diff --git a/docs/2026-08-09_leaderboard-charts.md b/docs/2026-08-09_leaderboard-charts.md
new file mode 100644
index 00000000..01a471e4
--- /dev/null
+++ b/docs/2026-08-09_leaderboard-charts.md
@@ -0,0 +1,80 @@
+# Leaderboard chart generation
+
+AssetOpsBench can generate publication-ready leaderboard charts directly from
+the structured evaluation report. Chart values are derived from individual
+`ScenarioResult` records in `EvalReport.results`; they are not hard-coded or
+scraped from README images or papers.
+
+## Install
+
+Matplotlib is optional so evaluation users who do not create charts do not
+need to install it:
+
+```bash
+uv sync --dev --group visualization
+```
+
+You can also select the group for one command with `uv run --group
+visualization ...`.
+
+## Generate charts
+
+```bash
+uv run --group visualization evaluate \
+  --trajectories traces/trajectories \
+  --scenarios groundtruth/*.json \
+  --scorer-default llm_judge \
+  --judge-model litellm_proxy/azure/gpt-5.4 \
+  --reports-dir reports \
+  --charts
+```
+
+Chart rendering runs after evaluation from the completed report and does not
+make an additional LLM call. Output is deterministic by runner name:
+
+```text
+reports/
+├── _aggregate.json
+└── charts/
+    ├── leaderboard-<runner>.svg
+    └── leaderboard-<runner>.png
+```
+
+SVG is the preferred README and publication format. PNG is provided for quick
+preview. Runner names that are not filesystem-safe receive a stable sanitized
+name and hash suffix.
+
+## Aggregation rules
+
+The compact leaderboard includes task completion, data retrieval accuracy,
+and generalized result verification. For each runner, model, and criterion,
+the success percentage is the number of `true` outcomes divided by the number
+of results containing an actual Boolean value for that criterion.
+
+- Only results produced by `llm_judge` are included.
+- Missing, null, numeric, and string values are excluded from the denominator.
+- Models and runners are aggregated independently.
+- The optional raw `hallucinations` criterion is inverted and presented as
+  “Hallucination-free,” so its positive meaning is explicit.
+
+The historical README leaderboard predates this report pipeline and has no
+checked-in `EvalReport` that can reproduce its published values. It remains
+unchanged; future leaderboard figures can be generated reproducibly with this
+command.
+
+## Accessibility measures
+
+The renderer follows Carbon's official
+[categorical palette](https://carbondesignsystem.com/data-visualization/color-palettes/)
+in its documented order because models are discrete categories. Each model
+also receives a hatch pattern and dark outline, and its style is kept
+consistent across every runner in the same report. Charts use a zero baseline,
+direct percentage labels, a percent scale, readable legends, and `N/A` gaps
+instead of treating missing criteria as failures. SVG text remains selectable,
+and each SVG includes image semantics plus a description containing the plotted
+models, percentages, and Boolean counts. PNG metadata carries the same data.
+
+These measures improve color-vision, grayscale, and reduced-size readability;
+they are not a claim of complete accessibility conformance. When publishing a
+chart, provide meaningful surrounding text or alternative text and retain the
+canonical JSON report for readers who need the underlying values.
diff --git a/docs/evaluation.md b/docs/evaluation.md
index 70174791..5a04247b 100644
--- a/docs/evaluation.md
+++ b/docs/evaluation.md
@@ -193,9 +193,15 @@ uv run evaluate \
   [--reports-dir DIR]            # default: reports/
   [--scorer-default NAME]        # default: llm_judge
   [--judge-model MODEL_ID]       # required when llm_judge runs
+  [--charts]                     # optional SVG/PNG leaderboards
   [-v]
 ```
 
+`--charts` derives one leaderboard per runner from the completed
+`EvalReport` and writes it under `<reports-dir>/charts`. See
+[Leaderboard chart generation](2026-08-09_leaderboard-charts.md) for the
+optional dependency, aggregation rules, outputs, and accessibility measures.
+
 
 ## Available scorers in this branch
 
diff --git a/pyproject.toml b/pyproject.toml
index 10d366d8..6e67d920 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -78,6 +78,10 @@ otel = [
     "opentelemetry-exporter-otlp-proto-http>=1.27.0",
     "opentelemetry-instrumentation-httpx>=0.48b0",
 ]
+# Optional static SVG/PNG leaderboard generation for evaluation reports.
+visualization = [
+    "matplotlib>=3.10",
+]
 
 [tool.uv]
 package = true
diff --git a/src/evaluation/cli.py b/src/evaluation/cli.py
index 475546d7..7a97a635 100644
--- a/src/evaluation/cli.py
+++ b/src/evaluation/cli.py
@@ -58,8 +58,7 @@ def _build_parser() -> argparse.ArgumentParser:
         "--scorer-default",
         dest="scorer_default",
         default="llm_judge",
-        help="Scorer name when scenario.scoring_method is unset. "
-        "Default: llm_judge.",
+        help="Scorer name when scenario.scoring_method is unset. Default: llm_judge.",
     )
     p.add_argument(
         "--judge-model",
@@ -68,6 +67,14 @@ def _build_parser() -> argparse.ArgumentParser:
         "litellm_proxy/anthropic/claude-opus-4-5). "
         "Required when any scenario routes to llm_judge.",
     )
+    p.add_argument(
+        "--charts",
+        action="store_true",
+        help=(
+            "Generate report-derived SVG and PNG leaderboards under "
+            "<reports-dir>/charts. Requires the visualization dependency group."
+        ),
+    )
     p.add_argument(
         "-v",
         "--verbose",
@@ -114,6 +121,17 @@ def main(argv: list[str] | None = None) -> int:
         format="%(asctime)s %(levelname)s %(name)s: %(message)s",
     )
 
+    if args.charts:
+        from .visualization import (
+            VisualizationDependencyError,
+            require_visualization_dependency,
+        )
+
+        try:
+            require_visualization_dependency()
+        except VisualizationDependencyError as exc:
+            parser.error(str(exc))
+
     try:
         scenario_ids = _resolve_scenario_ids(args.scenario_ids)
     except (FileNotFoundError, ValueError) as exc:
@@ -131,8 +149,21 @@ def main(argv: list[str] | None = None) -> int:
     )
 
     out_dir = write_reports_dir(report, args.reports_dir)
+    chart_paths: tuple[Path, ...] = ()
+    if args.charts:
+        from .visualization import render_leaderboards
+
+        chart_paths = render_leaderboards(report, out_dir / "charts")
     print(render_summary(report))
     print(f"\nAggregate report written: {out_dir}/_aggregate.json")
+    if args.charts:
+        if chart_paths:
+            print(f"Leaderboard charts written: {out_dir}/charts")
+        else:
+            print(
+                "No leaderboard charts generated: the report has no applicable "
+                "Boolean llm_judge criterion results."
+            )
     return 0
 
 
diff --git a/src/evaluation/tests/test_cli.py b/src/evaluation/tests/test_cli.py
index dc050bbd..115a33ac 100644
--- a/src/evaluation/tests/test_cli.py
+++ b/src/evaluation/tests/test_cli.py
@@ -2,7 +2,15 @@
 
 from __future__ import annotations
 
+from pathlib import Path
+
+import pytest
+
+from evaluation import cli
 from evaluation.cli import _build_parser, _resolve_scenario_ids
+from evaluation.models import EvalReport
+from evaluation.report import build_report
+from evaluation.visualization import VisualizationDependencyError
 
 
 def test_cli_accepts_optional_scenario_selector() -> None:
@@ -20,6 +28,103 @@ def test_cli_accepts_optional_scenario_selector() -> None:
     assert args.scenario_ids == "fcc+fmsr_all"
 
 
+def test_cli_charts_are_opt_in() -> None:
+    base_args = [
+        "--trajectories",
+        "trajectories",
+        "--scenarios",
+        "scenarios",
+    ]
+
+    assert _build_parser().parse_args(base_args).charts is False
+    assert _build_parser().parse_args([*base_args, "--charts"]).charts is True
+
+
+@pytest.mark.parametrize("charts", [False, True])
+def test_cli_chart_generation_is_opt_in(
+    monkeypatch: pytest.MonkeyPatch, tmp_path: Path, charts: bool
+) -> None:
+    calls: list[Path] = []
+
+    class StubEvaluator:
+        def __init__(self, **kwargs: object) -> None:
+            pass
+
+        def evaluate(self, **kwargs: object) -> EvalReport:
+            return build_report([])
+
+    def write_reports_dir(report: EvalReport, path: Path) -> Path:
+        return path
+
+    def record_render(report: EvalReport, path: Path) -> tuple[Path, ...]:
+        calls.append(path)
+        return ()
+
+    def accept_scorer(name: str) -> None:
+        pass
+
+    def dependency_available() -> None:
+        pass
+
+    monkeypatch.setattr(cli, "Evaluator", StubEvaluator)
+    monkeypatch.setattr(cli, "_validate_scorer_default", accept_scorer)
+    monkeypatch.setattr(cli, "write_reports_dir", write_reports_dir)
+    monkeypatch.setattr(
+        "evaluation.visualization.require_visualization_dependency",
+        dependency_available,
+    )
+    monkeypatch.setattr(
+        "evaluation.visualization.render_leaderboards",
+        record_render,
+    )
+
+    args = [
+        "--trajectories",
+        "trajectories",
+        "--scenarios",
+        "scenarios",
+        "--reports-dir",
+        str(tmp_path),
+    ]
+    if charts:
+        args.append("--charts")
+
+    result = cli.main(args)
+
+    assert result == 0
+    assert calls == ([tmp_path / "charts"] if charts else [])
+
+
+def test_cli_fails_before_evaluation_when_chart_dependency_is_missing(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    def dependency_error() -> None:
+        raise VisualizationDependencyError("install charts")
+
+    monkeypatch.setattr(
+        "evaluation.visualization.require_visualization_dependency",
+        dependency_error,
+    )
+    monkeypatch.setattr(
+        cli,
+        "Evaluator",
+        lambda **kwargs: pytest.fail("evaluation should not run"),
+    )
+
+    with pytest.raises(SystemExit) as exc_info:
+        cli.main(
+            [
+                "--trajectories",
+                "trajectories",
+                "--scenarios",
+                "scenarios",
+                "--charts",
+            ]
+        )
+
+    assert exc_info.value.code == 2
+
+
 def test_resolve_scenario_ids_loads_all_yaml_categories() -> None:
     selected = _resolve_scenario_ids("fcc+fmsr_all")
 
diff --git a/src/evaluation/tests/test_visualization.py b/src/evaluation/tests/test_visualization.py
new file mode 100644
index 00000000..12a9aeb0
--- /dev/null
+++ b/src/evaluation/tests/test_visualization.py
@@ -0,0 +1,257 @@
+"""Tests for report-derived leaderboard aggregation and rendering."""
+
+from __future__ import annotations
+
+from pathlib import Path
+from types import ModuleType
+
+import pytest
+
+from evaluation.models import (
+    EvalReport,
+    OpsMetrics,
+    ScenarioResult,
+    ScorerResult,
+)
+from evaluation.report import build_report
+from evaluation.visualization import (
+    CARBON_CATEGORICAL_COLORS,
+    CriterionRate,
+    VisualizationDependencyError,
+    criterion_rates,
+    model_styles,
+    render_leaderboards,
+    require_visualization_dependency,
+)
+
+
+def _result(
+    *,
+    runner: str = "meta-agent",
+    model: str = "model-a",
+    scorer: str = "llm_judge",
+    **details: object,
+) -> ScenarioResult:
+    return ScenarioResult(
+        scenario_id="scenario",
+        runner=runner,
+        model=model,
+        question="q",
+        answer="a",
+        score=ScorerResult(
+            scorer=scorer,
+            passed=False,
+            details=dict(details),
+        ),
+        ops=OpsMetrics(),
+    )
+
+
+def _rates_by_model(
+    report: EvalReport, runner: str
+) -> dict[str, dict[str, CriterionRate]]:
+    return {
+        model.model: {rate.criterion: rate for rate in model.rates}
+        for model in criterion_rates(report, runner).models
+    }
+
+
+def test_criterion_rates_use_only_applicable_boolean_results() -> None:
+    report = build_report(
+        [
+            _result(task_completion=True),
+            _result(task_completion=False),
+            _result(task_completion=1),
+            _result(data_retrieval_accuracy="true"),
+            _result(),
+        ]
+    )
+
+    rates = _rates_by_model(report, "meta-agent")["model-a"]
+
+    assert rates["task_completion"].successes == 1
+    assert rates["task_completion"].applicable == 2
+    assert rates["task_completion"].percentage == 50.0
+    assert rates["data_retrieval_accuracy"].applicable == 0
+    assert rates["data_retrieval_accuracy"].percentage is None
+
+
+def test_models_and_runners_remain_separate() -> None:
+    report = build_report(
+        [
+            _result(model="model-a", task_completion=True),
+            _result(model="model-b", task_completion=False),
+            _result(runner="plan-execute", model="model-a", task_completion=False),
+        ]
+    )
+
+    meta_rates = _rates_by_model(report, "meta-agent")
+    plan_rates = _rates_by_model(report, "plan-execute")
+
+    assert meta_rates["model-a"]["task_completion"].percentage == 100.0
+    assert meta_rates["model-b"]["task_completion"].percentage == 0.0
+    assert plan_rates["model-a"]["task_completion"].percentage == 0.0
+
+
+def test_non_llm_judge_results_do_not_contaminate_rates() -> None:
+    report = build_report(
+        [
+            _result(task_completion=True),
+            _result(scorer="static_json", task_completion=False),
+        ]
+    )
+
+    rate = _rates_by_model(report, "meta-agent")["model-a"]["task_completion"]
+
+    assert (rate.successes, rate.applicable, rate.percentage) == (1, 1, 100.0)
+
+
+def test_hallucinations_are_exposed_as_a_positive_outcome() -> None:
+    report = build_report(
+        [
+            _result(hallucinations=False),
+            _result(hallucinations=True),
+            _result(hallucinations=False),
+        ]
+    )
+
+    rate = criterion_rates(report, "meta-agent", ("hallucinations",)).models[0].rates[0]
+
+    assert rate.successes == 2
+    assert rate.applicable == 3
+    assert rate.percentage == pytest.approx(66.6667)
+
+
+def test_model_styles_are_stable_global_and_follow_carbon_order() -> None:
+    results = [
+        _result(runner="runner-b", model="z-model", task_completion=True),
+        _result(runner="runner-a", model="a-model", task_completion=True),
+    ]
+
+    forward = model_styles(build_report(results))
+    reversed_order = model_styles(build_report(list(reversed(results))))
+
+    assert forward == reversed_order
+    assert forward["a-model"].color == CARBON_CATEGORICAL_COLORS[0]
+    assert forward["z-model"].color == CARBON_CATEGORICAL_COLORS[1]
+    assert all(style.hatch for style in forward.values())
+
+
+def test_styles_remain_unique_beyond_base_palette() -> None:
+    report = build_report(
+        [
+            _result(model=f"model-{index:02}", task_completion=True)
+            for index in range(len(CARBON_CATEGORICAL_COLORS) + 1)
+        ]
+    )
+
+    styles = model_styles(report)
+
+    assert len({(style.color, style.hatch) for style in styles.values()}) == len(styles)
+
+
+def test_renderer_creates_svg_and_png_for_each_runner(tmp_path: Path) -> None:
+    matplotlib = pytest.importorskip("matplotlib")
+    original_backend = matplotlib.get_backend()
+    report = build_report(
+        [
+            _result(runner=runner, model=model, task_completion=True)
+            for runner in ("meta-agent", "plan-execute")
+            for model in ("model-a", "model-b")
+        ]
+    )
+
+    paths = render_leaderboards(report, tmp_path / "charts")
+    repeated_paths = render_leaderboards(report, tmp_path / "charts-repeated")
+
+    assert [path.name for path in paths] == [
+        "leaderboard-meta-agent.svg",
+        "leaderboard-meta-agent.png",
+        "leaderboard-plan-execute.svg",
+        "leaderboard-plan-execute.png",
+    ]
+    assert all(path.stat().st_size > 0 for path in paths)
+    assert [path.read_bytes() for path in paths] == [
+        path.read_bytes() for path in repeated_paths
+    ]
+    svg = paths[0].read_text(encoding="utf-8")
+    assert 'role="img"' in svg
+    assert '<desc id="chart-desc">' in svg
+    assert "model-a: Task completion 100.0% (1/1)" in svg
+    assert "<text" in svg
+    assert matplotlib.get_backend() == original_backend
+
+
+def test_renderer_uses_collision_safe_filenames(tmp_path: Path) -> None:
+    pytest.importorskip("matplotlib")
+    report = build_report(
+        [
+            _result(runner=runner, task_completion=True)
+            for runner in ("alpha.one", "alpha.two", "Alpha", "alpha")
+        ]
+    )
+
+    paths = render_leaderboards(report, tmp_path / "charts")
+
+    assert len(paths) == 8
+    assert len({path.name.casefold() for path in paths}) == 8
+    assert all(path.parent == tmp_path / "charts" for path in paths)
+
+
+def test_renderer_removes_only_stale_managed_outputs(tmp_path: Path) -> None:
+    pytest.importorskip("matplotlib")
+    output_dir = tmp_path / "charts"
+    unrelated = output_dir / "notes.txt"
+    output_dir.mkdir()
+    unrelated.write_text("keep", encoding="utf-8")
+    render_leaderboards(
+        build_report(
+            [
+                _result(runner="runner-a", task_completion=True),
+                _result(runner="runner-b", task_completion=True),
+            ]
+        ),
+        output_dir,
+    )
+
+    render_leaderboards(
+        build_report([_result(runner="runner-a", task_completion=True)]),
+        output_dir,
+    )
+
+    assert not (output_dir / "leaderboard-runner-b.svg").exists()
+    assert not (output_dir / "leaderboard-runner-b.png").exists()
+    assert unrelated.read_text(encoding="utf-8") == "keep"
+
+    assert render_leaderboards(build_report([]), output_dir) == ()
+    assert not list(output_dir.glob("leaderboard-*.svg"))
+    assert not list(output_dir.glob("leaderboard-*.png"))
+    assert unrelated.exists()
+
+
+def test_hatches_choose_contrast_against_each_fill() -> None:
+    styles = model_styles(
+        build_report(
+            [
+                _result(model="a-model", task_completion=True),
+                _result(model="b-model", task_completion=True),
+            ]
+        )
+    )
+
+    assert styles["a-model"].hatch_color == "#ffffff"
+    assert styles["b-model"].hatch_color == "#161616"
+
+
+def test_missing_visualization_dependency_has_actionable_error(
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    def missing_matplotlib(name: str) -> ModuleType:
+        raise ModuleNotFoundError("No module named 'matplotlib'", name="matplotlib")
+
+    monkeypatch.setattr("evaluation.visualization.import_module", missing_matplotlib)
+
+    with pytest.raises(
+        VisualizationDependencyError, match="uv sync --group visualization"
+    ):
+        require_visualization_dependency()
diff --git a/src/evaluation/visualization.py b/src/evaluation/visualization.py
new file mode 100644
index 00000000..ca2bb81f
--- /dev/null
+++ b/src/evaluation/visualization.py
@@ -0,0 +1,510 @@
+"""Accessible leaderboard charts derived from an :class:`EvalReport`."""
+
+from __future__ import annotations
+
+import hashlib
+import json
+import re
+import textwrap
+from collections.abc import Sequence
+from dataclasses import dataclass
+from html import escape
+from importlib import import_module
+from pathlib import Path
+from tempfile import TemporaryDirectory
+from types import ModuleType
+from typing import Any
+
+from .models import EvalReport
+
+DEFAULT_CRITERIA = (
+    "task_completion",
+    "data_retrieval_accuracy",
+    "generalized_result_verification",
+)
+
+CRITERION_LABELS = {
+    "task_completion": "Task completion",
+    "data_retrieval_accuracy": "Data retrieval accuracy",
+    "generalized_result_verification": "Result verification",
+    "hallucinations": "Hallucination-free",
+}
+
+# Official Carbon categorical palette for light themes, in documented order:
+# https://carbondesignsystem.com/data-visualization/color-palettes/
+CARBON_CATEGORICAL_COLORS = (
+    "#6929c4",
+    "#1192e8",
+    "#005d5d",
+    "#9f1853",
+    "#fa4d56",
+    "#570408",
+    "#198038",
+    "#002d9c",
+    "#ee538b",
+    "#b28600",
+    "#009d9a",
+    "#012749",
+    "#8a3800",
+    "#a56eff",
+)
+
+# Pattern is redundant with color. A prime-sized cycle delays repeated pairs
+# when the report contains more models than Carbon's base palette.
+_HATCHES = (
+    "///",
+    "\\\\\\",
+    "|||",
+    "---",
+    "+++",
+    "xxx",
+    "...",
+    "ooo",
+    "OOO",
+    "***",
+    "/\\",
+    "|+",
+    "x.",
+)
+_INVERTED_CRITERIA = frozenset({"hallucinations"})
+_SAFE_NAME = re.compile(r"[^A-Za-z0-9._-]+")
+_MANAGED_NAME = re.compile(r"leaderboard-[A-Za-z0-9._-]+\.(?:svg|png)")
+_MANIFEST_FILENAME = ".leaderboard-manifest.json"
+
+
+class VisualizationDependencyError(RuntimeError):
+    """Raised when chart generation is requested without Matplotlib."""
+
+
+@dataclass(frozen=True)
+class CriterionRate:
+    """Exact Boolean counts and their derived percentage for one criterion."""
+
+    criterion: str
+    successes: int
+    applicable: int
+
+    @property
+    def percentage(self) -> float | None:
+        if self.applicable == 0:
+            return None
+        return self.successes / self.applicable * 100.0
+
+
+@dataclass(frozen=True)
+class ModelRates:
+    """Criterion rates for one model within one runner."""
+
+    model: str
+    rates: tuple[CriterionRate, ...]
+
+
+@dataclass(frozen=True)
+class LeaderboardData:
+    """Plot-ready, scorer-specific data for one orchestration method."""
+
+    runner: str
+    criteria: tuple[str, ...]
+    models: tuple[ModelRates, ...]
+
+
+@dataclass(frozen=True)
+class ModelStyle:
+    """A report-global categorical color and redundant hatch pattern."""
+
+    color: str
+    hatch: str
+    hatch_color: str
+
+
+def criterion_rates(
+    report: EvalReport,
+    runner: str,
+    criteria: Sequence[str] = DEFAULT_CRITERIA,
+) -> LeaderboardData:
+    """Aggregate strict Boolean LLM-judge details for one runner.
+
+    Missing and non-Boolean criterion values are excluded from the denominator.
+    The raw ``hallucinations`` criterion is inverted into a positive outcome.
+    """
+    criterion_names = tuple(criteria)
+    if len(set(criterion_names)) != len(criterion_names):
+        raise ValueError("leaderboard criteria must be unique")
+
+    matching = [
+        result
+        for result in report.results
+        if result.runner == runner and result.score.scorer == "llm_judge"
+    ]
+    model_names = sorted({result.model for result in matching})
+    grouped: dict[str, dict[str, list[int]]] = {
+        model: {criterion: [0, 0] for criterion in criterion_names}
+        for model in model_names
+    }
+
+    for result in matching:
+        for criterion in criterion_names:
+            value = result.score.details.get(criterion)
+            if not isinstance(value, bool):
+                continue
+            counts = grouped[result.model][criterion]
+            counts[1] += 1
+            positive = not value if criterion in _INVERTED_CRITERIA else value
+            counts[0] += int(positive)
+
+    models = tuple(
+        ModelRates(
+            model=model,
+            rates=tuple(
+                CriterionRate(
+                    criterion=criterion,
+                    successes=grouped[model][criterion][0],
+                    applicable=grouped[model][criterion][1],
+                )
+                for criterion in criterion_names
+            ),
+        )
+        for model in model_names
+    )
+    return LeaderboardData(runner=runner, criteria=criterion_names, models=models)
+
+
+def model_styles(report: EvalReport) -> dict[str, ModelStyle]:
+    """Assign deterministic styles globally across all LLM-judge results."""
+    models = sorted(
+        {
+            result.model
+            for result in report.results
+            if result.score.scorer == "llm_judge"
+        }
+    )
+    styles: dict[str, ModelStyle] = {}
+    for index, model in enumerate(models):
+        color = CARBON_CATEGORICAL_COLORS[index % len(CARBON_CATEGORICAL_COLORS)]
+        styles[model] = ModelStyle(
+            color=color,
+            hatch=_HATCHES[index % len(_HATCHES)],
+            hatch_color=_hatch_color(color),
+        )
+    return styles
+
+
+def require_visualization_dependency() -> None:
+    """Fail early, before evaluation may make a paid judge-model call."""
+    _load_matplotlib()
+
+
+def render_leaderboards(
+    report: EvalReport,
+    output_dir: Path,
+    criteria: Sequence[str] = DEFAULT_CRITERIA,
+) -> tuple[Path, ...]:
+    """Render one SVG and PNG leaderboard per applicable runner."""
+    styles = model_styles(report)
+    runners = sorted(
+        {
+            result.runner
+            for result in report.results
+            if result.score.scorer == "llm_judge"
+        }
+    )
+    leaderboards = [criterion_rates(report, runner, criteria) for runner in runners]
+    leaderboards = [data for data in leaderboards if _has_applicable_data(data)]
+    if not leaderboards:
+        _publish_outputs(Path(output_dir), None, ())
+        return ()
+
+    output_dir = Path(output_dir)
+    output_dir.parent.mkdir(parents=True, exist_ok=True)
+    matplotlib, figures, backend_agg, patches = _load_matplotlib()
+    generated_names: list[str] = []
+    with TemporaryDirectory(
+        dir=output_dir.parent, prefix=".leaderboard-render-"
+    ) as temporary:
+        staged_dir = Path(temporary)
+        with matplotlib.rc_context(_MATPLOTLIB_STYLE):
+            for data in leaderboards:
+                figure = _render_figure(figures, backend_agg, patches, data, styles)
+                try:
+                    title = f"AssetOpsBench {data.runner} leaderboard"
+                    description = _chart_description(data)
+                    base_name = f"leaderboard-{_filesystem_safe(data.runner)}"
+                    for suffix in (".svg", ".png"):
+                        name = f"{base_name}{suffix}"
+                        path = staged_dir / name
+                        figure.savefig(
+                            path,
+                            dpi=180,
+                            bbox_inches="tight",
+                            facecolor="white",
+                            metadata={
+                                "Title": title,
+                                "Description": description,
+                                "Creator": "AssetOpsBench",
+                                "Date": report.generated_at,
+                            },
+                        )
+                        if suffix == ".svg":
+                            _enhance_svg(path, description)
+                        generated_names.append(name)
+                finally:
+                    figure.clear()
+        return _publish_outputs(output_dir, staged_dir, tuple(generated_names))
+
+
+def _load_matplotlib() -> tuple[ModuleType, ModuleType, ModuleType, ModuleType]:
+    try:
+        matplotlib = import_module("matplotlib")
+        figures = import_module("matplotlib.figure")
+        backend_agg = import_module("matplotlib.backends.backend_agg")
+        patches = import_module("matplotlib.patches")
+    except ModuleNotFoundError as exc:
+        raise VisualizationDependencyError(
+            "leaderboard charts require the visualization dependency group; "
+            "install it with `uv sync --group visualization`"
+        ) from exc
+    return matplotlib, figures, backend_agg, patches
+
+
+def _render_figure(
+    figures: ModuleType,
+    backend_agg: ModuleType,
+    patches: ModuleType,
+    data: LeaderboardData,
+    styles: dict[str, ModelStyle],
+) -> Any:
+    model_count = len(data.models)
+    width = max(10.0, min(16.0, 7.5 + model_count * 0.65))
+    legend_labels = [textwrap.fill(model.model, width=42) for model in data.models]
+    legend_lines = sum(label.count("\n") + 1 for label in legend_labels)
+    height = min(12.0, max(6.5, 5.6 + legend_lines * 0.22))
+    figure = figures.Figure(figsize=(width, height), layout="constrained")
+    backend_agg.FigureCanvasAgg(figure)
+    axis = figure.subplots()
+    bar_width = min(0.78 / max(model_count, 1), 0.22)
+
+    legend_handles = []
+    for model_index, (model_data, legend_label) in enumerate(
+        zip(data.models, legend_labels, strict=True)
+    ):
+        style = styles[model_data.model]
+        offset = (model_index - (model_count - 1) / 2) * bar_width
+        positions = [index + offset for index in range(len(data.criteria))]
+        heights = [rate.percentage or 0.0 for rate in model_data.rates]
+        bars = axis.bar(
+            positions,
+            heights,
+            width=bar_width * 0.94,
+            color=style.color,
+            edgecolor="#161616",
+            linewidth=0.8,
+            zorder=3,
+        )
+        hatch_bars = axis.bar(
+            positions,
+            heights,
+            width=bar_width * 0.94,
+            color="none",
+            edgecolor=style.hatch_color,
+            linewidth=0,
+            hatch=style.hatch,
+            zorder=4,
+        )
+        for bar, rate in zip(bars, model_data.rates, strict=True):
+            percentage = rate.percentage
+            if percentage is None:
+                bar.set_visible(False)
+                axis.text(
+                    bar.get_x() + bar.get_width() / 2,
+                    1.5,
+                    "N/A",
+                    ha="center",
+                    va="bottom",
+                    rotation=90,
+                    fontsize=7,
+                    color="#525252",
+                )
+                continue
+            axis.text(
+                bar.get_x() + bar.get_width() / 2,
+                percentage + 1.5,
+                f"{percentage:.1f}%",
+                ha="center",
+                va="bottom",
+                rotation=90,
+                fontsize=7.5,
+                color="#161616",
+            )
+        for hatch_bar, rate in zip(hatch_bars, model_data.rates, strict=True):
+            if rate.percentage is None:
+                hatch_bar.set_visible(False)
+        legend_handles.append(
+            patches.Patch(
+                facecolor=style.color,
+                edgecolor=style.hatch_color,
+                linewidth=0.8,
+                hatch=style.hatch,
+                label=legend_label,
+            )
+        )
+
+    axis.set_title(textwrap.fill(f"{data.runner} leaderboard", 72), loc="left", pad=18)
+    axis.set_ylabel("Success rate (%)")
+    axis.set_xticks(
+        range(len(data.criteria)),
+        [textwrap.fill(_criterion_label(name), 22) for name in data.criteria],
+    )
+    axis.set_ylim(0, 112)
+    axis.set_yticks(range(0, 101, 20), [f"{value}%" for value in range(0, 101, 20)])
+    axis.axhline(0, color="#161616", linewidth=1.0)
+    axis.yaxis.grid(True, color="#e0e0e0", linewidth=0.8, zorder=0)
+    axis.spines[["top", "right", "left"]].set_visible(False)
+    axis.tick_params(axis="both", length=0)
+    figure.legend(
+        handles=legend_handles,
+        loc="outside lower center",
+        ncols=min(2, model_count),
+        frameon=False,
+        title="Models (color and pattern)",
+        fontsize=8.5,
+    )
+    return figure
+
+
+def _criterion_label(criterion: str) -> str:
+    return CRITERION_LABELS.get(criterion, criterion.replace("_", " ").capitalize())
+
+
+def _has_applicable_data(data: LeaderboardData) -> bool:
+    return any(rate.applicable for model in data.models for rate in model.rates)
+
+
+def _filesystem_safe(value: str) -> str:
+    cleaned = _SAFE_NAME.sub("-", value).strip("-._").lower() or "runner"
+    if cleaned == value and len(cleaned) <= 80:
+        return cleaned
+    digest = hashlib.sha256(value.encode("utf-8")).hexdigest()[:12]
+    return f"{cleaned[:67]}-{digest}"
+
+
+def _chart_description(data: LeaderboardData) -> str:
+    model_values = []
+    for model in data.models:
+        rates = "; ".join(
+            f"{_criterion_label(rate.criterion)} "
+            + (
+                f"{rate.percentage:.1f}% ({rate.successes}/{rate.applicable})"
+                if rate.percentage is not None
+                else "N/A"
+            )
+            for rate in model.rates
+        )
+        model_values.append(f"{model.model}: {rates}")
+    return (
+        f"Grouped bar chart for {data.runner}. Models are distinguished by color "
+        "and pattern. Values are LLM-judge success percentages; missing Boolean "
+        f"criteria are shown as N/A. Data: {' | '.join(model_values)}"
+    )
+
+
+def _enhance_svg(path: Path, description: str) -> None:
+    """Add screen-reader semantics not emitted by Matplotlib's SVG backend."""
+    svg = path.read_text(encoding="utf-8")
+    svg = svg.replace(
+        "<svg ",
+        '<svg role="img" aria-labelledby="chart-title chart-desc" ',
+        1,
+    )
+    svg = svg.replace("<title>", '<title id="chart-title">', 1)
+    svg = svg.replace(
+        "</title>",
+        f'</title>\n <desc id="chart-desc">{escape(description)}</desc>',
+        1,
+    )
+    path.write_text(svg, encoding="utf-8")
+
+
+def _publish_outputs(
+    output_dir: Path,
+    staged_dir: Path | None,
+    names: tuple[str, ...],
+) -> tuple[Path, ...]:
+    """Publish staged files atomically and remove only prior managed files."""
+    previous = _read_manifest(output_dir)
+    staged_manifest: Path | None = None
+    if names:
+        if staged_dir is None:
+            raise ValueError("staged output directory is required")
+        output_dir.mkdir(parents=True, exist_ok=True)
+        for name in names:
+            if not _MANAGED_NAME.fullmatch(name):
+                raise ValueError(f"unsafe generated chart name: {name!r}")
+            (staged_dir / name).replace(output_dir / name)
+        staged_manifest = staged_dir / _MANIFEST_FILENAME
+        staged_manifest.write_text(
+            json.dumps({"version": 1, "files": list(names)}, indent=2) + "\n",
+            encoding="utf-8",
+        )
+
+    for name in previous.difference(names):
+        (output_dir / name).unlink(missing_ok=True)
+
+    manifest = output_dir / _MANIFEST_FILENAME
+    if staged_manifest is not None:
+        staged_manifest.replace(manifest)
+    else:
+        manifest.unlink(missing_ok=True)
+    return tuple(output_dir / name for name in names)
+
+
+def _read_manifest(output_dir: Path) -> set[str]:
+    manifest = output_dir / _MANIFEST_FILENAME
+    try:
+        raw = json.loads(manifest.read_text(encoding="utf-8"))
+    except (FileNotFoundError, json.JSONDecodeError, OSError):
+        return set()
+    files = raw.get("files") if isinstance(raw, dict) else None
+    if not isinstance(files, list):
+        return set()
+    return {
+        name
+        for name in files
+        if isinstance(name, str)
+        and Path(name).name == name
+        and _MANAGED_NAME.fullmatch(name)
+    }
+
+
+def _hatch_color(color: str) -> str:
+    luminance = _relative_luminance(color)
+    dark_luminance = _relative_luminance("#161616")
+    dark_contrast = (luminance + 0.05) / (dark_luminance + 0.05)
+    light_contrast = 1.05 / (luminance + 0.05)
+    return "#ffffff" if light_contrast > dark_contrast else "#161616"
+
+
+def _relative_luminance(color: str) -> float:
+    channels = [int(color[index : index + 2], 16) / 255 for index in (1, 3, 5)]
+    linear = [
+        channel / 12.92 if channel <= 0.04045 else ((channel + 0.055) / 1.055) ** 2.4
+        for channel in channels
+    ]
+    return 0.2126 * linear[0] + 0.7152 * linear[1] + 0.0722 * linear[2]
+
+
+_MATPLOTLIB_STYLE = {
+    "axes.facecolor": "white",
+    "axes.labelcolor": "#161616",
+    "axes.titlecolor": "#161616",
+    "axes.titlesize": 15,
+    "axes.titleweight": "bold",
+    "figure.facecolor": "white",
+    "font.family": "sans-serif",
+    "font.size": 10,
+    "hatch.linewidth": 0.8,
+    "svg.hashsalt": "assetopsbench-leaderboards",
+    "svg.fonttype": "none",
+    "text.color": "#161616",
+    "xtick.color": "#161616",
+    "ytick.color": "#161616",
+}
diff --git a/uv.lock b/uv.lock
index 2d78826b..13856b4f 100644
--- a/uv.lock
+++ b/uv.lock
@@ -300,6 +300,9 @@ tsfm = [
     { name = "torch" },
     { name = "transformers" },
 ]
+visualization = [
+    { name = "matplotlib" },
+]
 
 [package.metadata]
 requires-dist = [
@@ -349,6 +352,7 @@ tsfm = [
     { name = "torch", specifier = ">=2.0" },
     { name = "transformers", specifier = ">=4.40" },
 ]
+visualization = [{ name = "matplotlib", specifier = ">=3.10" }]
 
 [[package]]
 name = "asttokens"
@@ -634,6 +638,72 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/60/97/891a0971e1e4a8c5d2b20bbe0e524dc04548d2307fee33cdeba148fd4fc7/comm-0.2.3-py3-none-any.whl", hash = "sha256:c615d91d75f7f04f095b30d1c1711babd43bdc6419c1be9886a85f2f4e489417", size = 7294, upload-time = "2025-07-25T14:02:02.896Z" },
 ]
 
+[[package]]
+name = "contourpy"
+version = "1.3.3"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy" },
+]
+sdist = { url = "https://files.pythonhosted.org/packages/58/01/1253e6698a07380cd31a736d248a3f2a50a7c88779a1813da27503cadc2a/contourpy-1.3.3.tar.gz", hash = "sha256:083e12155b210502d0bca491432bb04d56dc3432f95a979b429f2848c3dbe880", size = 13466174, upload-time = "2025-07-26T12:03:12.549Z" }
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