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diff --git a/manifest.json b/manifest.json
new file mode 100644
index 0000000000000000000000000000000000000000..2587aede19688062b33c0dea6f0e948b17dd07f2
--- /dev/null
+++ b/manifest.json
@@ -0,0 +1,207 @@
+{
+  "schema_version": "skillcenter-huggingface-release/v3",
+  "packager_version": "0.1.0",
+  "primary_key": "entry_cid",
+  "dataset_id": "endomorphosis/ipfs_argentina_laws",
+  "dataset_repo_id": "justicedao/ipfs_argentina_laws-ir",
+  "dataset_revision": "0361102728979d41f9e2953f8b03fcc4243d3039",
+  "country": {
+    "slug": "argentina",
+    "repo": "endomorphosis/ipfs_argentina_laws",
+    "name": "Argentina",
+    "indexable": true
+  },
+  "disclaimer": "Research snapshot. Not legal advice. The official gazette / authentic source prevails.",
+  "bm25": {
+    "k1": 1.2,
+    "b": 0.75,
+    "title_weight": 5.0,
+    "body_weight": 1.0,
+    "average_document_length": 326.36434898797444,
+    "tokenizer": "fts5-unicode61-remove-diacritics-2-python/v1",
+    "max_query_terms": 64,
+    "posting_rows_per_record": 4096,
+    "terms_per_shard": 4096
+  },
+  "counts": {
+    "bm25_document_chunks": 25,
+    "bm25_documents": 101035,
+    "bm25_keyword_shards": 22,
+    "bm25_posting_rows": 86888,
+    "bm25_postings": 9327357,
+    "bm25_terms": 86072,
+    "corpus_chunks": 25,
+    "corpus_rows": 101035,
+    "graph_edge_chunks": 251,
+    "graph_edges": 1025923,
+    "graph_incoming_adjacency_edges": 1025923,
+    "graph_incoming_adjacency_rows": 101348,
+    "graph_incoming_adjacency_shards": 25,
+    "graph_node_chunks": 27,
+    "graph_nodes": 109681,
+    "graph_outgoing_adjacency_edges": 1025923,
+    "graph_outgoing_adjacency_rows": 101035,
+    "graph_outgoing_adjacency_shards": 25,
+    "vector_chunks": 25,
+    "vector_rows": 101035,
+    "n_laws": 8643,
+    "n_articles": 92392
+  },
+  "parquet": {
+    "compression": "zstd",
+    "compression_level": 6,
+    "max_rows_per_file": 4096,
+    "row_group_size": 4096
+  },
+  "graph": {
+    "adjacency_pointers_per_row": 4096,
+    "adjacency_pointers_per_shard": 8192,
+    "directions": [
+      "incoming",
+      "outgoing"
+    ],
+    "max_remote_walk_depth": 8,
+    "ordering": "score_desc_nulls_last",
+    "edge_types": [
+      "HAS_JURISDICTION",
+      "HAS_LAW_ID",
+      "HAS_LANGUAGE",
+      "HAS_SOURCE",
+      "BM25_NEIGHBOR_OF",
+      "CONTAINED_IN"
+    ]
+  },
+  "vector": {
+    "model_name": "thenlper/gte-small",
+    "dimension": 384,
+    "similarity": "cosine",
+    "assignment": "recursive_spherical_kmeans",
+    "layout": "semantic_centroid_groups",
+    "rows_sorted_by": "cosine_similarity_to_shard_centroid_desc",
+    "max_rows_per_chunk": 4096,
+    "max_rows_per_centroid": 8192,
+    "max_shards_per_centroid": 2,
+    "default_probe_centroids": 4,
+    "centroid_count": 38,
+    "shard_count": 38,
+    "n_vectors": 101035
+  },
+  "schema_mapping": {
+    "laws": {
+      "id": "source_id / law_id",
+      "title": "title",
+      "text": "body",
+      "article_count_dtype_source": "int32"
+    },
+    "articles": {
+      "id": "source_id / article_id",
+      "law_id": "law_id / parent_law_id",
+      "title": "title",
+      "text": "body"
+    },
+    "required_law_columns": [
+      "id",
+      "title",
+      "text"
+    ],
+    "required_article_columns": [
+      "id",
+      "law_id",
+      "title",
+      "text"
+    ],
+    "fail_closed": true,
+    "notes": "Malta and Germany share the same column names. Drift: Malta article_count is int64, Germany article_count is int32; Germany eli is often null. Identifiers are never invented."
+  },
+  "indexes": {
+    "bm25_document_chunks": {
+      "cid": "bafkreigy5mkwikyr32y7ueh66ilgi7mko5qahm5mwhhzm3augoigiumuf4",
+      "sha256": "d8eb15642b11deb1fa10fef216647d8a776003b3acb1cf966c1433906451942f",
+      "size_bytes": 12624,
+      "relative_path": "indexes/bm25_document_chunks.parquet"
+    },
+    "bm25_keyword_shards": {
+      "cid": "bafkreietej6aj43v4jlcuiw47uzl334nfiwopiypwwewh56xhi2f7gryke",
+      "sha256": "93227c04f375e2562a22dcfd32bdef8d2a2ce7a30fb58963f7d73a345f9a3851",
+      "size_bytes": 12540,
+      "relative_path": "indexes/bm25_keyword_shards.parquet"
+    },
+    "corpus_chunks": {
+      "cid": "bafkreickhec272gyjf2kwuwjbtekvbtiu2uyrxgj4pepovr3qnovqzroqi",
+      "sha256": "4a3905afe8d84974ab52c90cc8aa8668a6a988dcc9e3c8f7563b835d58662e82",
+      "size_bytes": 12573,
+      "relative_path": "indexes/corpus_chunks.parquet"
+    },
+    "graph_edge_chunks": {
+      "cid": "bafkreiehamnnmqzpforjm4jcnoqxxbrfeacy76xfok24nvh42uuuslon7u",
+      "sha256": "87031ad6432f2ba29671226ba17b862520058ffae572b5c6d4fcd529492dcdfd",
+      "size_bytes": 46309,
+      "relative_path": "indexes/graph_edge_chunks.parquet"
+    },
+    "graph_incoming_adjacency": {
+      "cid": "bafkreidzjvcwdkr75invd7qvqaiefbsigo4av2fe46xnnjxs6of4w4b7tu",
+      "sha256": "794d4561aa3fea1b51fe15801042864833b80ae8a4e7aed6a6f2f38bcb703f9d",
+      "size_bytes": 15661,
+      "relative_path": "indexes/graph_incoming_adjacency.parquet"
+    },
+    "graph_node_chunks": {
+      "cid": "bafkreigsqcex65xxrzsw72lnvbgl2aypnhwzcz6666p4vmwx3p4ymuxhzm",
+      "sha256": "d280897f76f78e656fe96da84cbd030f69ed9167def79fcab2d7dbf98652e7cb",
+      "size_bytes": 12722,
+      "relative_path": "indexes/graph_node_chunks.parquet"
+    },
+    "graph_outgoing_adjacency": {
+      "cid": "bafkreid4qjp3dnulzqsac2jy4rteayhfoahmk46u5zkzewfuvzjj7wsley",
+      "sha256": "7c825fb1b68bcc24016938e4664060e5700ec573d4ee559258b4ae529fda4b26",
+      "size_bytes": 15642,
+      "relative_path": "indexes/graph_outgoing_adjacency.parquet"
+    },
+    "vector_chunks": {
+      "cid": "bafkreibf2hmove7uqm4xyi5jdvfmw3yrvjm35kgeyscowzg53cqxfiyxii",
+      "sha256": "25d1d8ea93f483397c23a91d4acb6f11aa59bea8c4c484eb64ddd8a172a31742",
+      "size_bytes": 133891,
+      "relative_path": "indexes/vector_chunks.parquet"
+    }
+  },
+  "source": {
+    "source_dataset": "endomorphosis/ipfs_argentina_laws",
+    "source_revision": "0361102728979d41f9e2953f8b03fcc4243d3039",
+    "laws_path": "/workspace/country-laws-ir/cache/hf/datasets--endomorphosis--ipfs_argentina_laws/snapshots/0361102728979d41f9e2953f8b03fcc4243d3039/data/laws.parquet",
+    "articles_path": "/workspace/country-laws-ir/cache/hf/datasets--endomorphosis--ipfs_argentina_laws/snapshots/0361102728979d41f9e2953f8b03fcc4243d3039/data/articles.parquet",
+    "n_laws_source": 8643,
+    "n_articles_source": 92507,
+    "laws_columns": [
+      "id",
+      "title",
+      "text",
+      "source_url",
+      "source_type",
+      "jurisdiction",
+      "country",
+      "language",
+      "eli",
+      "date",
+      "date_issued",
+      "retrieved_at",
+      "license",
+      "law_status",
+      "identifier",
+      "official_identifier",
+      "article_count",
+      "json_path",
+      "metadata_json"
+    ],
+    "articles_columns": [
+      "law_id",
+      "id",
+      "title",
+      "text",
+      "source_url",
+      "document_number",
+      "article_number",
+      "record_type",
+      "metadata_json"
+    ],
+    "article_count_dtype": "int32"
+  }
+}
diff --git a/scripts/generate_country_laws_ir.py b/scripts/generate_country_laws_ir.py
new file mode 100644
index 0000000000000000000000000000000000000000..766283c87af8b6bc7a22047e77e6b71e5737f8ad
--- /dev/null
+++ b/scripts/generate_country_laws_ir.py
@@ -0,0 +1,15 @@
+#!/usr/bin/env python3
+"""Generation entrypoint copied into Hub dataset repos."""
+
+from __future__ import annotations
+
+import sys
+from pathlib import Path
+
+ROOT = Path(__file__).resolve().parents[1]
+sys.path.insert(0, str(ROOT))
+
+from country_laws_ir.__main__ import main
+
+if __name__ == "__main__":
+    raise SystemExit(main())
diff --git a/scripts/query_country_laws_hf.py b/scripts/query_country_laws_hf.py
new file mode 100644
index 0000000000000000000000000000000000000000..1c88c8d93680b59a5993e75f27cb37a60a0f7183
--- /dev/null
+++ b/scripts/query_country_laws_hf.py
@@ -0,0 +1,200 @@
+#!/usr/bin/env python3
+"""Thin-client query for country-laws IR releases (local dir or Hub)."""
+
+from __future__ import annotations
+
+import argparse
+import json
+import math
+from collections import defaultdict
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import pyarrow.parquet as pq
+
+K1 = 1.2
+B = 0.75
+TITLE_WEIGHT = 5.0
+BODY_WEIGHT = 1.0
+MAX_QUERY_TERMS = 64
+
+
+def tokenize(text: str) -> list[str]:
+    import re
+    import unicodedata
+
+    if not text:
+        return []
+    nfkd = unicodedata.normalize("NFKD", text)
+    folded = "".join(ch for ch in nfkd if not unicodedata.combining(ch)).lower()
+    return re.findall(r"[0-9A-Za-z]+", folded)
+
+
+class Release:
+    def __init__(self, root: Path):
+        self.root = Path(root)
+        self.manifest = json.loads((self.root / "manifest.json").read_text(encoding="utf-8"))
+
+    def _read(self, rel: str) -> pd.DataFrame:
+        path = self.root / rel
+        if path.is_dir():
+            files = sorted(path.glob("*.parquet"))
+            return pd.concat([pd.read_parquet(f) for f in files], ignore_index=True) if files else pd.DataFrame()
+        return pd.read_parquet(path)
+
+    def bm25(self, query: str, top_k: int = 10) -> list[dict]:
+        q_terms = tokenize(query)[:MAX_QUERY_TERMS]
+        if not q_terms:
+            return []
+        shards = pd.read_parquet(self.root / "indexes" / "bm25_keyword_shards.parquet")
+        needed = set()
+        for term in q_terms:
+            hit = shards[(shards["first_key"] <= term) & (shards["last_key"] >= term)]
+            if hit.empty:
+                # fallback scan nearby shards
+                hit = shards
+            for rel in hit["relative_path"].tolist():
+                needed.add(rel)
+        postings = pd.concat(
+            [pd.read_parquet(self.root / rel) for rel in sorted(needed)],
+            ignore_index=True,
+        )
+        postings = postings[postings["term"].isin(q_terms)]
+        docs = self._read("data/bm25/documents")
+        avgdl = float(self.manifest["bm25"]["average_document_length"]) or 1.0
+        scores: dict[int, float] = defaultdict(float)
+        for rec in postings.itertuples(index=False):
+            idf = float(rec.idf)
+            for di, ttf, btf, dl in zip(rec.document_indices, rec.title_frequencies, rec.body_frequencies, rec.document_lengths):
+                tf = TITLE_WEIGHT * int(ttf) + BODY_WEIGHT * int(btf)
+                denom = tf + K1 * (1.0 - B + B * (int(dl) / avgdl))
+                if denom:
+                    scores[int(di)] += idf * (tf * (K1 + 1.0)) / denom
+        ranked = sorted(scores.items(), key=lambda kv: kv[1], reverse=True)[:top_k]
+        by_idx = docs.set_index("document_index")
+        out = []
+        for di, score in ranked:
+            row = by_idx.loc[di]
+            out.append(
+                {
+                    "document_index": int(di),
+                    "entry_cid": row["entry_cid"],
+                    "title": row["title"],
+                    "record_type": row["record_type"],
+                    "law_id": row["law_id"],
+                    "score": float(score),
+                }
+            )
+        return out
+
+    def vector(self, query: str, top_k: int = 10, candidate_centroids: int = 4, device: str = "cpu") -> list[dict]:
+        from sentence_transformers import SentenceTransformer
+
+        model = SentenceTransformer(self.manifest["vector"]["model_name"], device=device)
+        q = model.encode([query], normalize_embeddings=True, convert_to_numpy=True)[0].astype(np.float32)
+        meta = pd.read_parquet(self.root / "indexes" / "vector_chunks.parquet")
+        cents = np.stack(meta["centroid"].map(lambda c: np.asarray(c, dtype=np.float32)).to_numpy())
+        sims = cents @ q
+        order = np.argsort(-sims)[: max(1, candidate_centroids)]
+        shards = meta.iloc[order]
+        frames = [pd.read_parquet(self.root / rel) for rel in shards["relative_path"].tolist()]
+        vecs = pd.concat(frames, ignore_index=True)
+        emb = np.stack(vecs["embedding"].map(lambda e: np.asarray(e, dtype=np.float32)).to_numpy())
+        scores = emb @ q
+        top = np.argsort(-scores)[:top_k]
+        out = []
+        for i in top:
+            row = vecs.iloc[int(i)]
+            out.append(
+                {
+                    "document_index": int(row["document_index"]),
+                    "entry_cid": row["entry_cid"],
+                    "title": row["title"],
+                    "record_type": row["record_type"],
+                    "law_id": row["law_id"],
+                    "score": float(scores[int(i)]),
+                }
+            )
+        return out
+
+    def neighbors(self, node_cid: str, direction: str = "both", limit: int = 25) -> list[dict]:
+        dirs = ["incoming", "outgoing"] if direction == "both" else [direction]
+        hits = []
+        for d in dirs:
+            path = self.root / "data" / "graph" / "adjacency" / d
+            files = sorted(path.glob("*.parquet"))
+            for f in files:
+                df = pd.read_parquet(f)
+                sub = df[df["node_cid"] == node_cid]
+                for rec in sub.itertuples(index=False):
+                    for i, neigh in enumerate(rec.neighbor_cids):
+                        hits.append(
+                            {
+                                "direction": d,
+                                "node_cid": node_cid,
+                                "neighbor_cid": neigh,
+                                "edge_type": rec.edge_types[i] if i < len(rec.edge_types) else "",
+                                "score": rec.scores[i] if rec.scores is not None and i < len(rec.scores) else None,
+                            }
+                        )
+        hits.sort(key=lambda r: (-(r["score"] or 0), r["neighbor_cid"]))
+        return hits[:limit]
+
+
+def _print(rows: list[dict]) -> None:
+    print(json.dumps(rows, indent=2, ensure_ascii=False))
+
+
+def _resolve_root(args: argparse.Namespace) -> Path:
+    if args.local_dir:
+        return Path(args.local_dir)
+    if args.repo_id:
+        from huggingface_hub import snapshot_download
+
+        from .auth import configure_hf, load_token
+
+        configure_hf()
+        kwargs = {"repo_id": args.repo_id, "repo_type": "dataset", "token": load_token()}
+        if args.revision:
+            kwargs["revision"] = args.revision
+        return Path(snapshot_download(**kwargs))
+    raise SystemExit("pass --local-dir or --repo-id")
+
+
+def main(argv: list[str] | None = None) -> int:
+    p = argparse.ArgumentParser(description="Query a country-laws IR release")
+    p.add_argument("--local-dir")
+    p.add_argument("--repo-id")
+    p.add_argument("--revision", default=None)
+    sub = p.add_subparsers(dest="cmd", required=True)
+
+    b = sub.add_parser("bm25")
+    b.add_argument("query")
+    b.add_argument("--top-k", type=int, default=10)
+
+    v = sub.add_parser("vector")
+    v.add_argument("query")
+    v.add_argument("--top-k", type=int, default=10)
+    v.add_argument("--candidate-centroids", type=int, default=4)
+    v.add_argument("--device", default="cpu")
+
+    g = sub.add_parser("graph")
+    g.add_argument("action", choices=["neighbors"])
+    g.add_argument("node_cid")
+    g.add_argument("--direction", default="both")
+    g.add_argument("--limit", type=int, default=25)
+
+    args = p.parse_args(argv)
+    rel = Release(_resolve_root(args))
+    if args.cmd == "bm25":
+        _print(rel.bm25(args.query, top_k=args.top_k))
+    elif args.cmd == "vector":
+        _print(rel.vector(args.query, top_k=args.top_k, candidate_centroids=args.candidate_centroids, device=args.device))
+    elif args.cmd == "graph":
+        _print(rel.neighbors(args.node_cid, direction=args.direction, limit=args.limit))
+    return 0
+
+
+if __name__ == "__main__":
+    raise SystemExit(main())