forecasting-reverso
Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.
npx skills add majiayu000/claude-skill-registry --skill forecasting-reverso --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Reverso Time Series Forecasting Produce zero-shot univariate time series forecasts using the Reverso foundation model family (arXiv:2602.17634), implemented in NumPy/Numba for CPU-only container execution. ## Setup (run once per conversation) ```bash uv pip install numba --system --break-system-packages cp /mnt/skills/user/forecasting-reverso/scripts/reverso.py /home/claude/reverso.py cp /mnt/skills/user/forecasting-reverso/scripts/load_checkpoint.py /home/claude/load_checkpoint.py ``` ## Obtaining Weights Two paths depending on network access: ### Path A: Direct download (HuggingFace allow-listed) ```python import urllib.request, os os.makedirs("/tmp/reverso", exist_ok=True) url = "https://huggingface.co/shinfxh/reverso/resolve/main/checkpoints/reverso_small/checkpoint.pth" urllib.request.urlretrieve(url, "/tmp/reverso/checkpoint.pth") ``` ### Path B: User upload (HuggingFace not accessible) If the download fails with a network error, tell the user: > I can't reach HuggingFace from this environment. Please download the checkpoint from > https://huggingface.co/shinfxh/reverso/blob/main/checkpoints/reverso_small/checkpoint.pth > and upload it here. Then load from `/mnt/user-data/u
- Setup (run once per conversation)
- Obtaining Weights
- Path A: Direct download (HuggingFace allow-listed)
- Path B: User upload (HuggingFace not accessible)
- Loading weights
- Model Configuration
- Forecasting
- Key parameters
- Input Handling
- Visualization
- Performance
- Container Environment Limits
- Limitations
uv pip install numba --system --break-system-packages cp /mnt/skills/user/forecasting-reverso/scripts/reverso.py /home/claude/reverso.py cp /mnt/skills/user/forecasting-reverso/scripts/load_checkpoint.py /home/claude/load_checkpoint.py
What does the forecasting-reverso skill do?
Zero-shot univariate time series forecasting using the Reverso foundation model (NumPy/Numba CPU-only inference). Activate when users provide time series data and request forecasts, predictions, or extrapolations. Supports Reverso Small (550K params). Triggers on "forecast", "predict", "time series", "Reverso", or when tabular data with a temporal dimension needs future-value estimation.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill forecasting-reverso --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.