feat(integration): Complete feature rewrite with pipeline architecture, effects system, and display improvements

Major changes:
- Pipeline architecture with capability-based dependency resolution
- Effects plugin system with performance monitoring
- Display abstraction with multiple backends (terminal, null, websocket)
- Camera system for viewport scrolling
- Sensor framework for real-time input
- Command-and-control system via ntfy
- WebSocket display backend for browser clients
- Comprehensive test suite and documentation

Issue #48: ADR for preset scripting language included

This commit consolidates 110 individual commits into a single
feature integration that can be reviewed and tested before
further refinement.

engine/pipeline/adapters/effect_plugin.py

@@ -0,0 +1,117 @@
+"""Adapter wrapping EffectPlugin as a Stage."""
+
+from typing import Any
+
+from engine.pipeline.core import PipelineContext, Stage
+
+
+class EffectPluginStage(Stage):
+    """Adapter wrapping EffectPlugin as a Stage.
+
+    Supports capability-based dependencies through the dependencies parameter.
+    """
+
+    def __init__(
+        self,
+        effect_plugin,
+        name: str = "effect",
+        dependencies: set[str] | None = None,
+    ):
+        self._effect = effect_plugin
+        self.name = name
+        self.category = "effect"
+        self.optional = False
+        self._dependencies = dependencies or set()
+
+    @property
+    def stage_type(self) -> str:
+        """Return stage_type based on effect name.
+
+        HUD effects are overlays.
+        """
+        if self.name == "hud":
+            return "overlay"
+        return self.category
+
+    @property
+    def render_order(self) -> int:
+        """Return render_order based on effect type.
+
+        HUD effects have high render_order to appear on top.
+        """
+        if self.name == "hud":
+            return 100  # High order for overlays
+        return 0
+
+    @property
+    def is_overlay(self) -> bool:
+        """Return True for HUD effects.
+
+        HUD is an overlay - it composes on top of the buffer
+        rather than transforming it for the next stage.
+        """
+        return self.name == "hud"
+
+    @property
+    def capabilities(self) -> set[str]:
+        return {f"effect.{self.name}"}
+
+    @property
+    def dependencies(self) -> set[str]:
+        return self._dependencies
+
+    @property
+    def inlet_types(self) -> set:
+        from engine.pipeline.core import DataType
+
+        return {DataType.TEXT_BUFFER}
+
+    @property
+    def outlet_types(self) -> set:
+        from engine.pipeline.core import DataType
+
+        return {DataType.TEXT_BUFFER}
+
+    def process(self, data: Any, ctx: PipelineContext) -> Any:
+        """Process data through the effect."""
+        if data is None:
+            return None
+        from engine.effects.types import EffectContext, apply_param_bindings
+
+        w = ctx.params.viewport_width if ctx.params else 80
+        h = ctx.params.viewport_height if ctx.params else 24
+        frame = ctx.params.frame_number if ctx.params else 0
+
+        effect_ctx = EffectContext(
+            terminal_width=w,
+            terminal_height=h,
+            scroll_cam=0,
+            ticker_height=h,
+            camera_x=0,
+            mic_excess=0.0,
+            grad_offset=(frame * 0.01) % 1.0,
+            frame_number=frame,
+            has_message=False,
+            items=ctx.get("items", []),
+        )
+
+        # Copy sensor state from PipelineContext to EffectContext
+        for key, value in ctx.state.items():
+            if key.startswith("sensor."):
+                effect_ctx.set_state(key, value)
+
+        # Copy metrics from PipelineContext to EffectContext
+        if "metrics" in ctx.state:
+            effect_ctx.set_state("metrics", ctx.state["metrics"])
+
+        # Copy pipeline_order from PipelineContext services to EffectContext state
+        pipeline_order = ctx.get("pipeline_order")
+        if pipeline_order:
+            effect_ctx.set_state("pipeline_order", pipeline_order)
+
+        # Apply sensor param bindings if effect has them
+        if hasattr(self._effect, "param_bindings") and self._effect.param_bindings:
+            bound_config = apply_param_bindings(self._effect, effect_ctx)
+            self._effect.configure(bound_config)
+
+        return self._effect.process(data, effect_ctx)