penguineer

Find the root cause of off-spec lots from historian and LIMS exports, on a laptop, offline.

penguineer reads files and writes files. Its engines are deterministic Python; an LLM is optional and never decides a ranking. Every command is defined once as a Pydantic parameter model, a result model and a function, and the CLI, the --json output and the Python API are generated from that one definition. Alchemist imports penguineer as a library and adds the UI, runners and project management on top.

Status: early. init, data health, graph check, graph draft, graph lags, lag, investigate, bench homeostat and mcp work. On Homeostat plants, the injected cause is in the top three in 93% of hidden scenarios (results).

Documentation

The documentation is at https://kausalflow.com/penguineer/. Its source is in docs/ and builds with Zensical:

uvx zensical serve   # preview on http://localhost:8000

Install

uv tool install penguineer   # or: pip install penguineer; Python 3.11+
penguineer --help

From a checkout: uv sync, then uv run penguineer --help.

Try it on the demo plant

uv run penguineer data health \
  --data examples/demo/historian.csv \
  --graph examples/demo/graph.yaml

data health: 5 tags, 3555 samples, 2026-08-03 00:00 to 2026-08-03 11:59 UTC
findings: 4 (1 critical, 3 warning, 0 info)

  critical PI-103.PV  out_of_range 2026-08-03 09:30  outside instrument range [0, 10]: min 10.918, max 11.114
  warning  AI-201.PV  spike        2026-08-03 08:15  spike of 2.5293 from 3.2329
  warning  FI-102.PV  gap          2026-08-03 05:59  no samples for 46 min (usual period 60 s)
  warning  TI-101.PV  flatline     2026-08-03 03:00  value frozen at 180.585 for 2.0 h

The demo data has exactly those four faults injected (see scripts/make_demo_data.py). The setpoint tag FIC-102.SP is constant all day and is not reported, because the graph says it is a loop setpoint.

Add --json to get the result model on stdout. Every run writes runs/<run_id>/run.json under --project (default: the current folder) with the params, the equivalent command line, versions and SHA-256 hashes of every input.

Investigate an off-spec lot

examples/offspec/ is a small plant with one off-spec lot and a known cause (a fouled feed preheater), a decoy and a frozen tag:

uv run penguineer investigate --lot L0803-A \
  --lims examples/offspec/lims.csv --data examples/offspec/historian.csv \
  --graph examples/offspec/graph.yaml --lots examples/offspec/lots.csv

  #1 TI-101.PV  high   score 0.99  process
     TI-101.PV (E-101) ran 5.73 degC low from 2026-08-03 05:29, 50 min upstream of the sample point; purity moved when it arrived.

It writes report.json, report.html, report.md and evidence plots under reports/<run_id>/. See Investigating an off-spec lot.

Try it on a live simulated plant

Homeostat Live publishes a simulated plant's sensor data, lab results and data dictionary. This downloads a released week and derives a plant graph (tags, instrument ranges, control loops, lab qualities) from the dictionary:

uv run python scripts/fetch_homeostat_live.py --plant isidis-01 \
  --since 2026-10-02T17:00:00+00:00 --until 2026-10-09T17:00:00+00:00 --out data/isidis-01
uv run penguineer data health --data data/isidis-01/live.csv --graph data/isidis-01/graph.yaml

The tests in tests/test_homeostat_live.py run on that week. They are skipped without network access, and CI requires them.

Commands

Exit codes: 0 success, 1 validation failed (for example graph errors), 2 usage error, 3 not enough data, 4 internal error or not implemented yet.

Python API

import penguineer

result = penguineer.run("data health", data="historian.csv", project="my-unit")
result.findings[0].detail

penguineer.schemas()  # JSON Schemas of every command's Params and Result

penguineer.execute(...) also returns the run record and run folder. Calls keep no global state: inputs and outputs live under the project folder passed in, so several processes can run side by side.

MCP server

pip install "penguineer[mcp]"
claude mcp add penguineer -- penguineer mcp --project /path/to/my-unit

penguineer mcp serves every command as a tool to an MCP client over stdio, with the command's parameter and result schemas. It is local only: no port, no UI. See the MCP guide.

Repository layout

packages/plantgraph/   plant context graph schema (depends only on Pydantic), shared with homeostat
src/penguineer/
  core/                command registry, RunContext, run records, errors
  cli/                 Typer app generated from the registry
  commands/            one module per command: Params, Result, run()
  io/                  historian, LIMS, graph, DEXPI P&ID, Homeostat Live and scenario-bundle readers
  engines/             deterministic engines: data health, dead times, investigate, calibration
  bench/               the homeostat bench plant, bundle export and scoring
  llm/                 optional LLM providers, response cache, versioned prompts
  report/              report models, grounded summary, evidence plots, HTML and Markdown templates
  graph/               graph check and graph draft
tests/                 unit, CLI, to_argv round-trip property tests, schema snapshot
examples/demo/         a small plant with injected data faults
examples/offspec/      a small plant with an off-spec lot and a known root cause

Development

uv sync
uv run pytest
uv run ruff check . && uv run ruff format --check .

A change to any command's Params or Result schema fails tests/test_schemas.py on purpose, because Alchemist builds on those schemas. After an intended change, regenerate the snapshot with PENGUINEER_UPDATE_SCHEMAS=1 uv run pytest tests/test_schemas.py.

License

Apache-2.0