git:20261101.e8fbacd to git:20261103.1ae4ddc

- > ADA is the math arm for LLMs: a Python package, command-line tool and set of function-calling tool definitions that compute numbers from CSV, TSV and Excel tables with pandas, SciPy and statsmodels, show the calculation and the rows behind each one, and check a number stated about a table: verified, or unverified with one of six reasons (missing_context, ambiguous_intent, inaccessible_data, unsupported_analysis, conflicting_evidence, failed_check) and the one question that would let the check go ahead. A language model (Claude, Codex, or an open-source model run with Ollama, llama.cpp, vLLM or LM Studio) calls ADA instead of doing arithmetic itself. ADA also maps a folder of related files and the keys that join them, fits cross-validated models that predict a column (Kaggle style, with a readable model on request), and makes a paper's assets from research data (audit, dictionary, tables, figures, methods draft) that `ada verify` rebuilds and checks. MIT licence, Python 3.11+.

- tools.run(name, arguments, root="data") # runs ada_ask, ada_check_claim, ada_scan or ada_model; returns JSON to send back

- ada mcp # MCP over stdio: {"mcpServers": {"ada": {"command": "uvx", "args": ["--from", "automated-data-analyst", "ada", "mcp"]}}}

- Predict a column (needs `pip install "automated-data-analyst[model]"`):

-

- ```

- ada model titanic/ --metric accuracy --out submission.csv # cross-validated LightGBM, XGBoost and a linear model, blended

- ada auto titanic/ --metric accuracy --jobs 2 # a wider search: CatBoost, a neural network, a second layer

- ada model train.csv test.csv --target price --explain # a linear or additive model with coefficients and effect curves

- ```

-

- Install with `pip install automated-data-analyst`. For a paper, in the folder that holds a study's data:

-

- ```

- ada init data/measurements.csv # asks about the study; writes study.yml

- ada build # writes paper-assets/

- ada verify # rebuilds and checks every number

- ```

-

- `study.yml` holds the design (independent, paired, repeated or clustered), the subject or cluster column, each variable's role and type, exclusions with their reasons, and the planned analyses. The design picks the test: Welch's t-test or ANOVA for independent groups, the paired t-test for paired data, a linear mixed model with a random intercept per subject or cluster for repeated and clustered data, with p-values adjusted within families. ADA runs only the analyses listed, labels each planned or exploratory, and refuses designs it can't fit instead of approximating them.

- From Python: `from ada import research; research.build("study.yml")`, or `import ada; ada.analyze("data.csv")` for the first look.

- - [Predicting a column](https://raw.githubusercontent.com/saineshnakra/automated-data-analyst/main/docs/reference/modelling.md): `ada model`, `ada auto` and `--explain`

- - [Paper assets for a study](https://raw.githubusercontent.com/saineshnakra/automated-data-analyst/main/docs/research.md): `ada init`, `ada build` and `ada verify`, the study.yml format, the tests used and every file written

- - [Agent skill](https://raw.githubusercontent.com/saineshnakra/automated-data-analyst/main/ada/skills/ada/SKILL.md): how an AI assistant should use ADA on a researcher's data

- - [How it works](https://raw.githubusercontent.com/saineshnakra/automated-data-analyst/main/docs/how-it-works.md): the steps from upload to report

- - [Kaggle competitions](https://raw.githubusercontent.com/saineshnakra/automated-data-analyst/main/results/kaggle/README.md): `ada model` and `ada auto` scored on 22 tabular competitions, with a holdout inside each

- - [Tested on 60 research datasets](https://raw.githubusercontent.com/saineshnakra/automated-data-analyst/main/results/README.md): how the first look did on real data, and what it still gets wrong