refactor(agent): rename agent_cli to metadata_ops, strip temp debug logs

- Rename py/agent_cli/ -> py/metadata_ops/ (module was never agent-related) - Rename tests/agent_cli/ -> tests/metadata_ops/ - Remove 9 low-value/debug INFO log points across agent_handlers.py, agent_service.py, llm_service.py, and metadata_ops/__init__.py - Keep LLM raw response at DEBUG level for diagnostics - Consolidate per-model progress + LLM result into single concise log line with basename instead of full path - Update package/class/method docstrings to clarify this is a pipeline infrastructure, not a true agent loop
2026-07-06 01:11:17 -03:00 · 2026-07-05 18:00:58 +08:00
parent 7b19bbb14e
commit 51c0135250
12 changed files with 113 additions and 126 deletions
--- a/py/metadata_ops/init.py
+++ b/py/metadata_ops/init.py
@@ -1,4 +1,4 @@
-"""Agent CLI — thin in-process wrappers around LoRA Manager internal services.
+"""Metadata operations — thin in-process wrappers around LoRA Manager internal services.

 All functions are simple Python async functions that delegate to the
 appropriate internal service.  They use **relative imports** within the
@@ -7,15 +7,15 @@ risk of double import or circular dependencies.

 Usage (in-process, primary)::

-    from py.agent_cli import list_base_models, read_metadata
+    from py.metadata_ops import list_base_models, read_metadata

    models = await list_base_models()
    meta   = await read_metadata("/path/to/model.safetensors")

 Usage (subprocess, debugging / external)::

-    python -m py.agent_cli base-models list
-    python -m py.agent_cli metadata read /path/to/model.safetensors
+    python -m py.metadata_ops base-models list
+    python -m py.metadata_ops metadata read /path/to/model.safetensors
 """

 from __future__ import annotations
@@ -214,7 +214,6 @@ async def download_preview(
            )
            with open(output_path, "wb") as f:
                f.write(optimized_data)
-            logger.info("Preview downloaded and optimised for %s", model_path)
            return output_path
        except Exception as exc:
            logger.warning("Preview optimisation failed, saving raw: %s", exc)
@@ -224,7 +223,6 @@ async def download_preview(
    try:
        ok, _ = await downloader.download_file(url, output_path, use_auth=False)
        if ok:
-            logger.info("Preview downloaded (fallback) for %s", model_path)
            return output_path
    except Exception as exc:
        logger.warning("Preview fallback download failed for %s: %s", model_path, exc)
--- a/py/metadata_ops/main.py
+++ b/py/metadata_ops/main.py
@@ -1,17 +1,12 @@
-"""Subprocess entry point for AgentCLI (debugging / external use).
+"""Subprocess entry point for ``metadata_ops`` (debugging / external use).

 Usage::

-    python -m py.agent_cli base-models list [--limit N]
-    python -m py.agent_cli metadata read <path>
-    python -m py.agent_cli metadata update <path> --json '{...}'
-    python -m py.agent_cli preview download <path> --url <url>
-    python -m py.agent_cli cache refresh <path>
-
-NOTE: This is an **optional** convenience wrapper.  The primary consumer of
-AgentCLI is the :mod:`AgentService` (in-process).  This entry point exists
-for manual debugging and future integration with subprocess-based agent
-frameworks.
+    python -m py.metadata_ops base-models list [--limit N]
+    python -m py.metadata_ops metadata read <path>
+    python -m py.metadata_ops metadata update <path> --json '{...}'
+    python -m py.metadata_ops preview download <path> --url <url>
+    python -m py.metadata_ops cache refresh <path>
 """

 from __future__ import annotations
--- a/py/routes/handlers/agent_handlers.py
+++ b/py/routes/handlers/agent_handlers.py
@@ -99,12 +99,11 @@ class AgentHandler:
        # Launch execution in the background
        progress_reporter = AgentProgressReporter()
        logger.info(
-            "LLM enrichment '%s' starting for %d model(s) in background task",
+            "LLM enrichment '%s' starting for %d model(s)",
            skill_name, len(model_paths),
        )

        async def _run() -> None:
-            logger.info("Background task started for enrichment '%s'", skill_name)
            try:
                result = await service.execute_skill(
                    skill_name=skill_name,
@@ -137,8 +136,7 @@ class AgentHandler:
                )

        # Fire and forget — progress comes via WebSocket
-        task = asyncio.create_task(_run())
-        logger.info("LLM enrichment '%s' background task created (id=%s)", skill_name, task)
+        asyncio.create_task(_run())

        return web.json_response(
            {
--- a/py/services/agent/init.py
+++ b/py/services/agent/init.py
@@ -1,8 +1,12 @@
-"""Agent-powered skill system for LoRA Manager.
+"""LLM-powered metadata enrichment pipeline infrastructure.

-This package provides the orchestration layer for LLM/agent-powered features.
+This package provides the orchestration layer for LLM-powered features.
 Skills define *what* to do (prompt template).  The :class:`AgentService`
 handles *how* (LLM calls, context gathering, validation, progress).
+
+NOTE: The current implementation is a code-driven pipeline, not a true
+agent loop.  Future agent orchestration (LLM-driven tool selection) will
+live alongside this package with its own namespace.
 """

 from __future__ import annotations
--- a/py/services/agent/agent_service.py
+++ b/py/services/agent/agent_service.py
@@ -1,16 +1,17 @@
-"""Agent orchestration service.
+"""Pipeline orchestration service.

-The :class:`AgentService` coordinates skill execution:
+The :class:`AgentService` coordinates LLM-powered pipeline execution:

-1. Look up the skill in :class:`SkillRegistry`
-2. Validate input against the skill's ``input_schema``
-3. Prepare context via :mod:`~py.agent_cli` (read metadata, list base models, fetch HF README)
+1. Look up the pipeline definition in :class:`SkillRegistry`
+2. Validate input against its ``input_schema``
+3. Prepare context via :mod:`~py.metadata_ops` (read metadata, list base models, fetch HF README)
 4. If ``llm_required``: call :class:`LLMService` with the rendered prompt
-5. Post-process via :class:`PostProcessor` (delegates I/O to :mod:`~py.agent_cli`)
+5. Post-process via :class:`PostProcessor` (delegates I/O to :mod:`~py.metadata_ops`)
 6. Broadcast progress and completion via :class:`WebSocketManager`

-Skills define *what* to do (prompt template).  The AgentService handles *how*
-(LLM calls, context gathering, validation, progress).
+Pipeline definitions (*skills*) describe *what* to do (prompt template).
+The AgentService handles *how* (LLM calls, context gathering, validation,
+progress).
 """

 from __future__ import annotations
@@ -202,11 +203,11 @@ class AgentService:
        input_data: Dict[str, Any],
        progress_callback: Optional[AgentProgressReporter] = None,
    ) -> SkillResult:
-        """Execute an agent skill.
+        """Execute a pipeline (skill) on the given models.

        Args:
-            skill_name: Name of the skill to execute
-            input_data: Input validated against the skill's ``input_schema``
+            skill_name: Name of the pipeline to execute
+            input_data: Input validated against the pipeline's ``input_schema``
            progress_callback: Optional WebSocket progress reporter

        Returns:
@@ -214,7 +215,6 @@ class AgentService:
        """

        registry = await self._ensure_registry()
-        logger.info("execute_skill '%s': looking up skill", skill_name)
        skill = registry.get_skill(skill_name)
        if skill is None:
            return SkillResult(
@@ -246,7 +246,6 @@ class AgentService:
        errors: List[str] = []
        post_processor = PostProcessor()

-        logger.info("execute_skill '%s': starting with %d model(s)", skill_name, total)
        await self._emit_progress(
            progress_callback, skill_name, status="started",
            total=total, processed=0, success=0,
@@ -256,13 +255,14 @@ class AgentService:
        llm_configured = llm.is_configured() if skill.llm_required else True

        for model_path in model_paths:
+            model_filename = os.path.basename(model_path)
            logger.info(
-                "execute_skill '%s': processing model %d/%d: %s",
-                skill_name, processed + 1, total, model_path,
+                "[%s] [%d/%d] %s",
+                skill_name, processed + 1, total, model_filename,
            )
            updated_data: Dict[str, Any] = {}
            try:
-                from ...agent_cli import read_metadata
+                from ...metadata_ops import read_metadata
                metadata = await read_metadata(model_path)

                prompt_vars: Dict[str, Any] = {"model_path": model_path}
@@ -275,10 +275,6 @@ class AgentService:
                if skill.llm_required and llm_configured:
                    prompt_template = registry.load_prompt(skill_name)
                    rendered = _render_prompt(prompt_template, prompt_vars)
-                    logger.info(
-                        "execute_skill '%s': LLM call for %s (prompt=%d chars)",
-                        skill_name, model_path, len(rendered),
-                    )
                    llm_response = await llm.chat_completion_json(
                        system_prompt=prompt_vars.get(
                            "system_prompt",
@@ -286,6 +282,13 @@ class AgentService:
                        ),
                        user_prompt=rendered,
                    )
+                    if llm_response:
+                        logger.info(
+                            "[%s] [%d/%d] %s → base_model=%s confidence=%s",
+                            skill_name, processed + 1, total, model_filename,
+                            (llm_response.get("base_model") or "?")[:50],
+                            llm_response.get("confidence", "?"),
+                        )

                model_result = await post_processor.process(
                    skill_name=skill_name,
@@ -329,7 +332,6 @@ class AgentService:
            summary=f"Processed {processed}/{total} models, {success_count} succeeded",
        )

-        logger.info("execute_skill '%s': done — %s", skill_name, result.summary)
        await self._emit_progress(
            progress_callback, skill_name, status="completed",
            total=total, processed=processed, success=success_count,
@@ -366,7 +368,7 @@ class AgentService:
        base models, loads user priority tags, and returns a dict that maps to
        ``{{variable}}`` placeholders in ``prompt.md``.
        """
-        from ...agent_cli import identify_model_type, list_base_models
+        from ...metadata_ops import identify_model_type, list_base_models
        from ..settings_manager import SettingsManager

        context: Dict[str, Any] = {
@@ -411,10 +413,6 @@ class AgentService:
                cleaned = clean_readme_for_llm(readme) if readme else ""
            context["readme_content"] = cleaned if cleaned else "(README not available)"
            context["readme_content_full"] = readme or ""
-            logger.info(
-                "Cleaned README for %s (%d chars): ---BEGIN---\n%s\n---END---",
-                repo, len(cleaned), cleaned[:800] if cleaned else "(empty)",
-            )

        try:
            raw_models = await list_base_models()
--- a/py/services/agent/post_processor.py
+++ b/py/services/agent/post_processor.py
@@ -1,11 +1,11 @@
 """Post-processing engine for skill pipeline outputs.

 The :class:`PostProcessor` takes the LLM's structured JSON output and applies
-it to a model's on-disk metadata via the :mod:`~py.agent_cli` functions.
+it to a model's on-disk metadata via the :mod:`~py.metadata_ops` functions.

 It handles all the skill-specific business logic — conditions, transformations,
 and orchestration of multiple side-effects (write metadata, download preview,
-refresh cache).  All actual I/O is delegated to :mod:`~py.agent_cli`.
+refresh cache).  All actual I/O is delegated to :mod:`~py.metadata_ops`.
 """

 from __future__ import annotations
@@ -30,7 +30,7 @@ class PostProcessor:
            skill_name="enrich_hf_metadata",
            model_path="/path/to/model.safetensors",
            llm_output={...},
-            metadata={...},    # from agent_cli.read_metadata()
+            metadata={...},    # from metadata_ops.read_metadata()
        )
    """

@@ -73,7 +73,7 @@ class PostProcessor:
        metadata: Dict[str, Any],
        readme_content: str = "",
    ) -> Dict[str, Any]:
-        from ...agent_cli import (
+        from ...metadata_ops import (
            apply_metadata_updates,
            download_preview,
            refresh_cache,
--- a/py/services/llm_service.py
+++ b/py/services/llm_service.py
@@ -521,15 +521,9 @@ class LLMService:

        try:
            parsed = json.loads(result["content"])
-            logger.info(
-                "LLM response base_model=%s tags=%s confidence=%s",
-                parsed.get("base_model", "?")[:50],
-                parsed.get("tags", []),
-                parsed.get("confidence", "?"),
-            )
-            logger.info(
+            logger.debug(
                "LLM raw content: %s",
-                (result.get("content") or "")[:1200],
+                json.dumps(parsed, ensure_ascii=False)[:2000],
            )
            return parsed
        except (json.JSONDecodeError, TypeError) as exc:
--- a/tests/enrich_hf_validation/config.py
+++ b/tests/enrich_hf_validation/config.py
@@ -72,7 +72,7 @@ _FALLBACK_BASE_MODELS: List[str] = [
 async def init_supported_base_models() -> None:
    """Populate ``SUPPORTED_BASE_MODELS`` from the production codebase.

-    Calls ``py.agent_cli.list_base_models()`` which merges a hardcoded
+    Calls ``py.metadata_ops.list_base_models()`` which merges a hardcoded
    fallback with models fetched from the CivitAI API.  When the call
    fails (e.g. offline, API error), falls back to ``_FALLBACK_BASE_MODELS``.

@@ -80,7 +80,7 @@ async def init_supported_base_models() -> None:
    ``run_validation.main()``, not at module level).
    """
    try:
-        from py.agent_cli import list_base_models
+        from py.metadata_ops import list_base_models

        models = await list_base_models()
        if models:
--- a/tests/metadata_ops/init.py
+++ b/tests/metadata_ops/init.py
--- a/tests/metadata_ops/test_metadata_ops.py
+++ b/tests/metadata_ops/test_metadata_ops.py
@@ -1,7 +1,7 @@
-"""Tests for the AgentCLI module (py/agent_cli/).
+"""Tests for the metadata_ops module (py/metadata_ops/).

 All tests mock the underlying services (scanner, MetadataManager, downloader)
-since the AgentCLI is a thin delegation layer.
+since it is a thin delegation layer.

 Mock targets must match where imports are resolved inside each function
 (lazy imports via ``from X import Y`` inside function body).
@@ -13,7 +13,7 @@ from unittest import mock

 import pytest

-from py.agent_cli import (
+from py.metadata_ops import (
    list_base_models,
    read_metadata,
    apply_metadata_updates,
@@ -160,7 +160,7 @@ class TestApplyMetadataUpdates:
    @pytest.mark.asyncio
    async def test_updates_field(self):
        with (
-            mock.patch("py.agent_cli.read_metadata") as mock_read,
+            mock.patch("py.metadata_ops.read_metadata") as mock_read,
            mock.patch("py.utils.metadata_manager.MetadataManager") as mm,
        ):
            mock_read.return_value = {"base_model": "", "tags": []}
@@ -176,7 +176,7 @@ class TestApplyMetadataUpdates:
    @pytest.mark.asyncio
    async def test_noop_when_value_unchanged(self):
        with (
-            mock.patch("py.agent_cli.read_metadata") as mock_read,
+            mock.patch("py.metadata_ops.read_metadata") as mock_read,
            mock.patch("py.utils.metadata_manager.MetadataManager") as mm,
        ):
            mock_read.return_value = {"base_model": "Flux.1 D"}
@@ -189,7 +189,7 @@ class TestApplyMetadataUpdates:
    @pytest.mark.asyncio
    async def test_multiple_fields(self):
        with (
-            mock.patch("py.agent_cli.read_metadata") as mock_read,
+            mock.patch("py.metadata_ops.read_metadata") as mock_read,
            mock.patch("py.utils.metadata_manager.MetadataManager") as mm,
        ):
            mm.save_metadata = mock.AsyncMock(return_value=True)
@@ -207,7 +207,7 @@ class TestApplyMetadataUpdates:
    @pytest.mark.asyncio
    async def test_empty_updates_noop(self):
        with (
-            mock.patch("py.agent_cli.read_metadata"),
+            mock.patch("py.metadata_ops.read_metadata"),
            mock.patch("py.utils.metadata_manager.MetadataManager") as mm,
        ):
            updated = await apply_metadata_updates("/p.safetensors", {})
@@ -277,7 +277,7 @@ class TestRefreshCache:
                get_checkpoint_scanner=mock.AsyncMock(return_value=None),
                get_embedding_scanner=mock.AsyncMock(return_value=None),
            ),
-            mock.patch("py.agent_cli.read_metadata") as mock_read,
+            mock.patch("py.metadata_ops.read_metadata") as mock_read,
        ):
            mock_read.return_value = {"base_model": "SDXL 1.0"}
            result = await refresh_cache("/some/path.safetensors")
@@ -306,7 +306,7 @@ class TestRefreshCache:
                get_checkpoint_scanner=mock.AsyncMock(return_value=None),
                get_embedding_scanner=mock.AsyncMock(return_value=None),
            ),
-            mock.patch("py.agent_cli.read_metadata") as mock_read,
+            mock.patch("py.metadata_ops.read_metadata") as mock_read,
        ):
            mock_read.return_value = {}
            result = await refresh_cache("/some/path.safetensors")
--- a/tests/metadata_ops/test_readme_processor.py
+++ b/tests/metadata_ops/test_readme_processor.py
--- a/tests/services/test_post_processor.py
+++ b/tests/services/test_post_processor.py
@@ -39,9 +39,9 @@ class TestProcessDispatch:
    @pytest.mark.asyncio
    async def test_enrich_hf_metadata_routes_correctly(self, processor):
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview") as mock_dl,
-            mock.patch("py.agent_cli.refresh_cache") as mock_ref,
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview") as mock_dl,
+            mock.patch("py.metadata_ops.refresh_cache") as mock_ref,
        ):
            mock_apply.return_value = ["metadata_source"]
            mock_dl.return_value = None
@@ -82,9 +82,9 @@ class TestEnrichHfMetadata:
        """Empty current base_model → new value is applied."""
        llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -100,9 +100,9 @@ class TestEnrichHfMetadata:
        """Existing base_model from CivitAI → not overwritten."""
        llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -119,9 +119,9 @@ class TestEnrichHfMetadata:
        """Existing base_model from HF → overwritten (LLM is more reliable)."""
        llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -136,9 +136,9 @@ class TestEnrichHfMetadata:
    async def test_base_model_skipped_when_llm_empty(self, processor):
        """LLM returns empty base_model → nothing written."""
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -156,9 +156,9 @@ class TestEnrichHfMetadata:
        """New trigger words written when current list is empty."""
        llm = {**self.MIN_LLM_OUTPUT, "trigger_words": ["trigger1", "trigger2"]}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=None),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -176,9 +176,9 @@ class TestEnrichHfMetadata:
        """short_description written to civitai.description for HF models."""
        llm = {**self.MIN_LLM_OUTPUT, "short_description": "A short summary"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=None),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -194,9 +194,9 @@ class TestEnrichHfMetadata:
        """short_description NOT written for CivitAI models (has own description)."""
        llm = {**self.MIN_LLM_OUTPUT, "short_description": "A short summary"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=None),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -213,9 +213,9 @@ class TestEnrichHfMetadata:
    async def test_readme_content_converted_to_model_description(self, processor):
        """Raw README converted to HTML and stored as modelDescription."""
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=None),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -232,9 +232,9 @@ class TestEnrichHfMetadata:
    async def test_readme_content_skipped_for_civitai_model(self, processor):
        """README content NOT converted for CivitAI models."""
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=None),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -260,9 +260,9 @@ widget:
 Content
 """
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=None),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -286,9 +286,9 @@ Content
    async def test_gallery_images_skipped_for_civitai_model(self, processor):
        """Gallery images NOT extracted for CivitAI models."""
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=None),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=None),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -310,9 +310,9 @@ Content
    async def test_tags_merged_and_deduplicated(self, processor):
        llm = {**self.MIN_LLM_OUTPUT, "tags": ["flux", "lora", "STYLE"]}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -332,9 +332,9 @@ Content
    @pytest.mark.asyncio
    async def test_audit_fields_always_set(self, processor):
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -352,9 +352,9 @@ Content
    async def test_preview_downloaded_when_url_provided(self, processor):
        llm = {**self.MIN_LLM_OUTPUT, "preview_url": "https://ex.com/img.png"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates") as mock_apply,
-            mock.patch("py.agent_cli.download_preview") as mock_dl,
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates") as mock_apply,
+            mock.patch("py.metadata_ops.download_preview") as mock_dl,
+            mock.patch("py.metadata_ops.refresh_cache"),
        ):
            mock_dl.return_value = "/p.webp"
            result = await processor.process(
@@ -373,9 +373,9 @@ Content
        """If current_preview file exists on disk, skip download."""
        llm = {**self.MIN_LLM_OUTPUT, "preview_url": "https://ex.com/img.png"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates"),
-            mock.patch("py.agent_cli.download_preview") as mock_dl,
-            mock.patch("py.agent_cli.refresh_cache"),
+            mock.patch("py.metadata_ops.apply_metadata_updates"),
+            mock.patch("py.metadata_ops.download_preview") as mock_dl,
+            mock.patch("py.metadata_ops.refresh_cache"),
            mock.patch("os.path.exists", return_value=True),
        ):
            await processor.process(
@@ -392,9 +392,9 @@ Content
    async def test_cache_refreshed_when_updates_applied(self, processor):
        llm = {**self.MIN_LLM_OUTPUT, "base_model": "Flux.1 D"}
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates", return_value=["base_model"]),
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache") as mock_ref,
+            mock.patch("py.metadata_ops.apply_metadata_updates", return_value=["base_model"]),
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache") as mock_ref,
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",
@@ -407,9 +407,9 @@ Content
    @pytest.mark.asyncio
    async def test_cache_not_refreshed_when_nothing_changed(self, processor):
        with (
-            mock.patch("py.agent_cli.apply_metadata_updates", return_value=[]),
-            mock.patch("py.agent_cli.download_preview", return_value=False),
-            mock.patch("py.agent_cli.refresh_cache") as mock_ref,
+            mock.patch("py.metadata_ops.apply_metadata_updates", return_value=[]),
+            mock.patch("py.metadata_ops.download_preview", return_value=False),
+            mock.patch("py.metadata_ops.refresh_cache") as mock_ref,
        ):
            await processor.process(
                skill_name="enrich_hf_metadata",