parameter-db-formatter

To format an SP-STM parameter database in Obsidian, apply required frontmatter validation, enforce module presence, and verify enums like measurement_mode. It transforms scattered parameter notes into a consistent digital-twin record with cross-material summaries.

A cross-material parameter quick table is generated by validating standard parameters across materials and measurement modes, producing a digital-twin parameter intelligence output. The skill automatically derives this table from your existing parameter library notes.

Valid_under context checking blocks AI parameter recommendations when the current experimental environment violates defined valid_under constraints. It ensures context safety by explaining the specific mismatch between the environment and the required boundaries.

Yes, you can create a complete parameter database from scratch by providing a user description of the materials and measurement modes. The skill derives and generates the full record while strictly forbidding the fabrication of statistics and sample sizes.

A failure-risk map is a digital-twin parameter intelligence output that links sensitivity boundaries to specific Issue records. It identifies potential failure points in your SP-STM parameter database and connects them to documented problems.

Yes, the parameter database formatter validates the AI recommendation format by checking success rates tied to sample size and confidence intervals. This ensures statistical reliability within your SP-STM parameter library.