Instructions

1. Identify and Load Schedule Data

• •Primary Source: Look for a spreadsheet containing the schedule. The user may specify a title (e.g., "NHL 2425 Schedule") or you may need to list available spreadsheets to find it.
• •Confirm Structure: The schedule should contain at minimum: Date, Visitor Team, and Home Team columns. Additional columns (time, scores, status) are acceptable but not required for core analysis.
• •Load Data: Use the google_sheet-get_sheet_data tool to fetch the raw data. Be prepared for large datasets; the tool may return an "overlong" output saved to a file.

2. Parse and Prepare Data

• •Execute the Parsing Script: Run the bundled parse_schedule.py script. It will:
  1. •Load the overlong tool output from the workspace dump directory.
  2. •Extract the Date, Visitor, and Home fields from each row (skipping the header).
  3. •Build a chronological list of games for each team, tagging each game with its location (H for Home, A for Away).
  4. •Sort each team's schedule by date.

3. Perform Back-to-Back Analysis

• •Execute the Analysis Script: Run the bundled analyze_back_to_back.py script. It will:
  1. •For each team, iterate through its sorted game list.
  2. •Identify consecutive game dates (difference of 1 day).
  3. •Categorize each back-to-back set into one of four configurations based on the location sequence:
     • •HA: Home → Away
     • •AH: Away → Home
     • •HH: Home → Home
     • •AA: Away → Away
  4. •Tally the counts for each team and calculate the total.

4. Generate and Deliver Results

• •Create Output Spreadsheet: Create a new Google Sheet with a descriptive title (e.g., "NHL-B2B-Analysis").
• •Rename Default Sheet: Rename the default sheet to something meaningful like "B2B Analysis".
• •Populate Data: Update the sheet with the results. The table must have the exact headers: Team,HA,AH,HH,AA,Total. Populate rows for all teams in alphabetical order.
• •Verify: Optionally, fetch a small range of the new sheet to confirm successful creation and data accuracy.

5. Provide Summary Insights

After completing the analysis, offer a concise verbal summary to the user. Highlight:

• •The team with the most and fewest total back-to-back sets.
• •Notable patterns in configuration distribution (e.g., which configuration is most common league-wide).
• •Any other observations from the data (e.g., teams with unusually high AA counts indicating long road trips).

Key Triggers & Adaptations

• •The skill is triggered by requests for: "back-to-back analysis", "schedule analysis", "home/away breakdown", "travel analysis", or analysis of "schedule patterns".
• •While the example trajectory is for the NHL, the core logic applies to any league (NBA, MLB, NFL) with a similar schedule structure. Ensure the parsing script correctly identifies the relevant column names for the given dataset.
• •If the user requests a different type of schedule analysis (e.g., longest homestand, travel miles), adapt the analysis script logic accordingly, using the parsed team schedule data as a foundation.