input-file-skill

# DATA File Processing Skill This skill provides tools for working with simulator input files. The reference format uses .DATA as the primary input extension; other simulators use different extensions. ## Overview The DATA File Processing skill enables agents to: - **Parse** simulator input files to understand their structure and sections - **Modify** simulator input files using natural language instructions - **Validate** simulator input files for syntax and consistency - **Patch** specific keyword blocks without rewriting entire files ## Tools ### parse_simulation_input_file Parses a simulator input file and returns its structure (sections found). Reference format uses .DATA extension. **Usage:** ```python parse_simulation_input_file(file_path: str) -> str ``` **Example:** ``` parse_simulation_input_file("data/example_cases/SPE10/CASE/SPE10_TOPLAYER.DATA") ``` **Returns:** A string listing all sections found (e.g., "Sections found: HEADER, RUNSPEC, GRID, PROPS, SOLUTION, SUMMARY, SCHEDULE") **When to use:** First step in scenario testing chain (TOOL_DECISION_TREE.md Section 2.4) or when user asks to parse/list sections of a simulator input file. ### modify_simulation_input_file Modifies a simulator input file based on natural language instructions. Can modify entire file or target specific keyword blocks. **Usage:** ```python modify_simulation_input_file( file_path: str, modifications: str, output_path: Optional[str] = None, llm_model: Optional[str] = None, manual_context: Optional[str] = None, example_context: Optional[str] = None, target_keyword: Optional[str] = None ) -> str ``` **Parameters:** - `file_path`: Path to the simulator input file to modify - `modifications`: Natural language description of changes (e.g., "Increase water injection rate for well I1 to 55") - `output_path`: Where to save modified file (default: overwrite original) - `llm_model`: Optional model override for LLM-based modifications - `manual_context`: Optional simulator manual excerpt for the relevant keyword - `example_context`: Optional example DATA snippets for the relevant keyword - `target_keyword`: If set, only this keyword block is edited; rest of file is copied unchanged **Example:** ``` modify_simulation_input_file( file_path="SPE1CASE1.DATA", modifications="Increase water injection rate for well I1 in WCONINJE to 55", output_path="SPE1CASE1_AGENT_GENERATED.DATA", target_keyword="WCONINJE" ) ``` **When to use:** In scenario test chain (Section 2.4) after parse_simulation_input_file → simulator_manual → simulator_examples, or when user requests modifications to a simulator input file. ### patch_simulation_input_keyword Patches a specific keyword block in a simulator input file without modifying other parts. Useful for auto-fixes. **Usage:** ```python patch_simulation_input_keyword( file_path: str, keyword: str, output_path: str, item_index: Optional[int] = None, new_value: Optional[str] = None, new_block_content: Optional[str] = None ) -> str ``` **Parameters:** - `file_path`: Path to the simulator input file - `keyword`: Keyword to patch (e.g., WELLDIMS, TABDIMS) - `output_path`: Path for the new file (original is unchanged) - `item_index`: 1-based index of the numeric item to replace - `new_value`: New value for the item (used with item_index) - `new_block_content`: Exact new block content (keyword line + data). If set, item_index/new_value are ignored. **Example:** ``` patch_simulation_input_keyword( file_path="SPE1CASE1.DATA", keyword="WELLDIMS", output_path="SPE1CASE1_FIXED.DATA", item_index=1, new_value="10" ) ``` **When to use:** In HITL apply fix flow (Section 2.2) for auto-fixes, or when only a specific keyword needs to be changed. ## Workflow Integration This skill integrates with the Simulator Agent's decision tree (TOOL_DECISION_TREE.md): 1. **Scenario Test Chain (Section 2.4):** ``` parse_simulation_input_file → simulator_manual → simulator_examples → modify_simulation_input_file → run_and_heal ``` 2. **HITL Apply Fix (Section 2.2):** ``` patch_simulation_input_keyword or modify_simulation_input_file → run_and_heal ``` ## Implementation Details Tools are implemented as LangChain tools with Pydantic input schemas. The skill uses: - **utils.py**: Shared utility functions for parsing, keyword finding, and LLM integration - **Tool modules**: Individual tool implementations in separate modules - **LLM integration**: Uses ChatOpenAI for natural language modifications ## Error Handling All tools return descriptive error messages if: - Files are not found - Syntax errors occur during parsing - LLM modifications fail - Validation checks fail ## References - [OPM Flow Manual](references/OPM_MANUAL.md) - Detailed keyword reference - [Tool Decision Tree](references/TOOL_DECISION_TREE.md) - Routing logic - [Examples](references/EXAMPLES.md) - Usage examples ## Testing Run the test suite: ```bash python -m simulator_agent.skills.input_file_skill.test --file path/to/test.DATA ``` Or test individual tools: ```bash python -m simulator_agent.skills.input_file_skill.test --file path/to/test.DATA --tool parse_simulation_input_file ``` The test script is located in `test.py` at the root of the skill directory.