mcp-engine-semantic-authoring — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited mcp-engine-semantic-authoring (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
Semantic-layer work through manage_semantic. Read only the references needed for the current task.
manage_schema create_calc_column operation covered by mcp-engine-schema-authoring.list_model { "operation": "list", "spec": { "type": "measures" } } (repeat with "tables"; spec.type takes one value per call), and check for an existing equivalent with { "operation": "search", "spec": { "query": "<name>", "mode": "name" } } before creating a near-duplicate measure.create_measure, update_measure, create_kpi): measure-authoring-guide. When logic placement is unclear: modeling-best-practices-guide.create_calc_group): calc-groups-guide. DAX UDFs (create_udf): dax-udf-guide.create_named_expression): named-expressions-guide. Power Query parameters (create_pq_parameter, RangeStart/RangeEnd): power-query-parameters-guide.run_query { "operation": "execute", "query": "<query evaluating the measure>" } in a realistic filter context.dry_run: true and per-item identifiers for bulk semantic edits.After semantic work, report:
dry_run or bulk mode was used and the outcome.~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.