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+# AI Agent Collaboration Guide
+
+This document contains key context, nuances, and troubleshooting tips specifically designed to help AI agents (and human developers) quickly understand and contribute to this repository, avoiding common pitfalls.
+
+## 1. Project Dependencies and Environment
+- **Environment**: This project primarily uses [uv](https://docs.astral.sh/uv/) for dependency management and locking.
+- **External Schemas**: Many UTM standards implementations (such as the ASTM F3548 schemas) are provided by the external `uas_standards` package instead of living directly within the local `./monitoring` or `./schemas` directories.
+- **Agent Tip**: Since external packages reside in the virtual environment, you cannot explore them directly with standard local file `grep` searches. To inspect an external schema's properties (e.g., `OperationalIntentReference`), it is much faster to run a Python introspection script through `uv`:
+  ```bash
+  uv run python -c "from uas_standards.astm.f3548.v21.api import OperationalIntentReference; print(OperationalIntentReference.__annotations__)"
+  ```
+
+## 2. Navigating Data Schemas
+- **Implicit Types**: Many schema objects inherit from `ImplicitDict`. This means that reading their raw Python class definitions may not reveal all their expected structure. Rely on their `__annotations__` or their OpenAPI documentation.
+- **Hidden Schema Nuances**: Certain F3548 properties intuitively behave differently than standard logic might predict (e.g., F3548's `OperationalIntentReference` does *not* represent 4D `altitude` extents, whereas `Volume4D` details do). Always verify property existence programmatically instead of relying on assumptions.
+- **Datetime Types**: Fields annotated as `StringBasedDateTime` (or generic `Time` structures wrapping it) inherently expose a `.datetime` property mapping directly to Python's built-in `datetime.datetime` type. Always rely on this `.datetime` attribute for date-math instead of manually parsing string strings.
+
+## 3. `uss_qualifier` Test Development Rules
+- **Markdown Documentation is Mandatory**: Every check (e.g., `self._scenario.check(...)`) added to a Python test scenario must be meticulously documented in the corresponding Markdown documentation file for that specific test scenario or fragment.
+- **Documentation Traceability**: All documented test checks must trace back to exactly one or more requirements using a specific bold format, and feature a severity emoji prefix (e.g., `## 🛑 Correct operational intent details check`). You must refer to `monitoring/uss_qualifier/scenarios/README.md` for specific markup details before modifying test steps.
+
+## 4. Local Testing constraints
+- **Docker Dependency**: Mock USS and DSS environments require active Docker containers. Standard testing commands are typically structured via bash scripts like `./monitoring/uss_qualifier/run_locally.sh <config>`. If container-building fails due to `Authentication` or package registry issues in the agent's environment, gracefully halt and ask the human user to run the script instead.
+
+## 5. Continuous Improvement of this Guide
+- **Pay It Forward**: As an AI agent, your ability to acquire context quickly is critical. If, during your work, you find yourself spending significant time overcoming a misunderstanding, discovering a hidden project nuance, or writing introspection scripts to understand a schema, **please proactively update this `AGENTS.md` file**. Add concise tips or warnings to help future agents (including yourself) avoid the same friction, alongside completing your core task.
+- **Correcting Mistakes**: Documentation can become outdated or contain errors over time. If your core work reveals that information in this `AGENTS.md` file is mistaken or missing critical context, please take the initiative to fix those errors while completing your primary objectives!