docs: address PR #369 review comments and add dataset preflight

- Add context limit rationale to system_requirements.md with memory formula
- Update SKILL.md to include XReg/covariates in description and usage sections
- Add dataset-aware memory estimation to check_system.py with new CLI args
- Document memory estimation in api_reference.md with Mermaid diagram
- Add dataset preflight section to SKILL.md with examples

Resolves review comments about:
- How context limits (512/1024) were determined
- Including XReg mode description in skill documentation

Bonus enhancement: Dataset preflight checking prevents OOM before loading data.

timesfm-forecasting/references/api_reference.md

@@ -221,6 +221,70 @@ Where `B` = batch size (number of input series), `H` = forecast horizon.
 
 ---
 
+---
+
+## Memory Estimation
+
+Before running forecasts on large datasets, estimate memory requirements:
+
+### Formula
+
+```mermaid
+block-beta
+    columns 3
+    ram["Total RAM Required"] model["Model Weights<br/>~0.8 GB"] overhead["Runtime Overhead<br/>~0.5 GB"] buffers["I/O Buffers<br/>~0.2 MB per 1000 series<br/>per 1000 context"]
+    
+    ram --> model
+    ram --> overhead
+    ram --> buffers
+```
+
+**Formula**:  
+`RAM (GB) ≈ 0.8 + 0.5 + (0.0002 × num_series × context_length)`
+
+**Variables**:
+- `num_series`: Number of time series in your batch
+- `context_length`: Your `max_context` value (or max series length)
+- `batch_size`: Your `per_core_batch_size` (affects parallel processing overhead)
+
+### Quick Reference
+
+| Dataset Size | Context=512 | Context=1024 | Context=2048 |
+|--------------|-------------|--------------|--------------|
+| 100 series | ~1.4 GB | ~1.5 GB | ~1.7 GB |
+| 1,000 series | ~1.9 GB | ~2.3 GB | ~3.1 GB |
+| 10,000 series| ~9.0 GB | ~17.0 GB | ~33.0 GB |
+
+### Using the Preflight Checker
+
+```bash
+python scripts/check_system.py \
+  --num-series 1000 \
+  --context-length 1024 \
+  --batch-size 32
+```
+
+This validates both system requirements AND dataset fit before loading the model.
+
+### Reducing Memory Usage
+
+If your dataset is too large:
+
+1. **Reduce context length**: Use `max_context=512` instead of 1024+ (50% reduction)
+2. **Process in chunks**: Split large batches into smaller groups:
+
+```python
+CHUNK_SIZE = 100
+for i in range(0, len(inputs), CHUNK_SIZE):
+    chunk = inputs[i:i+CHUNK_SIZE]
+    point, quantiles = model.forecast(horizon=H, inputs=chunk)
+    # Save chunk results
+```
+
+3. **Reduce batch size**: Lower `per_core_batch_size` (slower but less memory)
+4. **Use CPU**: If GPU OOM, the model will automatically fall back to CPU
+
+
 ## Error Handling
 
 | Error | Cause | Fix |

timesfm-forecasting/references/system_requirements.md

@@ -5,6 +5,33 @@
 TimesFM can run on a variety of hardware configurations. This guide helps you
 choose the right setup and tune performance for your machine.
 
+### How Context Limits Are Determined
+
+The `max_context` values in each tier are **conservative recommendations** based on memory-performance tradeoffs, not hard limits. TimesFM 2.5 supports up to 16,384 context points, but smaller values are recommended for most use cases.
+
+**Why 512 and 1024?**
+
+| Factor | 512 Context | 1024 Context |
+|--------|-------------|--------------|
+| **Memory per 1000 series** | ~100 MB | ~200 MB |
+| **Typical Use Case** | Daily data, ~1-2 years | Daily data, ~2-3 years |
+| **Inference Speed** | Faster | Moderate |
+| **Hardware** | 4-8 GB RAM | 16 GB RAM or GPU |
+
+**Memory Formula**: `RAM ≈ model_weights + 0.5 GB + (0.2 MB × num_series × context_length / 1000)`
+
+Where:
+- `model_weights` = ~800 MB (TimesFM 2.5)
+- `context_length` = your `max_context` value
+- `num_series` = number of time series in your batch
+
+**You can use larger contexts** if your hardware supports it:
+- **Up to 2048**: Requires ~16 GB RAM for moderate batch sizes
+- **Up to 4096**: Requires GPU or 32+ GB RAM
+- **Up to 16384**: Maximum supported, requires significant memory
+
+See [Data Preparation Guide](data_preparation.md) for context length recommendations by data frequency.
+
 ### Tier 1: Minimal (CPU-Only, 4–8 GB RAM)
 
 - **Use case**: Light exploration, single-series forecasting, prototyping