Make both distributions fit to publish

The release workflow was already right; what it would have uploaded was not.
`fluksio` had no readme, so its PyPI page would have been blank — the app
repo's own README is a contributor's map of `frontend/` and `docker/`, which
is the wrong front page for `pip install fluksio`. It now has one of its own,
aimed at somebody who landed on the project page. Both distributions gain
authors, urls, keywords and classifiers; `twine check` passes clean on all
four artifacts where it warned on two before.

The workflow publishes `fluksio-worker` first, because `fluksio` depends on it
and the other order leaves a few seconds — the whole of a first release — in
which the dependency cannot be resolved. `--check-url` makes a re-run skip
what is already uploaded rather than failing on it, which matters because a
version on PyPI can never be replaced.

Licence metadata is deliberately still absent: LICENSE is MIT in somebody
else's name, inherited from the template this was scaffolded from, and whose
it should be is not a decision to make in a commit. NOTEPAD carries it.

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_012ue1tkFWB1bcGy3aWhCKpU

backend/README.md

@@ -0,0 +1,76 @@
+# Fluksio
+
+A node-based automation engine: flows, dashboards and batch runs, in one
+resident process with no infrastructure behind it.
+
+```sh
+pip install fluksio
+fluksio serve
+```
+
+That is the whole installation — no Docker, no database server, no ports to
+open. It keeps a SQLite database, a git repository of your flows and an
+artifact store under `~/.fluksio`, and prints an admin password once.
+
+## For data science
+
+Your functions become nodes where they already live. Install Fluksio into the
+environment you work in and your nodes run on it — the packages are already
+there:
+
+```python
+# myresearch/train.py
+import fluksio
+from fluksio import Port, node
+
+@node(
+    requires=["dataset", Port("lr", "float")],
+    provides=[Port("loss", "float", stream=True), Port("weights", "artifact")],
+    device="gpu", device_policy="prefer",
+)
+def fit(dataset, lr, epochs=25):
+    for epoch in range(epochs):
+        loss = step(...)
+        yield {"loss": loss}          # published as it happens, kept as a series
+    return {"weights": fluksio.save_artifact("weights.pt")}
+```
+
+```python
+# myresearch/pipeline.py
+from fluksio import Flow, Port
+from myresearch.train import fit
+
+train = Flow("train", nodes=[prepare, fit, evaluate],
+             inputs=[Port("lr", "float", initial=0.01)], outputs=["score"])
+```
+
+```sh
+fluksio login --url http://127.0.0.1:8000
+fluksio sync myresearch
+fluksio run train --lr 0.05 --wait
+```
+
+The decorators return your functions untouched, so everything stays callable,
+testable and importable as what it was. A metric leaves through a declared
+port rather than a logging call, which is why there is no `log_metric()`: the
+run keeps the whole series, a chart can bind to it, and a downstream node can
+consume it.
+
+## What else it does
+
+- **Flows** — typed messages between nodes, wired by name, edited on a canvas
+  or declared in code. Every change is a commit in a git repository you own.
+- **Runs** — an experiment and a CI-style job are the same entity. Parameters,
+  seed, result, per-node timings, artifacts and the commit it ran at.
+- **Dashboards** — charts and controls bound to the same messages the flows
+  carry, with no separate metrics pipeline.
+- **Remote workers** — `pip install fluksio-worker` on the GPU box; it dials
+  *out* over one websocket, so nothing there has to be reachable.
+
+## Links
+
+- Documentation: <https://docs.fluksio.com>
+- Getting started (data science): <https://docs.fluksio.com/getting-started/data-science/>
+- Home: <https://fluksio.com>
+
+Python 3.10 or newer, Linux or macOS.

backend/pyproject.toml

@@ -2,7 +2,25 @@
 name = "fluksio"
 version = "0.1.0"
 description = "Node-based automation engine: flows, dashboards, batch runs"
+readme = "README.md"
 requires-python = ">=3.10"
+authors = [{ name = "Fluksio", email = "stroblme@posteo.de" }]
+keywords = ["automation", "workflow", "dataflow", "experiment-tracking", "mlops"]
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Intended Audience :: Developers",
+    "Intended Audience :: Science/Research",
+    "Operating System :: MacOS",
+    "Operating System :: POSIX :: Linux",
+    "Programming Language :: Python :: 3",
+    "Programming Language :: Python :: 3.10",
+    "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
+    "Programming Language :: Python :: 3.13",
+    "Topic :: Home Automation",
+    "Topic :: Scientific/Engineering",
+    "Topic :: System :: Distributed Computing",
+]
 dependencies = [
     "fastapi[standard]<1.0.0,>=0.114.2",
     "python-multipart<1.0.0,>=0.0.7",
@@ -32,6 +50,11 @@ dependencies = [
     "uv>=0.5",
 ]
 
+[project.urls]
+Homepage = "https://fluksio.com"
+Documentation = "https://docs.fluksio.com"
+"Getting started" = "https://docs.fluksio.com/getting-started/data-science/"
+
 [project.scripts]
 fluksio = "fluksio.cli:main"