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name: limi-x description: "Use LimiX for structured/tabular foundation-model inference, configuration, retrieval tuning, and benchmark-style workflows." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LimiX repo skill

Use this skill when a task involves LimiX, LimiXPredictor, LDM structured-data foundation models, tabular classification/regression, missing-value imputation, LimiX inference configs, retrieval-based ensemble inference, or LimiX benchmark-style dataset loops.

LimiX is a source-repo style project rather than a packaged PyPI distribution. Future agents should treat this skill as self-contained operating guidance: read the bundled references/scripts here first, then apply them to the user's active LimiX checkout, local checkpoint, or local tabular data. Do not assume model checkpoints or benchmark datasets have already been downloaded.

First checks

1. Read references/repo-provenance.md before deciding whether this skill is current for a checkout.
2. Read references/installation.md when installing dependencies, checking CUDA/flash-attn, or explaining why source imports fail.
3. Read references/model-and-config-overview.md to choose model family, task, and inference config style.
4. Run scripts/check_limix_environment.py for a safe import/config/backend diagnostic before full checkpoint inference.
5. Use references/troubleshooting.md for cross-cutting checkpoint, CUDA, source-layout, download, and data-size failures.

Route by task

User intent	Load this sub-skill	Why
Direct Python API prediction with LimiXPredictor, including classification, regression, or MVI	sub-skills/predictor-inference/SKILL.md	Owns constructor/predict signatures, local checkpoint/config recipes, return shapes, and MVI helpers.
Validate or run benchmark-style dataset-folder workflows	sub-skills/benchmark-cli/SKILL.md	Owns dataset-root layout, classification/regression CLI flags, output CSVs, metric interpretation, and safe layout validation.
Tune retrieval-based ensemble inference or preview search-space parameters	sub-skills/retrieval-optimization/SKILL.md	Owns retrieval config keys, attention/retrieval classes, Optuna search flow, and memory/OOM guidance.
Choose, inspect, generate, or debug inference config JSON and preprocessing transforms	sub-skills/configuration-preprocessing/SKILL.md	Owns config catalog, config schema, preprocessing classes, CPU-safety checks, and config validator/generator scripts.

Minimal setup shape

A practical LimiX session usually needs:

• a LimiX source checkout or equivalent import path exposing inference, model, utils, and retrieval_extension modules;
• Python 3.12-era dependencies from the project's environment/Docker guidance, especially PyTorch, scikit-learn, NumPy, pandas, SciPy, einops, tqdm, huggingface-hub, kditransform, hyperopt, and optional Optuna;
• a local LimiX checkpoint (LimiX-16M.ckpt or LimiX-2M.ckpt) when running predictions;
• a config JSON list compatible with the task and device;
• CUDA/GPU for retrieval configs, DDP, flash-attn acceleration, and practical full checkpoint inference; CPU is only appropriate for non-retrieval setup/config checks and limited non-retrieval experimentation.

Safe diagnostic:

python scripts/check_limix_environment.py --config path/to/config.json

Use --expect-cuda only when the task requires GPU runtime evidence. The diagnostic does not download checkpoints or run full model inference.

Key operating rules

• Never treat a config parse or import check as proof that full LimiX checkpoint inference ran.
• Use no-retrieval configs on CPU. The predictor rejects retrieval when the first pipeline has retrieval_config.use_retrieval=true and the device is CPU.
• Prefer explicit local --data_dir, --model_path, and --inference_config_path values. Omitting model/data paths can trigger network downloads into local caches.
• Keep benchmark-scale runs, checkpoint downloads, and Optuna searches behind explicit user approval because they can be long-running, network-dependent, and GPU-memory sensitive.
• When a workflow spans multiple areas, start with the route that owns the immediate user failure: config errors route to configuration/preprocessing; data-root errors route to benchmark CLI; OOM or retrieval parameters route to retrieval optimization; output-shape/API questions route to predictor inference.