---

name: libra description: "Use the Libra ergonomic machine-learning client for query-driven tabular, NLP, vision, recommendation, dashboard, and analysis workflows." disable-model-invocation: true metadata: disco-role: operating license: MIT

Libra Repo Skill

Use this skill when a task involves the Palashio/libra Python package, especially from libra import client. Libra builds ML workflows from natural-language-ish query strings, stores trained models and artifacts in client.models, and exposes helper methods for model inspection, plots, prediction, analysis, tuning, recommendations, Streamlit dashboards, NLP generation, image classification, and GANs.

Always read references/environment-and-compatibility.md before running code. The repository is a legacy TensorFlow/Keras/pandas project: modern Python stacks need compatibility shims, while the original pins are internally inconsistent. Use the bundled scripts/libra_compat.py before importing libra on modern pandas, and reapply it after import if you are going to run query methods, because queries.py resets FutureWarning handling during import. Do not assume CUDA is usable just because GPUs are present.

If the checkout or package version has changed, compare it with references/repo-provenance.md before trusting the generated skill or before running refresh work.

Setup

• Do not install requirements.txt blindly. It mixes old TensorFlow/Keras pins with imports that only work on older pandas/scikit-learn releases.
• Preferred modern path for inspection and smoke tests:
  1. Prepare a compatible Python environment using the package notes in references/environment-and-compatibility.md.
  2. From the repository root, run python -m pip install -e . --no-deps so the local checkout is importable without pulling the legacy dependency list verbatim.
  3. Run python skills/disco/libra/scripts/inspect_client_surface.py --methods-only for a minimal import/surface check.
  4. Run python skills/disco/libra/scripts/smoke_tabular_decision_tree.py for a tiny CPU smoke test.
• For stricter legacy fidelity, use the older Python 3.8 stack described in references/environment-and-compatibility.md.

Fast routing

• For client(...), dataset reading, models, info(), model(), predict(), plots(), accuracy(), losses(), target(), vocab(), recommend(), dashboard(), tuning, or package/runtime setup, start with this root skill plus references/api-surface.md and references/troubleshooting.md.
• For tabular regression, classification, clustering, recommendation, model inspection, tuning, or dashboard launch on CSV/XLSX/JSON data, load sub-skills/tabular-modeling/SKILL.md.
• For text classification, summarization, named entities, GPT-2 text generation, image captioning, or NLP preprocessing and corpora setup, load sub-skills/nlp-and-generation/SKILL.md.
• For image classification, image preprocessing read modes, TFJS/TFLite export, feature maps, GAN generation, or image-layout troubleshooting, load sub-skills/vision-and-generative/SKILL.md.

Core operating pattern

from libra import client

c = client("path/to/data.csv")
c.decision_tree_query("predict target column")
print(c.models.keys())
print(c.info())

Key behavior to keep in mind:

1. A client instance represents one dataset path. Most queries read self.dataset each time.
2. Query methods store a dictionary under a fixed key such as decision_tree, svm, regression_ANN, classification_ANN, convolutional_NN, text_classification, summarization, image_caption, text_generation, named_entity_recognition, content_recommender, or DCGAN.
3. latest_model is updated after most query methods; many helper methods default to that value.
4. Target-column inference goes through get_value_instruction() plus Levenshtein matching, so user instructions should contain words close to the target column name. If target detection is wrong, make the instruction closer to the exact column name or pass explicit parameters like label_column, drop, text, image_column, or read_mode where available.
5. Training methods can write files: model save flags, TFJS/TFLite export, CNN image preprocessing, GAN output images, Keras Tuner directories, plot saving, and Streamlit dashboard launches. Choose output directories deliberately.

Self-contained helper scripts

• scripts/libra_compat.py applies pandas/private-warning compatibility shims before importing legacy Libra code on modern stacks.
• scripts/inspect_client_surface.py prints public client methods and signatures.
• scripts/smoke_tabular_decision_tree.py creates a tiny synthetic CSV and runs a CPU decision-tree smoke test without using the original checkout datasets.

These scripts are intentionally generic; they avoid absolute source-checkout paths and do not depend on original-repo example files or notebooks at runtime.