AC rice-phenotype-prediction
Predict rice agronomic traits (yield, plant height, heading date, grain size, etc.) from genotype and environmental data using pre-trained MMoE deep learning models. Use when the user asks about rice phenotype prediction, crop trait estimation, genotype-environment interaction, or environmental stress effects on rice. Supports Chinese and English. Trigger terms: 水稻, 表型, 预测, 株高, 产量, 粒长, 抽穗期, 千粒重, 结实率, rice, phenotype, yield, trait, stress.
As a process C 53/100 · Has gaps — weak spots: result and completion, inputs and preconditions, consistency
How to improve
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 0
✓ No critical or high findings
Files scanned: 14. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 40Consistency. Frontmatter name (rice-phenotype-prediction) differs from the folder (gain)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 60Failures and branches. 2 branches
- 70When it triggers. States when to use, but not when not to
- 100Steps. 25 steps
- 100Execution cost. Instruction body is 1787 tokens
- 100Running it twice. No mutating operations
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 442: enough signal without eating the budget
- +4Structure: 25 headings
- +3Step-by-step instructions: 25 items
- +4Has examples (11 code blocks)
- +3All 7 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.