Add detailed file index and enhance skill documentation for autopilot, decompose, deploy, plan, and research skills. Introduce tests-only mode in decompose skill, clarify required files for deploy and plan skills, and improve prerequisite checks across skills for better user guidance and workflow efficiency.

_docs/00_problem/input_data/data_parameters.md

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+# Input Data Parameters
+
+## Media Input
+
+### Single Image Detection (POST /detect)
+
+| Parameter | Type | Source | Description |
+|-----------|------|--------|-------------|
+| file | bytes (multipart) | Client upload | Image file (JPEG, PNG, etc. — any format OpenCV can decode) |
+| config | JSON string (optional) | Query/form field | AIConfigDto overrides |
+
+### Media Detection (POST /detect/{media_id})
+
+| Parameter | Type | Source | Description |
+|-----------|------|--------|-------------|
+| media_id | string | URL path | Identifier for media in the Loader service |
+| AIConfigDto body | JSON (optional) | Request body | Configuration overrides |
+| Authorization header | Bearer token | HTTP header | JWT for Annotations service |
+| x-refresh-token header | string | HTTP header | Refresh token for JWT renewal |
+
+Media files (images and videos) are resolved by the Inference pipeline via paths in the config. The Loader service provides model files, not media files directly.
+
+## Configuration Input (AIConfigDto / AIRecognitionConfig)
+
+| Field | Type | Default | Range/Meaning |
+|-------|------|---------|---------------|
+| frame_period_recognition | int | 4 | Process every Nth video frame |
+| frame_recognition_seconds | int | 2 | Minimum seconds between video annotations |
+| probability_threshold | float | 0.25 | Minimum detection confidence (0..1) |
+| tracking_distance_confidence | float | 0.0 | Movement threshold for tracking (model-width fraction) |
+| tracking_probability_increase | float | 0.0 | Confidence increase threshold for tracking |
+| tracking_intersection_threshold | float | 0.6 | Overlap ratio for NMS deduplication |
+| model_batch_size | int | 1 | Inference batch size |
+| big_image_tile_overlap_percent | int | 20 | Tile overlap for large images (0-100%) |
+| altitude | float | 400 | Camera altitude in meters |
+| focal_length | float | 24 | Camera focal length in mm |
+| sensor_width | float | 23.5 | Camera sensor width in mm |
+| paths | list[str] | [] | Media file paths to process |
+
+## Model Files
+
+| File | Format | Source | Description |
+|------|--------|--------|-------------|
+| azaion.onnx | ONNX | Loader service | Base detection model |
+| azaion.cc_{M}.{m}_sm_{N}.engine | TensorRT | Loader service (cached) | GPU-specific compiled engine |
+
+## Static Data
+
+### classes.json
+
+Array of 19 objects, each with:
+
+| Field | Type | Example | Description |
+|-------|------|---------|-------------|
+| Id | int | 0 | Class identifier |
+| Name | string | "ArmorVehicle" | English class name |
+| ShortName | string | "Броня" | Ukrainian short name |
+| Color | string | "#ff0000" | Hex color for visualization |
+| MaxSizeM | int | 8 | Maximum physical object size in meters |
+
+## Data Volumes
+
+- Single image: up to tens of megapixels (aerial imagery). Large images are tiled.
+- Video: processed frame-by-frame with configurable sampling rate.
+- Model file: ONNX model size depends on architecture (typically 10-100 MB). TensorRT engines are GPU-specific compiled versions.
+- Detection output: up to 300 detections per frame (model limit).
+
+## Data Formats
+
+| Data | Format | Serialization |
+|------|--------|---------------|
+| API requests | HTTP multipart / JSON | Pydantic validation |
+| API responses | JSON | Pydantic model_dump |
+| SSE events | text/event-stream | JSON per event |
+| Internal config | Python dict | AIRecognitionConfig.from_dict() |
+| Legacy (unused) | msgpack | serialize() / from_msgpack() |