diff --git a/.gitignore b/.gitignore
index a0c251a..d41f21e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -19,3 +19,4 @@ challenges/*/terraform/versions.tf
 
 .vscode/
 .idea
+stats.html
\ No newline at end of file
diff --git a/ctf/__main__.py b/ctf/__main__.py
index 0606e1a..daeaceb 100644
--- a/ctf/__main__.py
+++ b/ctf/__main__.py
@@ -13,6 +13,7 @@
 from typing_extensions import Annotated
 
 from ctf import ENV, STATE
+from ctf.askgod import app as askgod_app
 from ctf.check import app as check_app
 from ctf.deploy import app as deploy_app
 from ctf.destroy import app as destroy_app
@@ -33,18 +34,23 @@
     help="CLI tool to manage CTF challenges as code. Run from the root CTF repo directory or set the CTF_ROOT_DIR environment variable to run the tool.",
     no_args_is_help=True,
 )
-app.add_typer(validate_app)
-app.add_typer(init_app)
-app.add_typer(new_app)
+app.add_typer(
+    askgod_app,
+    name="askgod",
+    help="Commands for interacting with a live askgod server (github.com/nsec/askgod).",
+)
+app.add_typer(check_app)
+app.add_typer(deploy_app)
 app.add_typer(destroy_app)
 app.add_typer(flags_app)
-app.add_typer(services_app)
 app.add_typer(generate_app)
-app.add_typer(deploy_app)
+app.add_typer(init_app)
+app.add_typer(list_app)
+app.add_typer(new_app)
 app.add_typer(redeploy_app)
-app.add_typer(check_app)
+app.add_typer(services_app)
 app.add_typer(stats_app)
-app.add_typer(list_app)
+app.add_typer(validate_app)
 app.add_typer(version_app)
 
 
diff --git a/ctf/askgod/__init__.py b/ctf/askgod/__init__.py
new file mode 100644
index 0000000..c22cf6b
--- /dev/null
+++ b/ctf/askgod/__init__.py
@@ -0,0 +1,6 @@
+import typer
+
+from ctf.askgod.stats import app as stats_app
+
+app = typer.Typer()
+app.add_typer(stats_app)
diff --git a/ctf/askgod/stats.py b/ctf/askgod/stats.py
new file mode 100644
index 0000000..070943e
--- /dev/null
+++ b/ctf/askgod/stats.py
@@ -0,0 +1,426 @@
+import json
+from datetime import datetime, timezone
+
+import requests
+import rich
+import typer
+from typing_extensions import Annotated
+
+from ctf.logger import LOG
+
+app = typer.Typer()
+
+
+@app.command(
+    help="Show stats from askgod, specifically regarding to AI agent flag submissions."
+)
+def stats(
+    askgod_url: Annotated[
+        str, typer.Option("--askgod-url", "-u", help="Askgod server URL.")
+    ] = "https://askgod.nsec",
+    html: Annotated[
+        bool, typer.Option("--html", help="Generate an HTML report (stats.html).")
+    ] = False,
+) -> None:
+    stats = {}
+    session = requests.Session()
+    session.base_url = askgod_url + "/1.0"
+    LOG.info(f"Fetching stats from {session.base_url}")
+    flags = get(session, "/flags")
+    scores = get(session, "/scores")
+    scoreboard = get(session, "/scoreboard")
+    # rich.print(flags)
+    # rich.print(scores)
+    # rich.print(scoreboard)
+
+    # Join the flags and scores data together based on flag's `id` and score's `flag_id` by modifying the `scores` list in place
+    for score in scores:
+        flag = next((f for f in flags if f["id"] == score["flag_id"]), None)
+        if flag:
+            score["flag"] = flag["flag"]
+            score["description"] = flag["description"]
+            score["return_string"] = flag["return_string"]
+        else:
+            LOG.warning(
+                f"Could not find flag for score with flag_id {score['flag_id']}"
+            )
+    LOG.info(f"Analyzing {len(scores)} scores...")
+    ai_agent_scores = [s for s in scores if s["ai_agent"]]
+    stats["total_scores"] = len(scores)
+    stats["ai_agent_scores"] = len(ai_agent_scores)
+    stats["ai_agent_score_percentage"] = (
+        round(len(ai_agent_scores) / len(scores) * 100) if scores else 0
+    )
+
+    stats["total_points"] = sum(s["value"] for s in scores)
+    stats["ai_agent_points"] = sum(s["value"] for s in ai_agent_scores)
+    stats["ai_agent_points_percentage"] = (
+        round(stats["ai_agent_points"] / stats["total_points"] * 100)
+        if stats["total_points"]
+        else 0
+    )
+
+    stats["total_teams"] = len(set(s["team_id"] for s in scores))
+    stats["teams_with_ai_agent_scores"] = len(
+        set(s["team_id"] for s in ai_agent_scores)
+    )
+    stats["teams_with_ai_agent_scores_percentage"] = (
+        round(stats["teams_with_ai_agent_scores"] / stats["total_teams"] * 100)
+        if stats["total_teams"]
+        else 0
+    )
+
+    teams_per_quintile = {}
+    # Separate teams into quintiles based on the scoreboard. The rank of a team is its position in the index of the scoreboard
+    for i in range(5):
+        teams_per_quintile[4 - i] = scoreboard[
+            len(scoreboard) // 5 * i : len(scoreboard) // 5 * (i + 1)
+        ]
+    # rich.print(teams_per_quintile)
+
+    stats["ai_agent_points_per_quintile"] = {}
+    for i in range(5):
+        quintile_team_ids = set(t["team"]["id"] for t in teams_per_quintile[i])
+        ai_agent_points_in_quintile = sum(
+            s["value"] for s in ai_agent_scores if s["team_id"] in quintile_team_ids
+        )
+        total_points_in_quintile = sum(
+            s["value"] for s in scores if s["team_id"] in quintile_team_ids
+        )
+        stats["ai_agent_points_per_quintile"][f"quintile_{i + 1}"] = {
+            "ai_agent_points": ai_agent_points_in_quintile,
+            "total_points": total_points_in_quintile,
+            "ai_agent_points_percentage": (
+                round(ai_agent_points_in_quintile / total_points_in_quintile * 100)
+                if total_points_in_quintile
+                else 0
+            ),
+        }
+
+    stats["ai_agent_scores_per_quintile"] = {}
+    for i in range(5):
+        quintile_team_ids = set(t["team"]["id"] for t in teams_per_quintile[i])
+        ai_agent_scores_in_quintile = sum(
+            1 for s in ai_agent_scores if s["team_id"] in quintile_team_ids
+        )
+        total_scores_in_quintile = sum(
+            1 for s in scores if s["team_id"] in quintile_team_ids
+        )
+        stats["ai_agent_scores_per_quintile"][f"quintile_{i + 1}"] = {
+            "ai_agent_scores": ai_agent_scores_in_quintile,
+            "total_scores": total_scores_in_quintile,
+            "ai_agent_scores_percentage": (
+                round(ai_agent_scores_in_quintile / total_scores_in_quintile * 100)
+                if total_scores_in_quintile
+                else 0
+            ),
+        }
+
+    stats["ai_agent_solve_per_point"] = {}
+    for i in range(21):
+        stats["ai_agent_solve_per_point"][i] = {
+            "ai_agent_solves": 0,
+            "total_solves": 0,
+            "ai_agent_solve_percentage": 0,
+        }
+    for score in scores:
+        stats["ai_agent_solve_per_point"][score["value"]]["total_solves"] += 1
+        if score["ai_agent"]:
+            stats["ai_agent_solve_per_point"][score["value"]]["ai_agent_solves"] += 1
+            stats["ai_agent_solve_per_point"][score["value"]][
+                "ai_agent_solve_percentage"
+            ] = round(
+                stats["ai_agent_solve_per_point"][score["value"]]["ai_agent_solves"]
+                / stats["ai_agent_solve_per_point"][score["value"]]["total_solves"]
+                * 100
+            )
+    stats["ai_agent_solve_per_point"] = dict(
+        sorted(stats["ai_agent_solve_per_point"].items(), key=lambda item: item[0])
+    )
+
+    flags_with_ai_solves = len(set(s["flag_id"] for s in ai_agent_scores))
+    total_flags_solved = len(set(s["flag_id"] for s in scores))
+    stats["flags_with_ai_agent_solves"] = flags_with_ai_solves
+    stats["total_flags_solved"] = total_flags_solved
+    stats["percentage_of_flags_with_ai_agent_solves"] = (
+        round(flags_with_ai_solves / total_flags_solved * 100) if scores else 0
+    )
+
+    # Bucket submissions into 4-second intervals and compute AI% per bucket
+    bucket_size = 10
+    buckets: dict[int, dict] = {}
+    for score in scores:
+        t = datetime.fromisoformat(score["submit_time"].replace("Z", "+00:00"))
+        epoch = int(t.timestamp())
+        bucket_key = (epoch // bucket_size) * bucket_size
+        if bucket_key not in buckets:
+            buckets[bucket_key] = {"ai_count": 0, "total_count": 0}
+        buckets[bucket_key]["total_count"] += 1
+        if score["ai_agent"]:
+            buckets[bucket_key]["ai_count"] += 1
+    stats["ai_agent_percentage_over_time"] = [
+        {
+            "bucket_start": datetime.fromtimestamp(k, tz=timezone.utc).strftime(
+                "%a %H:%M:%S"
+            ),
+            "ai_count": v["ai_count"],
+            "total_count": v["total_count"],
+            "ai_percentage": round(v["ai_count"] / v["total_count"] * 100),
+        }
+        for k, v in sorted(buckets.items())
+    ]
+
+    rich.print(stats)
+
+    if html:
+        html_content = generate_html(stats)
+        with open("stats.html", "w") as f:
+            f.write(html_content)
+        LOG.info("HTML report written to stats.html")
+
+
+def generate_html(stats: dict) -> str:
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+
+    quintile_labels = ["Bottom 20%", "20-40%", "40-60%", "60-80%", "Top 20%"]
+
+    points_ai_pct = [
+        stats["ai_agent_points_per_quintile"][f"quintile_{i}"][
+            "ai_agent_points_percentage"
+        ]
+        for i in range(1, 6)
+    ]
+    points_human_pct = [
+        100
+        - stats["ai_agent_points_per_quintile"][f"quintile_{i}"][
+            "ai_agent_points_percentage"
+        ]
+        for i in range(1, 6)
+    ]
+
+    scores_ai_pct = [
+        stats["ai_agent_scores_per_quintile"][f"quintile_{i}"][
+            "ai_agent_scores_percentage"
+        ]
+        for i in range(1, 6)
+    ]
+    scores_human_pct = [
+        100
+        - stats["ai_agent_scores_per_quintile"][f"quintile_{i}"][
+            "ai_agent_scores_percentage"
+        ]
+        for i in range(1, 6)
+    ]
+
+    per_point = stats["ai_agent_solve_per_point"]
+    point_labels = [str(k) for k in per_point if k > 0]
+    point_ai_pct = [
+        per_point[k]["ai_agent_solve_percentage"] for k in per_point if k > 0
+    ]
+    point_human_pct = [100 - v for v in point_ai_pct]
+
+    over_time = stats["ai_agent_percentage_over_time"]
+    time_labels = [b["bucket_start"] for b in over_time]
+    time_ai_pct = [b["ai_percentage"] for b in over_time]
+    time_human_pct = [100 - v for v in time_ai_pct]
+
+    return f"""<!DOCTYPE html>
+<html lang="en">
+<head>
+<meta charset="UTF-8">
+<meta name="viewport" content="width=device-width, initial-scale=1.0">
+<title>AI Agent Stats</title>
+<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+<style>
+  *, *::before, *::after {{ box-sizing: border-box; }}
+  body {{
+    font-family: system-ui, -apple-system, sans-serif;
+    background: #f1f5f9;
+    color: #1e293b;
+    margin: 0 auto;
+    padding: 2rem;
+    max-width: 150em;
+  }}
+  h1 {{ margin: 0 0 0.25rem; font-size: 1.75rem; font-weight: 700; }}
+  .subtitle {{ color: #64748b; font-size: 0.9rem; margin-bottom: 2rem; }}
+  .cards {{
+    display: grid;
+    grid-template-columns: repeat(auto-fill, minmax(160px, 1fr));
+    gap: 1rem;
+    margin-bottom: 2rem;
+  }}
+  .card {{
+    background: #fff;
+    border-radius: 10px;
+    padding: 1.25rem 1rem;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.08);
+  }}
+  .card .value {{
+    font-size: 2rem;
+    font-weight: 700;
+    color: #2563eb;
+    line-height: 1;
+    margin-bottom: 0.35rem;
+  }}
+  .card .label {{
+    font-size: 0.78rem;
+    color: #64748b;
+    text-transform: uppercase;
+    letter-spacing: 0.04em;
+  }}
+  .card .secondary {{
+    font-size: 0.85rem;
+    color: #94a3b8;
+    margin-top: 0.25rem;
+  }}
+  .charts-row {{
+    display: grid;
+    grid-template-columns: 1fr 1fr;
+    gap: 1rem;
+    margin-bottom: 1rem;
+  }}
+  .chart-box {{
+    background: #fff;
+    border-radius: 10px;
+    padding: 1.5rem;
+    box-shadow: 0 1px 3px rgba(0,0,0,0.08);
+  }}
+  .chart-box h2 {{
+    font-size: 1rem;
+    font-weight: 600;
+    margin: 0 0 1rem;
+    color: #334155;
+  }}
+  @media (max-width: 700px) {{
+    .charts-row {{ grid-template-columns: 1fr; }}
+  }}
+</style>
+</head>
+<body>
+<h1>AI Agent Stats</h1>
+<p class="subtitle">Generated {timestamp}</p>
+
+<div class="cards">
+  <div class="card"><div class="value">{stats["ai_agent_score_percentage"]}%</div><div class="label">Valid Flags submitted by an AI agent</div><div class="secondary">{stats["ai_agent_scores"]} / {stats["total_scores"]}</div></div>
+  <div class="card"><div class="value">{stats["ai_agent_points_percentage"]}%</div><div class="label">Points scored by AI Agents</div><div class="secondary">{stats["ai_agent_points"]} / {stats["total_points"]}</div></div>
+  <div class="card"><div class="value">{stats["teams_with_ai_agent_scores_percentage"]}%</div><div class="label">Teams Using AI Agents</div><div class="secondary">{stats["teams_with_ai_agent_scores"]} / {stats["total_teams"]}</div></div>
+  <div class="card"><div class="value">{stats["percentage_of_flags_with_ai_agent_solves"]}%</div><div class="label">Solved Flags w/ at least one agent Solve</div><div class="secondary">{stats["flags_with_ai_agent_solves"]} / {stats["total_flags_solved"]}</div></div>
+</div>
+
+<div class="charts-row">
+  <div class="chart-box">
+    <h2>Points per Quintile</h2>
+    <canvas id="pointsChart"></canvas>
+  </div>
+  <div class="chart-box">
+    <h2>Solves per Quintile</h2>
+    <canvas id="scoresChart"></canvas>
+  </div>
+</div>
+
+<div class="charts-row">
+  <div class="chart-box">
+    <h2>AI Solve % by Flag Point Value</h2>
+    <canvas id="perPointChart"></canvas>
+  </div>
+  <div class="chart-box">
+    <h2>AI Submission % over Time (4s buckets)</h2>
+    <canvas id="overTimeChart"></canvas>
+  </div>
+</div>
+
+<script>
+const quintileLabels = {json.dumps(quintile_labels)};
+const pointsAI = {json.dumps(points_ai_pct)};
+const pointsHuman = {json.dumps(points_human_pct)};
+const scoresAI = {json.dumps(scores_ai_pct)};
+const scoresHuman = {json.dumps(scores_human_pct)};
+const pointLabels = {json.dumps(point_labels)};
+const pointAIPct = {json.dumps(point_ai_pct)};
+const pointHumanPct = {json.dumps(point_human_pct)};
+const timeLabels = {json.dumps(time_labels)};
+const timeAIPct = {json.dumps(time_ai_pct)};
+const timeHumanPct = {json.dumps(time_human_pct)};
+
+const stackedOpts = {{
+  responsive: true,
+  plugins: {{
+    legend: {{ position: 'bottom' }},
+    tooltip: {{
+      callbacks: {{
+        label: ctx => `${{ctx.dataset.label}}: ${{ctx.parsed.y}}%`
+      }}
+    }}
+  }},
+  scales: {{
+    x: {{ stacked: true }},
+    y: {{ stacked: true, beginAtZero: true, max: 100, ticks: {{ callback: v => v + '%' }} }}
+  }}
+}};
+
+new Chart(document.getElementById('pointsChart'), {{
+  type: 'bar',
+  data: {{
+    labels: quintileLabels,
+    datasets: [
+      {{ label: 'AI Agent', data: pointsAI, backgroundColor: '#2563eb' }},
+      {{ label: 'Human', data: pointsHuman, backgroundColor: '#cbd5e1' }}
+    ]
+  }},
+  options: stackedOpts
+}});
+
+new Chart(document.getElementById('scoresChart'), {{
+  type: 'bar',
+  data: {{
+    labels: quintileLabels,
+    datasets: [
+      {{ label: 'AI Agent', data: scoresAI, backgroundColor: '#2563eb' }},
+      {{ label: 'Human', data: scoresHuman, backgroundColor: '#cbd5e1' }}
+    ]
+  }},
+  options: stackedOpts
+}});
+
+new Chart(document.getElementById('perPointChart'), {{
+  type: 'bar',
+  data: {{
+    labels: pointLabels,
+    datasets: [
+      {{ label: 'AI Agent', data: pointAIPct, backgroundColor: '#2563eb' }},
+      {{ label: 'Human', data: pointHumanPct, backgroundColor: '#cbd5e1' }}
+    ]
+  }},
+  options: stackedOpts
+}});
+
+new Chart(document.getElementById('overTimeChart'), {{
+  type: 'bar',
+  data: {{
+    labels: timeLabels,
+    datasets: [
+      {{ label: 'AI Agent', data: timeAIPct, backgroundColor: '#2563eb' }},
+      {{ label: 'Human', data: timeHumanPct, backgroundColor: '#cbd5e1' }}
+    ]
+  }},
+  options: {{
+    ...stackedOpts,
+    scales: {{
+      ...stackedOpts.scales,
+      x: {{ ...stackedOpts.scales.x, ticks: {{ maxRotation: 45, autoSkip: true, maxTicksLimit: 20 }} }}
+    }}
+  }}
+}});
+</script>
+</body>
+</html>"""
+
+
+def get(session: requests.Session, url: str) -> dict:
+    try:
+        response = session.get(url=f"{session.base_url}{url}")
+        response.raise_for_status()
+        return response.json()
+    except requests.HTTPError as e:
+        e.add_note(f"Failed to fetch stats from {e.request.url}: {e.text}")
+        raise e
diff --git a/poetry.lock b/poetry.lock
index df1785b..7f80110 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -81,10 +81,9 @@ uvloop = ["uvloop (>=0.15.2)"]
 name = "certifi"
 version = "2025.7.14"
 description = "Python package for providing Mozilla's CA Bundle."
-optional = true
+optional = false
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"workflow\""
 files = [
     {file = "certifi-2025.7.14-py3-none-any.whl", hash = "sha256:6b31f564a415d79ee77df69d757bb49a5bb53bd9f756cbbe24394ffd6fc1f4b2"},
     {file = "certifi-2025.7.14.tar.gz", hash = "sha256:8ea99dbdfaaf2ba2f9bac77b9249ef62ec5218e7c2b2e903378ed5fccf765995"},
@@ -94,10 +93,9 @@ files = [
 name = "charset-normalizer"
 version = "3.4.2"
 description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-optional = true
+optional = false
 python-versions = ">=3.7"
 groups = ["main"]
-markers = "extra == \"workflow\""
 files = [
     {file = "charset_normalizer-3.4.2-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:7c48ed483eb946e6c04ccbe02c6b4d1d48e51944b6db70f697e089c193404941"},
     {file = "charset_normalizer-3.4.2-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b2d318c11350e10662026ad0eb71bb51c7812fc8590825304ae0bdd4ac283acd"},
@@ -386,10 +384,9 @@ woff = ["brotli (>=1.0.1) ; platform_python_implementation == \"CPython\"", "bro
 name = "idna"
 version = "3.10"
 description = "Internationalized Domain Names in Applications (IDNA)"
-optional = true
+optional = false
 python-versions = ">=3.6"
 groups = ["main"]
-markers = "extra == \"workflow\""
 files = [
     {file = "idna-3.10-py3-none-any.whl", hash = "sha256:946d195a0d259cbba61165e88e65941f16e9b36ea6ddb97f00452bae8b1287d3"},
     {file = "idna-3.10.tar.gz", hash = "sha256:12f65c9b470abda6dc35cf8e63cc574b1c52b11df2c86030af0ac09b01b13ea9"},
@@ -1263,10 +1260,9 @@ typing-extensions = {version = ">=4.4.0", markers = "python_version < \"3.13\""}
 name = "requests"
 version = "2.32.4"
 description = "Python HTTP for Humans."
-optional = true
+optional = false
 python-versions = ">=3.8"
 groups = ["main"]
-markers = "extra == \"workflow\""
 files = [
     {file = "requests-2.32.4-py3-none-any.whl", hash = "sha256:27babd3cda2a6d50b30443204ee89830707d396671944c998b5975b031ac2b2c"},
     {file = "requests-2.32.4.tar.gz", hash = "sha256:27d0316682c8a29834d3264820024b62a36942083d52caf2f14c0591336d3422"},
@@ -1591,10 +1587,9 @@ typing-extensions = ">=4.12.0"
 name = "urllib3"
 version = "2.5.0"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
-optional = true
+optional = false
 python-versions = ">=3.9"
 groups = ["main"]
-markers = "extra == \"workflow\""
 files = [
     {file = "urllib3-2.5.0-py3-none-any.whl", hash = "sha256:e6b01673c0fa6a13e374b50871808eb3bf7046c4b125b216f6bf1cc604cff0dc"},
     {file = "urllib3-2.5.0.tar.gz", hash = "sha256:3fc47733c7e419d4bc3f6b3dc2b4f890bb743906a30d56ba4a5bfa4bbff92760"},
@@ -1612,4 +1607,4 @@ workflow = ["matplotlib", "pybadges", "standard-imghdr"]
 [metadata]
 lock-version = "2.1"
 python-versions = ">=3.11"
-content-hash = "f1c4f5b6299867368584046e3684bf12839972b619fe5b6b14e7b5b21ef590db"
+content-hash = "caed93386cad5888f8abea3bc094974913cce04350749aca2857832c4b3a140d"
diff --git a/pyproject.toml b/pyproject.toml
index f997e27..0f554c4 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,6 +13,7 @@ dependencies = [
     "jinja2==3.1.5",
     "jsonschema==4.23.0",
     "pyyaml<7",
+    "requests",
     "rich==14.1.0",
     "ruff==0.12.8",
     "setuptools",