Added VertexAI support, including GCP ADC Resolution

.env.example

@@ -2,7 +2,7 @@ ENV=dev                                                         # options: dev|s
 
 # Active LLM provider.  Selects which provider answers credentials,
 # metadata, and default-model lookups.  Leave unset to default to nv_build.
-# Options: openai | anthropic | nv_build
+# Options: openai | anthropic | vertexai | nv_build
 SKILLSPECTOR_PROVIDER=
 
 # Provider credentials — set the one matching SKILLSPECTOR_PROVIDER (or
@@ -21,6 +21,11 @@ OPENAI_BASE_URL=
 # For SKILLSPECTOR_PROVIDER=anthropic.
 ANTHROPIC_API_KEY=
 
+# For SKILLSPECTOR_PROVIDER=vertexai
+GOOGLE_APPLICATION_CREDENTIALS=
+GOOGLE_CLOUD_PROJECT=
+GOOGLE_CLOUD_LOCATION=
+
 # SkillSpector config
 SKILLSPECTOR_MODEL=                                             # leave empty to use the active provider's bundled default (see README); set to override (e.g. gpt-5.2)
 # SKILLSPECTOR_MODEL_REGISTRY=./model_registry.yaml             # optional override; defaults to each provider's bundled YAML in src/skillspector/providers/

README.md

@@ -109,6 +109,14 @@ export SKILLSPECTOR_PROVIDER=nv_build
 export NVIDIA_INFERENCE_KEY=nvapi-...
 skillspector scan ./my-skill/
 
+# VertexAI (Google Cloud)
+export SKILLSPECTOR_PROVIDER=vertexai
+export GOOGLE_APPLICATION_CREDENTIALS=/path/to/service-account.json
+export GOOGLE_CLOUD_PROJECT=your-project-id
+export GOOGLE_CLOUD_LOCATION=us-central1
+export SKILLSPECTOR_MODEL=gemini-2.5-pro
+skillspector scan ./my-skill/
+
 # Local Ollama or any OpenAI-compatible endpoint
 export SKILLSPECTOR_PROVIDER=openai
 export OPENAI_API_KEY=ollama

model_registry.yaml

@@ -40,3 +40,24 @@ models:
   "openai/openai/gpt-5.3-chat":
     context_length: 128000
     max_output_tokens: 16384
+
+  # Google Gemini models (via VertexAI or AI Studio OpenAI-compatible endpoints)
+  "gemini-2.0-flash":
+    context_length: 1048576
+    max_output_tokens: 8192
+
+  "gemini-2.5-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-2.5-flash":
+    context_length: 1048576
+    max_output_tokens: 65535
+
+  "gemini-3.1-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-3.5-flash":
+    context_length: 1048576
+    max_output_tokens: 8192

pyproject.toml

@@ -42,6 +42,7 @@ dependencies = [
     "langchain-openai>=1.1.10",
     "langsmith>=0.7.30",
     "yara-python>=4.5.0",
+    "google-auth>=2.53.0",
 ]
 
 [project.optional-dependencies]

src/skillspector/providers/__init__.py

@@ -49,6 +49,10 @@ def _select_active_provider() -> ModelMetadataProvider:
         from .anthropic import AnthropicProvider
 
         return AnthropicProvider()
+    if name == "vertexai":
+        from .vertexai import VertexAIProvider
+
+        return VertexAIProvider()
     if name == "nv_build":
         return NvBuildProvider()
     if name in ("nv_inference", ""):
@@ -63,7 +67,7 @@ def _select_active_provider() -> ModelMetadataProvider:
 
     raise ValueError(
         f"Unknown SKILLSPECTOR_PROVIDER: {name!r}. "
-        "Expected one of: openai, anthropic, nv_build (or unset)."
+        "Expected one of: openai, anthropic, vertexai, nv_build (or unset)."
     )
 
 

src/skillspector/providers/nv_build/model_registry.yaml

@@ -26,3 +26,24 @@ models:
   "openai/gpt-oss-120b":
     context_length: 128000
     max_output_tokens: 16384
+
+  # Google Gemini models (via VertexAI or AI Studio OpenAI-compatible endpoints)
+  "gemini-2.0-flash":
+    context_length: 1048576
+    max_output_tokens: 8192
+
+  "gemini-2.5-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-2.5-flash":
+    context_length: 1048576
+    max_output_tokens: 65535
+
+  "gemini-3.1-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-3.5-flash":
+    context_length: 1048576
+    max_output_tokens: 8192

src/skillspector/providers/openai/model_registry.yaml

@@ -12,3 +12,24 @@ models:
   "gpt-5.4":
     context_length: 1000000
     max_output_tokens: 128000
+
+  # Google Gemini models (via VertexAI or AI Studio OpenAI-compatible endpoints)
+  "gemini-2.0-flash":
+    context_length: 1048576
+    max_output_tokens: 8192
+
+  "gemini-2.5-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-2.5-flash":
+    context_length: 1048576
+    max_output_tokens: 65535
+
+  "gemini-3.1-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-3.5-flash":
+    context_length: 1048576
+    max_output_tokens: 8192

src/skillspector/providers/vertexai/__init__.py

@@ -0,0 +1,20 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""VertexAI provider package (VertexAI OpenAI-compatibility endpoint)."""
+
+from .provider import REGISTRY_PATH, VertexAIProvider
+
+__all__ = ["REGISTRY_PATH", "VertexAIProvider"]

src/skillspector/providers/vertexai/model_registry.yaml

@@ -0,0 +1,30 @@
+# Token-budget metadata for the VertexAIProvider. Bundled with the
+# package; consulted whenever the active provider is VertexAIProvider.
+#
+# Format:
+#   models:
+#     "<model-label>":
+#       context_length: <int>          # total context window in tokens (required)
+#       max_output_tokens: <int>       # model's max output cap (optional)
+
+models:
+  # Google Gemini models (via VertexAI)
+  "gemini-2.0-flash":
+    context_length: 1048576
+    max_output_tokens: 8192
+
+  "gemini-2.5-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-2.5-flash":
+    context_length: 1048576
+    max_output_tokens: 65535
+
+  "gemini-3.1-pro":
+    context_length: 1000000
+    max_output_tokens: 65536
+
+  "gemini-3.5-flash":
+    context_length: 1048576
+    max_output_tokens: 8192

src/skillspector/providers/vertexai/provider.py

@@ -0,0 +1,107 @@
+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""VertexAI provider — Gemini models via VertexAI OpenAI-compatible endpoint.
+
+Reads ``GOOGLE_APPLICATION_CREDENTIALS``, ``GOOGLE_CLOUD_PROJECT``, and
+``GOOGLE_CLOUD_LOCATION`` for credentials and constructs the VertexAI
+OpenAI-compatible endpoint URL.  Uses Google Cloud Application Default
+Credentials (ADC) to generate access tokens.  Defaults to Gemini 2.5 Flash.
+"""
+
+from __future__ import annotations
+
+import os
+from pathlib import Path
+
+import google.auth
+import google.auth.transport.requests
+
+from skillspector.providers import registry
+
+REGISTRY_PATH = str(Path(__file__).with_name("model_registry.yaml"))
+
+
+class VertexAIProvider:
+    """Stock VertexAI credentials + bundled-YAML metadata provider."""
+
+    DEFAULT_MODEL = "gemini-2.5-flash"
+    SLOT_DEFAULTS: dict[str, str] = {}
+
+
+    def resolve_credentials(self) -> tuple[str, str | None] | None:
+        """Return ``(access_token, base_url)`` from Google Cloud credentials.
+
+        Uses Application Default Credentials (ADC) via ``google.auth.default()``.
+        The access token is refreshed from the credentials object and returned
+        as the API key for the OpenAI-compatible client.
+
+        Returns ``None`` when required environment variables are not set.
+
+        Raises:
+            google.auth.exceptions.DefaultCredentialsError: When credentials
+                are configured but invalid or malformed.
+            ValueError: When project cannot be determined or token refresh fails.
+        """
+
+        project_id = os.environ.get("GOOGLE_CLOUD_PROJECT", "").strip()
+        location = os.environ.get("GOOGLE_CLOUD_LOCATION", "").strip()
+
+        if not project_id or not location:
+            return None
+
+        # If we get here, the user explicitly configured VertexAI,
+        # so let authentication errors propagate for debugging
+
+
+        credentials, default_project = google.auth.default(
+            scopes=["https://www.googleapis.com/auth/cloud-platform"]
+        )
+
+        project = project_id or default_project
+        if not project:
+            raise ValueError(
+                "Could not determine GCP project. Ensure GOOGLE_CLOUD_PROJECT "
+                "is set or the credentials file contains a project ID."
+            )
+
+        credentials.refresh(google.auth.transport.requests.Request())
+
+        access_token = credentials.token
+        if not access_token:
+            raise ValueError(
+                "Failed to obtain access token from Google Cloud credentials. "
+                "Ensure GOOGLE_APPLICATION_CREDENTIALS points to a valid "
+                "service account key file."
+            )
+
+        # Construct the VertexAI OpenAI-compatible base URL
+        base_url = (
+            f"https://{location}-aiplatform.googleapis.com/v1beta1/"
+            f"projects/{project}/locations/{location}/endpoints/openapi"
+        )
+
+        return access_token, base_url
+
+    def get_context_length(self, model: str) -> int | None:
+        return registry.lookup_context_length(REGISTRY_PATH, model)
+
+    def get_max_output_tokens(self, model: str) -> int | None:
+        return registry.lookup_max_output_tokens(REGISTRY_PATH, model)
+
+    def resolve_model(self, slot: str = "default") -> str:
+        """Resolve model: ``SKILLSPECTOR_MODEL`` env > slot default > ``DEFAULT_MODEL``."""
+        user_input = os.environ.get("SKILLSPECTOR_MODEL", "").strip()
+        return user_input or self.SLOT_DEFAULTS.get(slot, "") or self.DEFAULT_MODEL