Args:
    track: Track name to filter by.
        ``"creative-apps"`` | ``"reasoning-agents"`` | ``"enterprise-agents"`` | None (all)
    state: Issue state. ``"open"`` | ``"closed"`` | ``"all"``

Returns:
    A list of submission summaries. Each element is a dictionary
    containing issue_number, title, track, project_name, repo_url,
    created_at, has_demo.

Raises:
    RuntimeError: When gh command execution fails.

Parses each section of the Issue template and returns scoring data.
GitHub Username is hidden during scoring to eliminate bias, but
retained as the github_username field for report output.
If repo_url points to a GitHub repository, the README is also fetched.

Args:
    issue_number: The Issue number to fetch.

Returns:
    A dictionary containing detailed submission information.

Raises:
    RuntimeError: When gh command execution fails.

Loads the YAML file ``data/rubrics/{track}.yaml`` and returns
the scoring criteria (name, weight, description, scoring_guide).

Args:
    track: Track name. ``"creative-apps"`` | ``"reasoning-agents"``
        | ``"enterprise-agents"``

Returns:
    Rubric dict with track, track_display_name, criteria (list),
    total_weight, score_range, and notes.

Raises:
    FileNotFoundError: If the YAML file for the track does not exist.
    ValueError: If the track name is invalid.

Existing scores for the same Issue are overwritten (idempotent).
New Issues are appended.

Args:
    scores: List of scoring result dicts. Each must contain:
        - issue_number (int)
        - project_name (str)
        - track (str)
        - criteria_scores (dict[str, int]): per-criterion scores (1-10)
        - weighted_total (float): weighted total (0-100)
        - evidence (dict[str, str]): per-criterion evidence citations
        - confidence (str): 'high', 'medium', or 'low'
        - red_flags_detected (list[str]): red flag signals found
        - bonus_signals_detected (list[str]): bonus signals found
        - strengths (list[str])
        - improvements (list[str])
        - summary (str)

Returns:
    Summary dict (saved_count, updated_count, total_in_store, file_path).

Raises:
    OSError: If disk write fails.

Reads scoring results from data/scores.json and produces a report
containing overall ranking, per-track ranking, and individual
evaluation summaries.

Args:
    top_n: Number of top entries to highlight (default: 10).

Returns:
    Result dict (report_path, total_scored, top_n, top_entries).