neqsim-model-calibration-and-data-reconciliation
neqsim-model-calibration-and-data-reconciliation is a Physics, mathematics & simulation skill for AI agents, published by equinor in the neqsim repository on GitHub. Skillspace rates neqsim-model-calibration-and-data-reconciliation D (68/100) and it passed the safety scan; the rating is based on static analysis only.
Model calibration and data reconciliation workflow for NeqSim digital twins. USE WHEN: reducing model-vs-plant mismatch, tuning process model parameters from historian data, reconciling noisy measurements, or building validation reports for calibrated simulations. Covers tag mapping, data quality gates, steady-state window selection, bounded optimization, residual diagnostics, and results.json reporting.
- Grade
- D
- Quality
- 68 / 100
- Safety
- Passed
- GitHub stars
- 132
Unproven: rated by static analysis only, not yet compared head-to-head.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 100 / 100
- Maturity
- Basic: a single SKILL.md
- Security scan
- No issues found
- License
- Apache-2.0
- Adoption
- Adopted by 0 other repositories, in 0 collections
Not checked yet
- Not judged on the rubric yet (not a contender in its category)
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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