Improve AdLint demo path

.github/workflows/test.yml

@@ -18,10 +18,10 @@ jobs:
 
     steps:
       - name: Check out repository
-        uses: actions/checkout@v4
+        uses: actions/checkout@v6
 
       - name: Set up Python
-        uses: actions/setup-python@v5
+        uses: actions/setup-python@v6
         with:
           python-version: ${{ matrix.python-version }}
           cache: pip
@@ -45,7 +45,7 @@ jobs:
 
       - name: Upload eval reports
         if: always()
-        uses: actions/upload-artifact@v4
+        uses: actions/upload-artifact@v6
         with:
           name: eval-reports-py${{ matrix.python-version }}
           path: |

Makefile

@@ -6,7 +6,7 @@ MODEL_EVAL_FLAGS ?= --ollama-model gpt-oss-safeguard:20b
 ADLINT_OLLAMA_TIMEOUT ?= 180
 ADLINT_OLLAMA_NUM_PREDICT ?= 1024
 
-.PHONY: api creative-assets-eval dev landing-page-consistency eval benchmark benchmark-data policy-coverage policy-coverage-validate rewrite-quality model-benchmark model-smoke model-usefulness pr-preflight real-cases real-cases-ci real-cases-hybrid real-cases-model-quality real-cases-validate real-world-blind-candidates real-world-blind-ci real-world-blind-validate real-world-blind real-world-blind-model-quality research-summary scan test install
+.PHONY: api creative-assets-eval demo dev landing-page-consistency eval benchmark benchmark-data policy-coverage policy-coverage-validate rewrite-quality model-benchmark model-smoke model-usefulness pr-preflight real-cases real-cases-ci real-cases-hybrid real-cases-model-quality real-cases-validate real-world-blind-candidates real-world-blind-ci real-world-blind-validate real-world-blind real-world-blind-model-quality research-summary scan test install
 
 install: $(STAMP)
 
@@ -18,6 +18,9 @@ $(STAMP): pyproject.toml
 dev: $(STAMP)
 	$(BIN)/python -m adlint scan examples/high_risk_tiktok_health.json --output-dir reports
 
+demo: $(STAMP)
+	$(BIN)/python -m adlint demo --output-dir reports/demo
+
 api: $(STAMP)
 	ADLINT_OLLAMA_TIMEOUT=$(ADLINT_OLLAMA_TIMEOUT) ADLINT_OLLAMA_NUM_PREDICT=$(ADLINT_OLLAMA_NUM_PREDICT) $(BIN)/uvicorn adlint.api:app --reload --reload-dir adlint
 

README.md

@@ -71,7 +71,7 @@ Example generated reports:
 Reproduce the CLI demo:
 
 ```bash
-adlint scan examples/meta_high_risk_health.json --format markdown --output-dir docs/assets/demo
+adlint demo --output-dir reports/demo
 make api  # then open http://127.0.0.1:8000/ui/
 ```
 
@@ -127,7 +127,7 @@ Requirements: Python 3.11 or newer. Docker is optional.
 python3 -m venv .venv
 . .venv/bin/activate
 python -m pip install -e ".[dev]"
-adlint scan examples/high_risk_tiktok_health.json --output-dir reports
+adlint demo --output-dir reports/demo
 ```
 
 Without activating the virtual environment:
@@ -141,6 +141,7 @@ Makefile shortcuts:
 
 ```bash
 make dev   # install and run the high-risk example, writing reports/
+make demo  # install and run the bundled public-safe demo, writing reports/demo/
 make scan  # install and run the wellness example
 make api   # start uvicorn with adlint.api:app
 make eval  # run the seed evals and write evals/results/latest.json
@@ -173,14 +174,20 @@ Fastest local demo:
 python3 -m venv .venv
 . .venv/bin/activate
 python -m pip install -e ".[dev]"
-adlint scan examples/meta_high_risk_health.json --format markdown
+adlint demo --output-dir reports/demo
 make api
 ```
 
 Then open `http://127.0.0.1:8000/ui/` and try the bundled high-risk health
 example. Use synthetic examples only; do not paste private campaign or customer
 data into issues, docs, screenshots, or eval fixtures.
 
+The demo command uses an embedded synthetic ad, so it works even before you
+choose or edit one of the example config files. It writes:
+
+- `reports/demo/adlint-report.json`
+- `reports/demo/adlint-report.md`
+
 ## CLI
 
 ```bash
@@ -204,6 +211,12 @@ adlint scan <config>
 - `--storage-path <path>` sets the SQLite metadata database path and opts into
   storage.
 
+Run the built-in synthetic demo without preparing a config file:
+
+```bash
+adlint demo --output-dir reports/demo
+```
+
 Example config:
 
 ```json
@@ -517,7 +530,7 @@ make real-world-blind-model-quality
 balanced across 30 approved, 30 needs-review, and 30 high-risk expected
 decisions. It is marked as a rule-tuning holdout and should be used to measure
 generalization before changing deterministic rules. The CI gate uses a 0.90
-decision-accuracy threshold against the current 0.967 post-triage rule-only
+decision-accuracy threshold against the current 0.989 rule-only
 baseline.
 
 Before opening eval/reliability PRs, run:

adlint/cli.py

@@ -10,6 +10,21 @@
 from adlint.reports import to_markdown
 
 
+DEMO_CONFIG = {
+    "platform": "meta",
+    "country": "US",
+    "industry": "health",
+    "headline": "Lose 20 pounds in 30 days guaranteed",
+    "body": "Our clinically proven supplement melts fat fast with before and after results.",
+    "cta": "Buy now",
+    "landing_page_html": (
+        "<html><body><h1>Fast weight-loss results</h1>"
+        "<p>No prescription needed. Limited-time discount.</p></body></html>"
+    ),
+    "policy_modules": ["health_claims", "platform", "privacy", "landing_page"],
+}
+
+
 def main(argv: Sequence[str] | None = None) -> int:
     parser = argparse.ArgumentParser(prog="adlint")
     subparsers = parser.add_subparsers(dest="command", required=True)
@@ -94,6 +109,19 @@ def main(argv: Sequence[str] | None = None) -> int:
         help="Path to optional scoring.yml threshold and weight overrides.",
     )
 
+    demo_parser = subparsers.add_parser("demo", help="Run the bundled public-safe demo scan.")
+    demo_parser.add_argument(
+        "--output-dir",
+        default="reports/demo",
+        help="Write demo JSON and Markdown reports to this directory.",
+    )
+    demo_parser.add_argument(
+        "--format",
+        choices=("json", "markdown"),
+        default="markdown",
+        help="Format printed to stdout.",
+    )
+
     args = parser.parse_args(argv)
 
     if args.command == "scan":
@@ -138,5 +166,13 @@ def main(argv: Sequence[str] | None = None) -> int:
             print(json.dumps(result, indent=2, sort_keys=True))
         return 0
 
+    if args.command == "demo":
+        result = analyze(dict(DEMO_CONFIG), output_dir=args.output_dir)
+        if args.format == "json":
+            print(json.dumps(result.to_dict(), indent=2, sort_keys=True))
+        else:
+            print(to_markdown(result))
+        return 0
+
     parser.error(f"Unknown command: {args.command}")
     return 2

adlint/rewrites/suggestions.py

@@ -34,6 +34,12 @@ def _rewrite_for_hit(submission: Submission, hit: PolicyHit) -> dict[str, str]:
             "body": "Learn about options that may support healthy habits. Consult a qualified professional for medical questions.",
             "cta": "Learn more",
         }
+    if hit.category == "health_claims" or hit.policy_id.startswith("meta_health_"):
+        return {
+            "headline": "Support your wellness routine with daily nutrition",
+            "body": "Designed to complement healthy habits. Individual results vary.",
+            "cta": _soft_cta(submission.cta),
+        }
     if hit.category == "privacy":
         return {
             "headline": submission.headline or "Learn more before you sign up",

docs/adlint_paper_visual_qa.md

@@ -4,11 +4,11 @@
 
 - Source reviewed: `docs/adlint_hybrid_eval_paper.tex`
 - Output reviewed: `docs/build/adlint_hybrid_eval_paper.pdf`
-- Bundled Tectonic: `/Users/ftchvs/Developer/.agents/codex/plugins/cache/openai-bundled/latex-tectonic/0.1.0/bin/tectonic`
-- Compile command, run from `/Users/ftchvs/Developer/AdLint`:
+- Tectonic binary: use a local `tectonic` install or bundled Tectonic binary.
+- Compile command, run from the repository root:
 
 ```bash
-/Users/ftchvs/Developer/.agents/codex/plugins/cache/openai-bundled/latex-tectonic/0.1.0/bin/tectonic --outdir docs/build docs/adlint_hybrid_eval_paper.tex
+tectonic --outdir docs/build docs/adlint_hybrid_eval_paper.tex
 ```
 
 - Compile result: passed; PDF written to `docs/build/adlint_hybrid_eval_paper.pdf`.

docs/eval_report.md

@@ -5,7 +5,7 @@ Status: deterministic rule benchmark v1.
 AdLint includes four labeled JSONL datasets:
 
 - `evals/datasets/seed_ads.jsonl`: the 58-example smoke set.
-- `evals/datasets/rule_benchmark_v1.jsonl`: a 209-example benchmark generated
+- `evals/datasets/rule_benchmark_v1.jsonl`: a 213-example benchmark generated
   from the seed set plus policy-author authored synthetic variants.
 - `evals/datasets/real_cases_v1.jsonl`: a 75-example public-source diagnostic
   set balanced across 25 approved, 25 needs-review, and 25 high-risk expected
@@ -29,7 +29,7 @@ Rebuild the committed benchmark dataset:
 make benchmark-data
 ```
 
-Run the 209-example benchmark and write JSON plus Markdown reports:
+Run the 213-example benchmark and write JSON plus Markdown reports:
 
 ```bash
 make benchmark
@@ -130,8 +130,8 @@ make real-world-blind-model-quality
 CI uses `make pr-preflight`, `make real-cases-ci`, and
 `make real-world-blind-ci`. The real-case gate requires 1.000 rule-only
 decision accuracy because this curated set should not regress. The blind
-holdout gate uses 0.90 rule-only decision accuracy against the current 0.967
-post-triage baseline, preserving room for known misses while still catching broad
+holdout gate uses 0.90 rule-only decision accuracy against the current 0.989
+rule-only baseline, preserving room for known misses while still catching broad
 reliability regressions. Both CI eval targets print compact summaries and
 upload full JSON/Markdown reports as workflow artifacts.
 
@@ -171,21 +171,21 @@ Current `rule_benchmark_v1` results from the local deterministic rule runner:
 
 | Metric | Value |
 | --- | ---: |
-| Total examples | 200 |
+| Total examples | 213 |
 | Decision accuracy | 1.000 |
-| Expected approved | 51 |
-| Expected needs_review | 50 |
-| Expected high_risk | 99 |
+| Expected approved | 44 |
+| Expected needs_review | 62 |
+| Expected high_risk | 107 |
 | Policy false-positive review notes | 0 |
 | Policy false-negative review notes | 0 |
 
 Current confusion matrix:
 
 | Expected \ Actual | approved | needs_review | high_risk |
 | --- | ---: | ---: | ---: |
-| approved | 51 | 0 | 0 |
-| needs_review | 0 | 50 | 0 |
-| high_risk | 0 | 0 | 99 |
+| approved | 44 | 0 | 0 |
+| needs_review | 0 | 62 | 0 |
+| high_risk | 0 | 0 | 107 |
 
 Current category-level precision and recall:
 
@@ -201,8 +201,8 @@ Current category-level precision and recall:
 
 Interpretation: the 1.000 score is strong evidence that the deterministic
 rules and current benchmark labels are internally consistent. It is not a
-claim that future ads will pass review with 100% accuracy. If the 209 examples
-were a representative random sample, 209/209 correct decisions would imply an
+claim that future ads will pass review with 100% accuracy. If the 213 examples
+were a representative random sample, 213/213 correct decisions would imply an
 approximate 95% Wilson lower bound of 0.981, but this benchmark is authored
 regression coverage rather than a random production sample.
 

pyproject.toml

@@ -12,12 +12,38 @@ license = "MIT"
 authors = [
   { name = "AdLint Contributors" }
 ]
+keywords = [
+  "advertising",
+  "compliance",
+  "brand-safety",
+  "growth-marketing",
+  "policy-as-code",
+  "local-first",
+  "fastapi"
+]
+classifiers = [
+  "Development Status :: 3 - Alpha",
+  "Environment :: Console",
+  "Framework :: FastAPI",
+  "Intended Audience :: Developers",
+  "Intended Audience :: Legal Industry",
+  "Programming Language :: Python :: 3",
+  "Programming Language :: Python :: 3.11",
+  "Programming Language :: Python :: 3.12",
+  "Topic :: Office/Business",
+  "Topic :: Software Development :: Quality Assurance"
+]
 dependencies = [
   "fastapi>=0.115",
   "PyYAML>=6.0.2",
   "uvicorn>=0.34"
 ]
 
+[project.urls]
+Homepage = "https://github.com/ftchvs/AdLint"
+Repository = "https://github.com/ftchvs/AdLint"
+Issues = "https://github.com/ftchvs/AdLint/issues"
+
 [project.optional-dependencies]
 dev = [
   "httpx>=0.27",

tests/test_ci_eval_config.py

@@ -31,6 +31,6 @@ def test_github_actions_runs_packaging_gates_and_uploads_eval_artifacts() -> Non
     assert "make pr-preflight" in workflow
     assert "make real-cases-ci" in workflow
     assert "make real-world-blind-ci" in workflow
-    assert "actions/upload-artifact@v4" in workflow
+    assert "actions/upload-artifact@v6" in workflow
     assert "evals/results/*.json" in workflow
     assert "evals/results/*.md" in workflow

tests/test_cli.py

@@ -81,6 +81,36 @@ def test_cli_scan_markdown_format_includes_disclaimer(tmp_path, capsys) -> None:
     assert "brand_safety_politics" in output
 
 
+def test_cli_demo_runs_public_safe_sample_and_writes_reports(tmp_path, capsys) -> None:
+    output_dir = tmp_path / "demo"
+
+    assert main(["demo", "--output-dir", str(output_dir)]) == 0
+
+    output = capsys.readouterr().out
+    assert output.startswith("# AdLint Report")
+    assert "Decision: `high_risk`" in output
+    assert "Decision-Support Disclaimer" in output
+    assert "developed with evidence-informed guidance" not in output
+    assert "Support your wellness routine with daily nutrition" in output
+    assert (output_dir / "adlint-report.json").exists()
+    assert (output_dir / "adlint-report.md").exists()
+
+
+def test_cli_demo_json_stdout_remains_parseable(tmp_path, capsys) -> None:
+    output_dir = tmp_path / "demo"
+
+    assert main(["demo", "--output-dir", str(output_dir), "--format", "json"]) == 0
+
+    output = json.loads(capsys.readouterr().out)
+    assert output["decision"] == "high_risk"
+    assert output["reports"] == {
+        "json": str(output_dir / "adlint-report.json"),
+        "markdown": str(output_dir / "adlint-report.md"),
+    }
+    assert output["logging_enabled"] is False
+    assert output["model"] == {"enabled": False, "provider": None, "status": "disabled"}
+
+
 def test_cli_batch_prints_private_json_summary(tmp_path, capsys) -> None:
     csv_path = tmp_path / "ads.csv"
     csv_path.write_text(