Claude-skill-registry beam-tracking-ml

Design and refactor beam tracking ML/RL pipelines (CSI teacher vs RSRP student), enforce shape contracts, and produce inference-safe models.

install

source · Clone the upstream repo

git clone https://github.com/majiayu000/claude-skill-registry

Claude Code · Install into ~/.claude/skills/

T=$(mktemp -d) && git clone --depth=1 https://github.com/majiayu000/claude-skill-registry "$T" && mkdir -p ~/.claude/skills && cp -r "$T/skills/data/beam-tracking-ml" ~/.claude/skills/majiayu000-claude-skill-registry-beam-tracking-ml && rm -rf "$T"

manifest: skills/data/beam-tracking-ml/SKILL.md

Beam Tracking ML Skill

Use this Skill when:

translating the RL架構 diagram into code
refactoring
```
sionna_beam_tracking_v2.py
```
ideas into modular components
designing observation/action schemas

Guardrails

Always define and test shapes (B,N_BEAMS) etc.
Keep student (online) policy lightweight and deterministic.
Treat CSI-heavy path as offline only unless we explicitly design compression.

Where to put code

Models:
```
beam_tracking/model/
```
Training scripts:
```
scripts/
```
(do not bloat runtime xApp)
Interfaces:
```
beam_tracking/schemas.py
```

Suggested distillation workflow

Train teacher on CSI dataset (offline).
Run teacher over same trajectories, log action distributions.
Train student to match teacher (KL divergence).
Optionally fine-tune student with small online data.