Context Governance

The skill requires incremental context construction, forbids history rewriting, imposes a 10K-token item cap, and requires ContextualUserFragment structs, showing some boundary and capacity controls. It does not specify user confirmation, sensitive-data handling, isolation, data-flow disclosure, rollback, or external-effect controls, so points are deducted.

The rules are broadly consistent and include hard limits, but there is no implementation, dependency guidance, abnormal-input handling, failure feedback, or key-path test evidence. The score remains within the static-review ceiling.

The name and body identify model-visible context as the target area, but inputs, outputs, trigger conditions, non-fit boundaries, Chinese-language support, and environment fit are unspecified. This limits accurate invocation.

The file is concise and readable with explicit rules, but lacks installation or dependency notes, examples, FAQs, known limitations, versioning, changelog, maintenance ownership, and update instructions. The repository license and stated OpenAI provenance provide governance context but do not fill these skill-level gaps.

The rules address context growth, cache misses, item-size limits, and structural integration, so they may provide engineering value. However, no implementation or verifiable output is supplied, so task completion cannot be established and substantial engineering work remains.

The text is auditable as a static policy, but contains no test suite, CI coverage, third-party execution evidence, or corroborating sources. The assessment therefore relies on a single file and receives a very low score.