Update README with clearer instructions

- Updated the template spreadsheet link.
- Changed steps for rule publishing and automation creation.
- Improved formatting of supported predicates table.
- Added a new support contact method via community forum.

.github/workflows/main.yml

@@ -44,4 +44,3 @@ jobs:
           speckle_function_id: ${{ secrets.SPECKLE_FUNCTION_ID }}
           speckle_function_input_schema_file_path: ${{ env.FUNCTION_SCHEMA_FILE_NAME }}
           speckle_function_command: 'python -u main.py run'
-          speckle_function_recommended_memory_mi: 5000

.idea/misc.xml

@@ -4,7 +4,4 @@
     <option name="sdkName" value="WSL Checker" />
   </component>
   <component name="ProjectRootManager" version="2" project-jdk-name="uv (Checker)" project-jdk-type="Python SDK" />
-  <component name="PythonCompatibilityInspectionAdvertiser">
-    <option name="version" value="3" />
-  </component>
 </project>

README.md

@@ -1,13 +1,14 @@
-# Model Checker
+# Speckle Checker
 
-Model Checker is an Automate function that validates Speckle objects against configurable rules. This approach provides
-a flexible way to implement quality checks and maintain consistent standards across projects.
+Speckle Checker is an Automate function that validates Speckle objects against configurable rules defined in a
+spreadsheet. This approach provides a flexible way to implement quality checks without coding, making it accessible to
+all team members.
 
 ## Overview
 
-The Model Checker allows you to:
+The Checker function allows you to:
 
-- Define validation rules for your objects
+- Define validation rules in a spreadsheet
 - Configure severity levels for issues
 - Check properties across different types of objects
 - Generate reports of validation results
@@ -15,35 +16,39 @@ The Model Checker allows you to:
 
 ## Getting Started
 
-### 1. Access the Model Checker Application
+### 1. Prepare Your Rule Spreadsheet
 
-1. Go to the [Model Checker Application](https://model-checker.speckle.systems)
-2. Sign in with your Speckle account
-3. Create and manage your validation rules through the intuitive web interface
+1. Access
+   the [template spreadsheet](https://docs.google.com/spreadsheets/d/1eB0RVuOXdjLyn4_GAPSahV05p1lqfSGQbH8WWijnkkA/edit?gid=0#gid=0)
+2. Use the Speckle menu to launch the Speckle sidebar and make a copy.
+3. Define your rules using the format explained below
+4. Publish your rules by clicking "Publish Rules". Copy the resultant URL.
 
 ### 2. Create an Automation
 
 1. Go to your workspace project in [Speckle](https://app.speckle.systems/)
 2. Create a new Automation
-3. Select the Model Checker function
+3. Select the Checker function
 4. Configure the function:
+    - Paste your published rules URL
     - Set minimum severity level to report
     - Configure other options as needed
 5. Save and run your automation
 
 ## Rule Definition Format
 
-Rules are defined with the following components:
+Rules are defined in a spreadsheet with the following columns:
 
-| Logic | Property Name | Predicate    | Value     | Message              | Report Severity |
-|-------|---------------|--------------|-----------|----------------------|-----------------|
-| WHERE | category      | matches      | Walls     | Wall thickness check | ERROR           |
-| CHECK | Width         | greater than | 200       |                      |                 |
-| WHERE | category      | matches      | Columns   | Column height check  | WARNING         |
-| AND   | height        | in range     | 2500,4000 |                      |                 |
+| Rule Number | Logic | Property Name | Predicate    | Value     | Message              | Report Severity |
+|-------------|-------|---------------|--------------|-----------|----------------------|-----------------|
+| 1           | WHERE | category      | matches      | Walls     | Wall thickness check | ERROR           |
+| 1           | AND   | Width         | greater than | 200       |                      |                 |
+| 2           | WHERE | category      | matches      | Columns   | Column height check  | WARNING         |
+| 2           | AND   | height        | in range     | 2500,4000 |                      |                 |
 
-### Component Explanation
+### Column Explanation
 
+- **Rule Number**: Groups conditions that belong to the same rule
 - **Logic**: Defines how conditions are combined (WHERE, AND, CHECK)
 - **Property Name**: The object property or parameter to check
 - **Predicate**: Comparison operation (equals, greater than, etc.)
@@ -79,7 +84,7 @@ are reported as issues.
 
 ## Working with Object Properties
 
-The Model Checker understands properties in Speckle objects regardless of schema:
+The Checker understands properties in Speckle objects regardless of schema:
 
 - Direct properties: `category`, `name`, `id`
 - Nested properties: `parameters.WIDTH.value`
@@ -90,41 +95,27 @@ The Model Checker understands properties in Speckle objects regardless of schema
 ### Wall Thickness Check
 
 ```
-Rule: WHERE category equals "Walls" AND width less than "200"
-Message: "Walls must have width of at least 200."
+Rule 1: WHERE category equals "Walls" AND width less than "200"
+Message: "Wall too thin - minimum thickness is 200mm"
 Severity: ERROR
 ```
 
 ### Door Naming Convention
 
 ```
-Rule: WHERE category equals "Doors" AND name is not like "^D\d{3}$"
-Message: "All doors must have a name that follows the format "D" followed by three digits."
+Rule 2: WHERE category equals "Doors" AND name is not like "^D\d{3}$"
+Message: "Door name must follow pattern D followed by 3 digits"
 Severity: WARNING
 ```
 
 ### Structural Column Height Range
 
 ```
-Rule: WHERE category equals "Columns" AND is_structural is true AND height not in range "2400,4000"
-Message: "Structural columns must have a height between 2400 and 4000."
+Rule 3: WHERE category equals "Columns" AND is_structural is true AND height not in range "2400,4000"
+Message: "Structural column height outside acceptable range (2400-4000mm)"
 Severity: ERROR
 ```
 
 ## Support
 
 For issues or questions, please let us know on the [Speckle Community Forum](https://speckle.community/).
-
-### Alternative: TSV File Format
-
-While the Model Checker Application is the recommended way to create and manage rules, you can also create compatible
-TSV (Tab-Separated Values) files manually. This can be useful for:
-
-- Programmatically generating rules
-- Version controlling rules in a text format
-- Integrating with existing workflows
-- Creating rules in bulk
-
-The TSV file should follow the same structure as shown in the table above, with columns separated by tabs. The file will
-then need to be hosted somewhere and served with MIME-type of `text/tab-separated-values` and the URL used in the
-automation configuration.

src/predicates.py

@@ -17,4 +17,5 @@ PREDICATE_METHOD_MAP = {
     "identical to": PropertyRules.is_identical_value.__name__,
     "contains": PropertyRules.is_parameter_value_containing.__name__,
     "does not contain": PropertyRules.is_parameter_value_not_containing.__name__,
+    "is set": PropertyRules.is_parameter_value_not_empty.__name__,
 }

src/rule_processor.py

@@ -95,7 +95,7 @@ def evaluate_condition(
     Returns:
         True if the condition is met, False otherwise
     """
-    property_name = condition.get("Property Name", condition.get("Property Path"))
+    property_name = condition["Property Name"]
     predicate_key = condition["Predicate"]
     value = condition["Value"]
 
@@ -263,27 +263,21 @@ def apply_rules_to_objects(
         rules_processed += 1
 
         # Ensure rule_group has necessary columns
-        if "Message" not in rule_group.columns or (
-            "Report Severity" not in rule_group.columns and "Severity" not in rule_group.columns
-        ):
+        if "Message" not in rule_group.columns or "Report Severity" not in rule_group.columns:
             continue  # Or raise an exception if these columns are mandatory
 
+        pass_objects, fail_objects = process_rule(speckle_objects, rule_group)
+
         # Get the severity level for this rule
         rule_severity = get_severity(rule_group.iloc[-1])
         rule_severity_level = severity_levels[MinimumSeverity(rule_severity.value)]
 
-        # Check if the rule severity level meets the minimum severity level - no point in processing lower severity rules
-        if rule_severity_level < min_severity_level:
-            continue
-
-        pass_objects, fail_objects = process_rule(speckle_objects, rule_group)
-
         # For passing objects, only attach if we're showing all levels (INFO)
         if minimum_severity == MinimumSeverity.INFO:
             attach_results(pass_objects, rule_group.iloc[-1], rule_id_str, automate_context, True)
 
         # For failing objects, attach if they meet minimum severity threshold
-        if len(fail_objects) and rule_severity_level >= min_severity_level:
+        if rule_severity_level >= min_severity_level:
             attach_results(fail_objects, rule_group.iloc[-1], rule_id_str, automate_context, False)
 
         if len(pass_objects) == 0 and len(fail_objects) == 0 and not hide_skipped:
@@ -329,7 +323,7 @@ def get_severity(rule_info: pd.Series) -> SeverityLevel:
     Returns:
         Appropriate SeverityLevel enum value
     """
-    severity = rule_info.get("Report Severity") or rule_info.get("Severity")  # Extract severity from input data
+    severity = rule_info.get("Report Severity")  # Extract severity from input data
 
     # If severity is None or not a string (e.g., numeric input), default to ERROR
     if not isinstance(severity, str):

src/rules.py

@@ -890,3 +890,35 @@ class PropertyRules:
         return PropertyRules.compare_values(
             parameter_value, value_to_match, case_sensitive=True, tolerance=0, allow_yes_no_bools=False, use_exact=True
         )
+
+    @staticmethod
+    def is_parameter_value_not_empty(speckle_object: Base, parameter_name: str) -> bool:
+        """Checks if parameter exists and has a non-empty value.
+
+        Args:
+            speckle_object: The Speckle object to check
+            parameter_name: Name of the parameter to check
+
+        Returns:
+            bool: True if parameter exists and has a non-empty value
+        """
+        # Get the parameter value
+        parameter_value = PropertyRules.get_parameter_value(speckle_object, parameter_name)
+
+        # Check if parameter exists
+        if parameter_value is None:
+            return False
+
+        # Check if value is an empty string
+        if isinstance(parameter_value, str) and parameter_value.strip() == "":
+            return False
+
+        # Check if value is an empty list or collection
+        if hasattr(parameter_value, "__len__") and len(parameter_value) == 0:
+            return False
+
+        # Additional check for "None" string which can sometimes appear in exports
+        if isinstance(parameter_value, str) and parameter_value.lower() == "none":
+            return False
+
+        return True

src/spreadsheet.py

@@ -64,10 +64,8 @@ def process_rule_numbers(df: DataFrame) -> DataFrame:
         # Get slice of rows for this group
         group_slice = df.iloc[start_idx:end_idx]
 
-        # Try to get rule number from first row, fall back to "Rule #"
-        group_rule_num = (
-            group_slice["Rule Number"].iloc[0] if not pd.isna(group_slice["Rule Number"].iloc[0]) else "Rule #"
-        )
+        # Try to get rule number from first row
+        group_rule_num = group_slice["Rule Number"].iloc[0]
 
         if pd.isna(group_rule_num):
             # If no rule number, generate next available number
@@ -92,7 +90,8 @@ def process_rule_numbers(df: DataFrame) -> DataFrame:
 
 
 def validate_rule_numbers(df: DataFrame) -> list[str]:
-    """Validate rule numbers and return any warnings or errors.
+    """ "
+    Validate rule numbers and return any warnings or errors.
 
     This checks for issues like:
     1. Missing rule numbers
@@ -129,7 +128,8 @@ def validate_rule_numbers(df: DataFrame) -> list[str]:
 
 
 def read_rules_from_spreadsheet(url: str) -> tuple[DataFrameGroupBy, list[str]] | tuple[None, list[str]]:
-    """Reads rules from a TSV file at the provided URL, processes them, and returns grouped rules.
+    """ "
+    Reads rules from a TSV file at the provided URL, processes them, and returns grouped rules.
 
     This function is the main entry point for rule loading:
     1. Reads the TSV file from the provided URL

tests/test_function.py

@@ -30,14 +30,12 @@ class TestFunction:
 
         """Run an integration test for the automate function."""
         automation_context = AutomationContext.initialize(test_automation_run_data, test_automation_token)
-        default_url: str = (
-            "https://speckle-model-checker-cedxvz7lzq-ew.a.run.app/r/6hdycwPELyTIT7Ueedh0UsWdJlTBefwSjDlcnd8LXGg/tsv"
-        )
+        default_url: str = "https://drive.google.com/uc?export=download&id=1hiPSw23eOaqd27QD_YsXvZg9PWm7_XBx"
 
         automate_sdk = run_function(
             automation_context,
             automate_function,
-            FunctionInputs(spreadsheet_url=default_url, minimum_severity=MinimumSeverity.INFO, hide_skipped=True),
+            FunctionInputs(spreadsheet_url=default_url, minimum_severity=MinimumSeverity.WARNING, hide_skipped=True),
         )
 
         assert automate_sdk.run_status == AutomationStatus.SUCCEEDED

tests/test_parameters.py

@@ -415,3 +415,20 @@ class TestParameterHandling:
         """Test negative substring matching on parameter values."""
         v2_obj, _ = test_objects
         assert PropertyRules.is_parameter_value_not_containing(v2_obj, param_name, substring) == expected_result
+
+    @pytest.mark.parametrize(
+        "param_name, expected_result",
+        [
+            ("category", True),  # Parameter exists with non-empty value
+            ("family", True),  # Parameter exists with non-empty value
+            ("non_existent_param", False),  # Parameter doesn't exist
+            # The following would require setup with empty values
+            # ("empty_string_param", False),  # Parameter exists but has empty string value
+            # ("none_string_param", False),  # Parameter exists but has "None" string value
+        ],
+    )
+    def test_parameter_not_empty(self, test_objects, param_name, expected_result):
+        """Test 'not empty' check on parameter values."""
+        v2_obj, _ = test_objects
+
+        assert PropertyRules.is_parameter_value_not_empty(v2_obj, param_name) == expected_result