcomes_by_gc", index=False)
+        dept_by_gc_success.to_excel(writer, sheet_name="dept_by_gc_success", index=False)
+        reasons_by_gc.to_excel(writer, sheet_name="reasons_by_gc", index=False)
+
+    print("Wrote Excel workbook:", output_xlsx)
+
+
+
+
+
+def test_starfish():
+    api3 = f'{url}/api/v1/courses/29/gradebook_history/feed'
+    print(f"\n\ntesting: {api3}\n\n")
+    r3 = fetch(api3)
+    print(r3)
+
+    return
+
+    api1 = f'{url}/api/v1/courses/29/enrollments'
+    print(f"\n\ntesting: {api1}\n\n")
+    r1 = fetch(api1)
+    print(r1)
+
+    api2 = f'{url}/api/v1/courses/29/assignments'
+
+    print(f"\n\ntesting: {api2}\n\n")
+    r2 = fetch(api2)
+    print(r2)
+
+
 
 if __name__ == "__main__":
     
@@ -1200,6 +1314,8 @@ if __name__ == "__main__":
                 5: ['Create narrative format all semesters', recreate_all],
                 6: ['Recreate reg_data from full reg history', recreate_reg_data],
                 7: ['Compute enrollment changes', compute_enrollment_changes],
+                8: ['cvc report parse', cvc_report],
+                9: ['test starfish account', test_starfish],
     }
         
     '''1: ['Re-create schedule csv and json files from raw html',recent_schedules] ,