Cases
A reference example, with clear limits.
Text Metrics is a working local service used to develop and evaluate Service Knowledge Kit. It is a project-owned example, not a customer deployment.
Text Metrics
The example gives a new engineer a concrete service to understand: purpose, maintainer responsibilities, runtime dependencies, configuration and a local HTTP interface.
It accepts caller-supplied text as JSON and computes UTF-8 byte size, character, word and line counts, and a SHA-256 digest. Responses depend on the actual input. The service runs on loopback, uses the Python standard library, and does not store the submitted text or make external calls.
How SK uses it
SK reads selected documentation and source files without starting the service. It creates a source-linked onboarding draft, checks local documentation references and surfaces missing or conflicting evidence for review. A separate managed document can receive reviewed updates and be restored to its previous version.
Evidence and limitations
Its source, English documentation, configuration and tests live in the separate SK development repository. Local checks have exercised changed inputs, invalid requests and the service boundaries. This site does not expose the private repository or present it as a public release.
The service itself counts text; SK assembles the documentation draft. The default local SK workflow uses a fixed local template, with no AI-generated answers. No customer results, time savings or production suitability are established by this example.
Plain project maintenance covers the example and its documentation. A public case with reproducible release links and an evaluation summary will require a separately reviewed release.