This release is a pre-release and may not be stable for production use.
analitiq-validator
The Analitiq artifact validator. It validates Analitiq JSON documents against the contract models — the same Pydantic models the published JSON Schemas are generated from — plus the cross-file and cross-document checks a single-document model cannot express, so authoring, the connector-builder plugin, and downstream consumers all enforce one contract with no drift. It is structured so per-kind validators slot in without touching each other.
Today it covers:
- authored single documents — connector, endpoint, and type-map files, plus
connection,stream, andpipelinedocuments; - pipeline bundles — the cross-document referential integrity of an assembled run (pipeline + streams + connections + connectors + endpoints).
Single-document validity (structure and every cross-field rule) is delegated
to TypeAdapter(...).validate_python from analitiq-contract-models
(analitiq.contracts). It runs offline — no schema fetch, no network. On top
of the models it adds only what a single-document model cannot express — the
connection / stream / pipeline kinds are pure model validation (the model IS the
whole contract), so analitiq-validate --document x.json validates every authored
kind and emits the same uniform {passed, findings[]}.
Connector-package cross-file checks:
- cross-file coverage — a connector ships a sibling
type-map.jsoncarrying the sections its kind calls for, and no other type-map name, and an API connector's read map covers every(native_type, arrow_type)its endpoint files declare; - filename ↔ id — an endpoint file is named
{endpoint_id}.json; - advisory warnings the contract tolerates — duplicate type-map rules, read patterns spelling a lowercase literal, regex natives spelling a container that renders a scalar, write-map vocabulary gaps.
Pipeline-bundle referential integrity (validate_pipeline_bundle(bundle)) —
a bundle is a mapping of already-parsed {pipeline, streams, connections, connectors, endpoints} documents, checked for internal consistency independent of
any storage or on-disk layout:
- every
pipeline.streams[]ref resolves to exactly one bundled stream document; - every referenced connection is present in the bundle;
- every stream
endpoint_ref.connection_idis one of the pipeline's connections; - every connection's
connector_idis present among the bundled connectors; - every
scope='connection'endpoint_ref resolves to a bundled endpoint document; - the bundle names a pipeline (has a
pipeline_id).
A pipeline whose status is active is additionally held to referencing at
least one runnable stream. Which statuses a pipeline may carry is the pipeline
model's answer, so no status is a finding of its own here.
It does not assume the documents were already contract-validated, so a missing
reference field (a connection naming no connector, a stream slot with no
endpoint_ref) is reported as an unresolved reference — while per-document shape
(field types, lengths, enums) remains each document model's own job. Stream and
connection refs match on their base form, so a {id}_v{n} versioned ref resolves
the document that declares the bare {id} (connector identities match whole).
In-memory requests
analitiq.validator.document_set grades documents handed over as text, never
read from a filesystem. validate_single_document, validate_package and
validate_workspace each take the request model naming that unit, from
analitiq.contracts.validation_requests; the model owns the request shape and
is where a malformed argument is refused. A document is graded as the kind the
request names, or the kind its location gives it inside a package or
workspace. Checks spanning documents run where every document they read is
held. That module's docstring is the contract; it is not restated here.
Rule cases
Some rules enforced by a cross-document check ship example document sets with
the package, each one a directory holding a bundle.json or a connector package
rooted at connector.json. A rule is not required to carry any, so the corpus
grades only the rules it holds cases for. rule_cases() loads them and case_mismatch(case)
grades one against the installed validator, returning None when the
validator agrees:
from analitiq.validator.rule_cases import case_mismatch, rule_cases
mismatches = [m for m in map(case_mismatch, rule_cases()) if m]
Install
pip install analitiq-validator
This pulls analitiq-contract-models (and pydantic) transitively.
Use
analitiq-validate --document definition/connector.json
analitiq-validate --document connection.json # connection / stream / pipeline work too
The kind is detected from the document's shape — no --kind flag. Point
--document at definition/connector.json to also trigger cross-file coverage:
it discovers the sibling type-map.json and
endpoints/*.json from the connector's directory. A connection / stream /
pipeline document is validated purely against its contract model. Validation
is always model-driven and offline — --document is the only flag.
Output is a JSON report ({"passed": bool, "findings": [...]}) on stdout; the
process exits non-zero exactly when passed is false — a fail finding at
severity: "error", or a notApplicable finding naming a rule that is (or,
naming none, might as well be) error-tier, since a check that could not run
gets no benefit of the doubt.
A pipeline bundle is assembled from many documents, so it is validated as a library call rather than from a single file:
from analitiq.validator import validate_pipeline_bundle
findings = validate_pipeline_bundle(
{
"pipeline": pipeline_doc,
"streams": stream_docs,
"connections": connection_docs,
"connectors": connector_ids, # the connector identities present
"endpoints": connection_endpoint_docs, # scope='connection', connection_id, endpoint_id
}
)
if any(f["severity"] == "error" for f in findings):
raise SystemExit(findings)
Source of truth
The canonical source is
analitiq-ai/claude-code-plugins,
under packages/validator/ — with the contract models beside it at
packages/contract-models/, and the public JSON Schemas rendered from those same
models by scripts/render_schemas.py.
The package is authored directly in the public analitiq.validator namespace;
it is staged verbatim at build time, not rendered from a private tree. Edit the
source there, not the installed copy.
Metadata
Release files for analitiq-validator 1.0.0rc27
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| analitiq_validator-1.0.0rc27.tar.gz | 74.1 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| analitiq_validator-1.0.0rc27-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 156.7 kB
Release files / analitiq_validator-1.0.0rc27.tar.gz
| Download URL | analitiq_validator-1.0.0rc27.tar.gz |
|---|---|
| Size | 74.1 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
7e3b5f7485611a8f5f1f15991da40fad5d2ff3c621e1d7a849d6847bc1471f68
|
|
BLAKE2b-256 checksum How to use checksums |
2ff40ddc5b80d980318de15c9f5341b5f9d301cbe5f3c7bbb397372884e421a4
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.
Transparency logRelease files / analitiq_validator-1.0.0rc27-py3-none-any.whl
| Download URL | analitiq_validator-1.0.0rc27-py3-none-any.whl |
|---|---|
| Size | 82.6 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
db9270714fdd4a2c909c3da168548226ff6b9e80f3fd32a899ce5ebd16c7ef8b
|
|
BLAKE2b-256 checksum How to use checksums |
daf2d64157b5d82cfd8d83d97874c8963ad7482ccb0eb13690691497372323df
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Sep 23, 2026.
Transparency log