agsy-data-and-model-evaluation
Use when evaluating the model and analyzing results for an Agricultural Systems (AgSy) manuscript so it survives expert systems review — independent model evaluation (observed vs. simulated, fit statistics), sensitivity and uncertainty analysis, and trade-off / scenario analysis across the system. Guides evaluation norms; it does not fabricate results or run the model.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill agsy-data-and-model-evaluation --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Data & Model Evaluation (agsy-data-and-model-evaluation) A model is only as credible as its **evaluation**. AgSy reviewers are systems-modelling experts: they want to see the model tested against **independent** data, its **sensitivity and uncertainty** characterized, and the **trade-offs** the system exhibits — not a single tuned run presented as truth. Model description and choice live in `agsy-systems-framing-and-modeling`; this skill covers testing and reporting. ## When to trigger - Reporting how well the model reproduces observations - Running sensitivity / uncertainty analysis - Building scenario comparisons and trade-off analyses - A reviewer asked for validation, sensitivity, uncertainty, or alternative scenarios ## Evaluation norms AgSy expects 1. **Independent evaluation.** Compare **observed vs. simulated** on data **not used for calibration**. Report standard fit statistics — **RMSE, RRMSE, bias/ME, modelling efficiency (NSE), index of agreement (d), R²** — and show the 1:1 plot. State what "good enough" means for the decision. 2. **Sensitivity analysis.** Identify the parameters/inputs that drive outputs (local one-at-a-time and, where feasible, global methods — **M
- When to trigger
- Evaluation norms AgSy expects
- Stochastic & data-driven components
- Reproducibility while you work (not at the end)
- Anti-patterns
- Evaluation completeness rubric (what an AgSy referee checks)
- Worked micro-example (illustrative numbers)
- Referee pushback → the AgSy-specific fix
- Calibration anchors (hedged where policy is volatile)
- Output format
- Supplementary resources
What does the agsy-data-and-model-evaluation skill do?
Use when evaluating the model and analyzing results for an Agricultural Systems (AgSy) manuscript so it survives expert systems review — independent model evaluation (observed vs. simulated, fit statistics), sensitivity and uncertainty analysis, and trade-off / scenario analysis across the system. Guides evaluation norms; it does not fabricate results or run the model.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill agsy-data-and-model-evaluation --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Awesome-Journal-Skills, a repository with 909 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.