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diff --git a/data_integrity_summary.json b/data_integrity_summary.json
new file mode 100644
index 0000000000000000000000000000000000000000..388bc220479b76cd06e1bd430838078d564a5c54
--- /dev/null
+++ b/data_integrity_summary.json
@@ -0,0 +1,29 @@
+{
+  "data_storage": "hardlink_tree_to_source_for_local_standardization; paths under data/Eagle_dataset are real file entries sharing inodes with the source on this filesystem",
+  "file_name_counts": {
+    "constrained_kmeans_10.npy": 1200,
+    "constrained_kmeans_20.npy": 1200,
+    "constrained_kmeans_30.npy": 1200,
+    "constrained_kmeans_40.npy": 1200,
+    "sim.npz": 1200,
+    "triangles.npy": 1200
+  },
+  "geometry_file_counts": {
+    "Cre": 2400,
+    "Spl": 2400,
+    "Tri": 2400
+  },
+  "sample_directories": 1200,
+  "sha256_inventory": "files_sha256.jsonl",
+  "source_root": "/public/home/easyscience2024/lys/cfd_dataset/Eagle/Eagle_dataset",
+  "split_counts": {
+    "test": 118,
+    "train": 947,
+    "valid": 118
+  },
+  "standardized_data_path": "data/Eagle_dataset",
+  "standardized_dataset_root": "/public/home/easyscience2024/lys/modelscope_standardized/dataset/cfd_eagle",
+  "standardized_file_count": 7200,
+  "total_files": 7200,
+  "total_size_bytes": 354770576850
+}
diff --git a/dataset_infos.json b/dataset_infos.json
new file mode 100644
index 0000000000000000000000000000000000000000..178575806c3465b75e036a5322dbe78bd8cb2709
--- /dev/null
+++ b/dataset_infos.json
@@ -0,0 +1 @@
+{"default": {"features": {"text": {"_type": "Value"}}, "splits": {"train": {"name": "train", "dataset_name": "eagle"}, "validation": {"name": "validation", "dataset_name": "eagle"}, "test": {"name": "test", "dataset_name": "eagle"}}}}
\ No newline at end of file
diff --git a/files_sha256.jsonl b/files_sha256.jsonl
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index 0000000000000000000000000000000000000000..642ebf413f3b43bb89ad46fbc6d61fabec3031af
--- /dev/null
+++ b/files_sha256.jsonl
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+oid sha256:b29ebff7b89ca2104f87c88cf6bd987549f2715418aa99b21d4deda285127e08
+size 998158
diff --git a/metadata/schema.yaml b/metadata/schema.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..5fe860af8ea2ab46baca08c9c71d7da33fe72866
--- /dev/null
+++ b/metadata/schema.yaml
@@ -0,0 +1,59 @@
+dataset: Eagle
+format:
+  simulation: npz
+  mesh_cells: npy
+  clusters: npy
+sample_unit: one geometry trajectory directory, for example Tri/174/2
+required_files:
+  - sim.npz
+  - triangles.npy
+  - constrained_kmeans_10.npy
+  - constrained_kmeans_20.npy
+  - constrained_kmeans_30.npy
+  - constrained_kmeans_40.npy
+sim_npz_keys:
+  pointcloud:
+    shape: [time, nodes, 2]
+    dtype: float32
+    description_zh: 二维网格节点坐标
+  mask:
+    shape: [time, nodes]
+    dtype: int8
+    description_zh: 节点类型编码
+  VX:
+    shape: [time, nodes]
+    dtype: float32
+    description_zh: x 方向速度
+  VY:
+    shape: [time, nodes]
+    dtype: float32
+    description_zh: y 方向速度
+  PS:
+    shape: [time, nodes]
+    dtype: float32
+    description_zh: 静压分量
+  PG:
+    shape: [time, nodes]
+    dtype: float32
+    description_zh: 压力梯度或压力相关分量
+mesh:
+  triangles.npy:
+    shape: [time, cells, 3]
+    dtype: int64
+clusters:
+  constrained_kmeans_10.npy:
+    shape: [time, cluster_groups, 10]
+    dtype: int32
+  constrained_kmeans_20.npy:
+    shape: [time, cluster_groups, 20]
+    dtype: int32
+  constrained_kmeans_30.npy:
+    shape: [time, cluster_groups, 30]
+    dtype: int32
+  constrained_kmeans_40.npy:
+    shape: [time, cluster_groups, 40]
+    dtype: int32
+splits:
+  train: metadata/train.txt
+  valid: metadata/valid.txt
+  test: metadata/test.txt
diff --git a/metadata/test.txt b/metadata/test.txt
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--- /dev/null
+++ b/metadata/test.txt
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:c634b0e9be7688aa66c97a392a0b755bed30f6c52a4a8fe13796bca5c99224aa
+size 1180
diff --git a/metadata/train.txt b/metadata/train.txt
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--- /dev/null
+++ b/metadata/train.txt
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+oid sha256:0dac4bae3ab7de2b9d153fc31189054399e5099e1b04045f3e7341a211ea97a9
+size 8825
diff --git a/metadata/valid.txt b/metadata/valid.txt
new file mode 100644
index 0000000000000000000000000000000000000000..279bc255deaee004b2ca0d5c932d22d52485ce5b
--- /dev/null
+++ b/metadata/valid.txt
@@ -0,0 +1,3 @@
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+size 1180
diff --git a/onescience_run_manifest.yaml b/onescience_run_manifest.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2d9e5cdc8f68cb943a31af4493dbe59376c76b7a
--- /dev/null
+++ b/onescience_run_manifest.yaml
@@ -0,0 +1,310 @@
+onescience_manifest_version: "0.1"
+resource_type: dataset
+resource:
+  id: OneScience/eagle
+  name: eagle
+  domain: cfd
+  domain_tags: [cfd, eagle, turbulent_flow, unstructured_mesh]
+  task: eagle_flow_forecasting_dataset
+  task_tags: [train_data, eval_data, visualization_input]
+  modalities: [unstructured_mesh, temporal_field]
+  input_formats: [npz, npy]
+  output_formats: []
+  summary: Eagle 数据集标准包，包含 Cre/Spl/Tri 三类二维非定常流体仿真样本及 GraphViT 所需聚类文件。
+  language: zh
+  license: unknown
+
+platform_resource:
+  primary:
+    platform: modelscope
+    repo_id: OneScience/eagle
+    repo_type: dataset
+    url: https://modelscope.cn/datasets/OneScience/eagle
+    revision: main
+    readme_path: README.md
+    manifest_path: onescience_run_manifest.yaml
+  mirrors: []
+  access:
+    visibility: public
+    license: null
+
+website_integration:
+  enabled: true
+  click_target:
+    platform: modelscope
+    resource_url: https://modelscope.cn/datasets/OneScience/eagle
+  llm_handoff:
+    readme_required: true
+    manifest_required: true
+    download_readme_first: true
+    resolve_related_models: true
+    default_run_goal: minimal_validation
+    default_workdir: .
+    cwd_note: 如果使用 modelscope download --cache_dir 下载，请先 cd 到实际下载后的数据集仓库根目录，模型侧再把 ONESCIENCE_EAGLE_DATA_DIR 指向 data/Eagle_dataset。
+
+runtime:
+  enabled: true
+  onescience_domain: cfd
+  supported_execution: [local_dataset_validation, model_training_input, model_evaluation_input]
+  environment:
+    exported_env:
+      ONESCIENCE_EAGLE_DATA_DIR: <dataset_repo_root>/data/Eagle_dataset
+  dependencies:
+    python_packages: [numpy, ruamel.yaml]
+
+onescience:
+  repo: https://gitee.com/onescience-ai/onescience
+  official_links:
+    gitee:
+      doc: https://gitee.com/onescience-ai/onescience-doc
+      onescience: https://gitee.com/onescience-ai/onescience
+      skills: https://gitee.com/onescience-ai/oneskills
+    github:
+      doc: https://github.com/onescience-ai/OneScience-doc
+      onescience: https://github.com/onescience-ai/OneScience
+      skills: https://github.com/onescience-ai/oneskills
+  install:
+    required_by_default: false
+    command: bash install.sh cfd
+  compatibility:
+    examples_path: onescience/examples/cfd/EagleMeshTransformer
+    status: examples_compatible
+  datapipe: onescience.datapipes.cfd.EagleDatapipe
+
+runtime_package:
+  kind: standard_runtime_package
+  package_root: .
+  standard_layout:
+    workdir: .
+    data_dir: data/Eagle_dataset
+    metadata_dir: metadata
+    output_dir: validation_outputs
+  apply_policy:
+    mode: direct_use
+    target: session_workdir
+    overwrite: false
+    protect_installed_onescience: true
+  entry_files:
+    - data/Eagle_dataset
+    - metadata/schema.yaml
+    - metadata/train.txt
+    - metadata/valid.txt
+    - metadata/test.txt
+    - files_sha256.jsonl
+    - scripts/validate_eagle_dataset.py
+  entrypoints:
+    preflight: scripts/validate_eagle_dataset.py
+    inference: null
+    train: null
+    finetune: null
+    evaluate: null
+    visualize: null
+    deploy: null
+
+files:
+  dataset_files:
+    - id: eagle_data_root
+      path: data/Eagle_dataset
+      role: train_eval_data
+      description_zh: EAGLE 原始仿真和聚类数据根目录，包含 Cre/Spl/Tri。
+      required: true
+      required_for: [preflight, train, evaluate, visualize]
+      source:
+        platform: modelscope
+        repo_id: OneScience/eagle
+        repo_type: dataset
+        path: data/Eagle_dataset
+        revision: main
+        download_method: command_ref
+        command_ref: commands.download.download_dataset
+      local_path: data/Eagle_dataset
+    - id: eagle_schema
+      path: metadata/schema.yaml
+      role: schema
+      description_zh: 描述 sim.npz、triangles.npy 和 constrained_kmeans 文件的字段、shape 和 dtype。
+      required: true
+      required_for: [preflight]
+      source: {platform: modelscope, repo_id: OneScience/eagle, repo_type: dataset, path: metadata/schema.yaml, revision: main, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: metadata/schema.yaml
+    - id: eagle_sha256_inventory
+      path: files_sha256.jsonl
+      role: file_size_sha256_inventory
+      description_zh: 每个数据文件的相对路径、大小和 SHA256，用于上传前和下载后完整性校验。
+      required: true
+      required_for: [preflight]
+      source: {platform: modelscope, repo_id: OneScience/eagle, repo_type: dataset, path: files_sha256.jsonl, revision: main, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: files_sha256.jsonl
+  config_files: []
+  sample_files:
+    - id: eagle_split_metadata
+      path: metadata/*.txt
+      role: split_files
+      description_zh: EagleMeshTransformer 使用的 train/valid/test 相对路径列表。
+      required: true
+      required_for: [train, evaluate]
+      source: {platform: modelscope, repo_id: OneScience/eagle, repo_type: dataset, path: metadata/, revision: main, download_method: command_ref, command_ref: commands.download.download_dataset}
+      local_path: metadata/
+
+dataset:
+  format: [npz, npy]
+  sample_unit: Cre/Spl/Tri 下的一个 case/trajectory 目录
+  schema:
+    path: metadata/schema.yaml
+  splits:
+    train:
+      path: metadata/train.txt
+      count: 947
+    validation:
+      path: metadata/valid.txt
+      count: 118
+    test:
+      path: metadata/test.txt
+      count: 118
+  size:
+    sample_directories: 1200
+    files: 7200
+    geometry_directories:
+      Cre: 400
+      Spl: 400
+      Tri: 400
+
+relations:
+  compatible_models:
+    - id: OneScience/EagleMeshTransformer
+      name: EagleMeshTransformer
+      role: train_data
+      required_for: [preflight, train, inference, evaluate, visualize]
+      resource_ref:
+        platform: modelscope
+        repo_id: OneScience/EagleMeshTransformer
+        repo_type: model
+        url: https://modelscope.cn/models/OneScience/EagleMeshTransformer
+        revision: main
+        readme_path: README.md
+        manifest_path: onescience_run_manifest.yaml
+      expected_local_path: ${ONESCIENCE_EAGLE_DATA_DIR}
+      expected_model_env: ONESCIENCE_EAGLE_DATA_DIR
+      expected_model_config: conf/graphvit_eagle.yaml
+  required_by_models:
+    - resource_ref:
+        platform: modelscope
+        repo_id: OneScience/EagleMeshTransformer
+        repo_type: model
+        url: https://modelscope.cn/models/OneScience/EagleMeshTransformer
+        revision: main
+        readme_path: README.md
+        manifest_path: onescience_run_manifest.yaml
+
+run_matrix:
+  scenarios:
+    - name: validate_structure
+      capability: dataset_preflight
+      default: true
+      required_datasets:
+        - id: OneScience/eagle
+          role: train_data
+          local_path: data/Eagle_dataset
+      required_model_files: []
+      required_dataset_files:
+        - data/Eagle_dataset/Cre|Spl|Tri/<case>/<traj>/sim.npz
+        - data/Eagle_dataset/Cre|Spl|Tri/<case>/<traj>/triangles.npy
+        - data/Eagle_dataset/Cre|Spl|Tri/<case>/<traj>/constrained_kmeans_10.npy
+        - data/Eagle_dataset/Cre|Spl|Tri/<case>/<traj>/constrained_kmeans_20.npy
+        - data/Eagle_dataset/Cre|Spl|Tri/<case>/<traj>/constrained_kmeans_30.npy
+        - data/Eagle_dataset/Cre|Spl|Tri/<case>/<traj>/constrained_kmeans_40.npy
+      preconditions: [当前工作目录为数据集仓库根目录。]
+      command_refs: [commands.preflight.validate_dataset]
+      outputs:
+        - '控制台输出 "[OK] dataset validation completed"'
+    - name: validate_integrity
+      capability: dataset_integrity
+      required_datasets:
+        - id: OneScience/eagle
+          role: train_data
+          local_path: data/Eagle_dataset
+      required_model_files: []
+      required_dataset_files:
+        - files_sha256.jsonl
+        - "data/Eagle_dataset/**"
+      preconditions:
+        - 当前工作目录为数据集仓库根目录。
+      command_refs: [commands.preflight.validate_dataset_full_hash]
+      outputs:
+        - '控制台输出 "[OK] checksum manifest verified in size+sha256 mode"'
+    - name: provide_to_model
+      capability: train
+      required_datasets:
+        - id: OneScience/eagle
+          role: train_data
+          local_path: data/Eagle_dataset
+      required_model_files:
+        - "OneScience/EagleMeshTransformer:conf/graphvit_eagle.yaml"
+      required_dataset_files:
+        - "data/Eagle_dataset/**/sim.npz"
+        - "data/Eagle_dataset/**/triangles.npy"
+        - "data/Eagle_dataset/**/constrained_kmeans_40.npy"
+      preconditions:
+        - 模型仓库已下载。
+      command_refs: [commands.prepare.export_dataset_env]
+      outputs:
+        - 模型侧 scripts/preflight_check.py 可以通过数据目录检查。
+
+capabilities:
+  dataset_validation: true
+  train_input: true
+  inference_input: true
+  evaluation_input: true
+  visualization_input: true
+
+commands:
+  download:
+    - name: download_dataset
+      cwd: session_workdir
+      command: modelscope download --dataset OneScience/eagle
+    - name: download_model
+      cwd: session_workdir
+      command: modelscope download --model OneScience/EagleMeshTransformer
+  prepare:
+    - name: export_dataset_env
+      cwd: .
+      command: export ONESCIENCE_EAGLE_DATA_DIR=/path/to/OneScience_eagle/data/Eagle_dataset
+  preflight:
+    - name: validate_dataset
+      cwd: .
+      command: python scripts/validate_eagle_dataset.py
+    - name: validate_dataset_full_hash
+      cwd: .
+      command: python scripts/validate_eagle_dataset.py --full-hash
+  train: []
+  inference: []
+  evaluate: []
+  visualize: []
+  finetune: []
+  deploy: []
+
+expected_outputs:
+  validate_dataset: ["[OK] dataset validation completed"]
+  validate_dataset_full_hash: ["[OK] checksum manifest verified in size+sha256 mode: 7200 files"]
+  model_consumption: [模型包 scripts/preflight_check.py 通过数据目录检查。]
+
+diagnostics:
+  - id: missing_data_root
+    symptom: dataset root not found
+    action: 确认当前目录为 OneScience/eagle 数据集仓库根目录，且 data/Eagle_dataset 存在。
+  - id: missing_sample_file
+    symptom: missing required file
+    action: 对照 files_sha256.jsonl 重新上传或重新下载缺失样本目录。
+  - id: checksum_mismatch
+    symptom: sha256 mismatch
+    action: 数据文件损坏或未完整上传，需重新同步对应文件。
+  - id: schema_mismatch
+    symptom: unexpected pointcloud shape 或 unexpected dtype
+    action: 检查是否误用了非 Eagle v1 数据，或文件在解压/上传过程中被替换。
+
+domain_extension:
+  cfd:
+    geometry_types: [Cre, Spl, Tri]
+    mesh_type: unstructured_triangular_mesh
+    variables: [pointcloud, mask, VX, VY, PS, PG]
+    temporal_steps_per_sample: 990
+    cluster_files: [constrained_kmeans_10.npy, constrained_kmeans_20.npy, constrained_kmeans_30.npy, constrained_kmeans_40.npy]
diff --git a/scripts/validate_eagle_dataset.py b/scripts/validate_eagle_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..508d41c64459ac2218bc12475b1f9c2b9c68a3bf
--- /dev/null
+++ b/scripts/validate_eagle_dataset.py
@@ -0,0 +1,134 @@
+#!/usr/bin/env python3
+import argparse
+import hashlib
+import json
+from pathlib import Path
+
+import numpy as np
+
+
+REQUIRED_KEYS = ["pointcloud", "mask", "VX", "VY", "PS", "PG"]
+REQUIRED_FILES = [
+    "sim.npz",
+    "triangles.npy",
+    "constrained_kmeans_10.npy",
+    "constrained_kmeans_20.npy",
+    "constrained_kmeans_30.npy",
+    "constrained_kmeans_40.npy",
+]
+
+
+def fail(message):
+    raise SystemExit(f"[ERROR] {message}")
+
+
+def sha256_file(path):
+    h = hashlib.sha256()
+    with path.open("rb") as f:
+        for chunk in iter(lambda: f.read(1024 * 1024), b""):
+            h.update(chunk)
+    return h.hexdigest()
+
+
+def iter_inventory(path):
+    with path.open("r", encoding="utf-8") as f:
+        for line in f:
+            if line.strip():
+                yield json.loads(line)
+
+
+def inspect_sample(sample_dir):
+    for name in REQUIRED_FILES:
+        if not (sample_dir / name).exists():
+            fail(f"missing required file: {sample_dir / name}")
+
+    with np.load(sample_dir / "sim.npz", mmap_mode="r") as data:
+        missing = [key for key in REQUIRED_KEYS if key not in data.files]
+        if missing:
+            fail(f"{sample_dir / 'sim.npz'} missing keys: {missing}")
+        pointcloud = data["pointcloud"]
+        mask = data["mask"]
+        if pointcloud.ndim != 3 or pointcloud.shape[-1] != 2:
+            fail(f"unexpected pointcloud shape: {pointcloud.shape}")
+        if pointcloud.dtype != np.float32:
+            fail(f"unexpected pointcloud dtype: {pointcloud.dtype}")
+        if mask.shape != pointcloud.shape[:2]:
+            fail(f"mask shape {mask.shape} does not match pointcloud {pointcloud.shape[:2]}")
+        for key in ["VX", "VY", "PS", "PG"]:
+            arr = data[key]
+            if arr.shape != pointcloud.shape[:2]:
+                fail(f"{key} shape {arr.shape} does not match pointcloud {pointcloud.shape[:2]}")
+            if arr.dtype != np.float32:
+                fail(f"{key} dtype should be float32, got {arr.dtype}")
+
+    triangles = np.load(sample_dir / "triangles.npy", mmap_mode="r")
+    if triangles.ndim != 3 or triangles.shape[0] != pointcloud.shape[0] or triangles.shape[-1] != 3:
+        fail(f"unexpected triangles shape: {triangles.shape}")
+
+    for n_cluster in [10, 20, 30, 40]:
+        clusters = np.load(sample_dir / f"constrained_kmeans_{n_cluster}.npy", mmap_mode="r")
+        if clusters.ndim != 3 or clusters.shape[0] != pointcloud.shape[0] or clusters.shape[-1] != n_cluster:
+            fail(f"unexpected constrained_kmeans_{n_cluster}.npy shape: {clusters.shape}")
+
+
+def validate_inventory(dataset_root, inventory_path, full_hash):
+    checked = 0
+    for item in iter_inventory(inventory_path):
+        rel = item["path"]
+        path = dataset_root / rel
+        if not path.exists():
+            fail(f"inventory path missing: {path}")
+        size = path.stat().st_size
+        if size != item["size"]:
+            fail(f"size mismatch for {rel}: expected {item['size']}, got {size}")
+        if full_hash:
+            digest = sha256_file(path)
+            if digest != item["sha256"]:
+                fail(f"sha256 mismatch for {rel}: expected {item['sha256']}, got {digest}")
+        checked += 1
+    return checked
+
+
+def main():
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--data-root", default="data/Eagle_dataset")
+    parser.add_argument("--sample-limit", type=int, default=3)
+    parser.add_argument("--full-hash", action="store_true")
+    args = parser.parse_args()
+
+    repo_root = Path.cwd()
+    dataset_root = repo_root / args.data_root
+    inventory_path = repo_root / "files_sha256.jsonl"
+    summary_path = repo_root / "data_integrity_summary.json"
+
+    if not dataset_root.exists():
+        fail(f"dataset root not found: {dataset_root}")
+    if not inventory_path.exists():
+        fail(f"inventory not found: {inventory_path}")
+    if not summary_path.exists():
+        fail(f"summary not found: {summary_path}")
+
+    for geom in ["Cre", "Spl", "Tri"]:
+        geom_dir = dataset_root / geom
+        if not geom_dir.exists():
+            fail(f"missing geometry directory: {geom_dir}")
+
+    sample_dirs = sorted(p for p in dataset_root.glob("*/*/*") if p.is_dir())
+    if len(sample_dirs) != 1200:
+        fail(f"expected 1200 sample directories, got {len(sample_dirs)}")
+    for sample_dir in sample_dirs[: args.sample_limit]:
+        inspect_sample(sample_dir)
+
+    checked = validate_inventory(dataset_root, inventory_path, args.full_hash)
+    if checked != 7200:
+        fail(f"expected 7200 inventory files, got {checked}")
+
+    if args.full_hash:
+        print(f"[OK] checksum manifest verified in size+sha256 mode: {checked} files")
+    else:
+        print(f"[OK] checksum manifest verified in size mode: {checked} files")
+    print(f"[OK] dataset validation completed: {len(sample_dirs)} samples, sampled {args.sample_limit}")
+
+
+if __name__ == "__main__":
+    main()