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.ai/standards/plugin_design.md
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.ai/standards/plugin_design.md
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# [DEF:Std:Plugin:Standard]
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# @TIER: CRITICAL
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# @PURPOSE: Standards for building and integrating Plugins.
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# @LAYER: Domain (Plugin)
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# @INVARIANT: All plugins MUST inherit from `PluginBase`.
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# @INVARIANT: All plugins MUST be located in `backend/src/plugins/`.
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## 1. PLUGIN CONTRACT
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Every plugin must implement the following properties and methods:
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* `id`: Unique string (e.g., `"my-plugin"`).
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* `name`: Human-readable name.
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* `description`: Brief purpose.
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* `version`: Semantic version.
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* `get_schema()`: Returns JSON schema for input validation.
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* `execute(params: Dict[str, Any], context: TaskContext)`: Core async logic.
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## 2. STRUCTURED LOGGING (TASKCONTEXT)
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Plugins MUST use `TaskContext` for logging to ensure proper source attribution:
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* **Source Attribution:** Use `context.logger.with_source("src_name")` for specific operations (e.g., `"superset_api"`, `"git"`, `"llm"`).
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* **Levels:**
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* `DEBUG`: Detailed diagnostics (API responses).
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* `INFO`: Operational milestones (start/end).
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* `WARNING`: Recoverable issues.
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* `ERROR`: Failures stopping execution.
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* **Progress:** Use `context.logger.progress("msg", percent=XX)` for long-running tasks.
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## 3. BEST PRACTICES
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1. **Asynchronous Execution:** Always use `async/await` for I/O operations.
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2. **Schema Validation:** Ensure the `get_schema()` precisely matches the `execute()` input expectations.
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3. **Isolation:** Plugins should be self-contained and not depend on other plugins directly. Use core services (`ConfigManager`, `TaskManager`) via dependency injection or the provided `context`.
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# [/DEF:Std:Plugin]
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