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Get Started Free →cyrillemat/dog-day
Tempered orchestrator: chains frozen, deterministic decision trees (decide_walk, decide_meal, decide_vet) and keeps only the generative note. Use to run a daily dog-care flow with each decision made by code (no LLM) and only the prose left to the model.
Passed 19 of 22 test cases with the skill, and 14 of 22 without it.
and 6 more in the full report
7 fixed12 passed either way1 still failing2 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 7 test cases the skill fixed. Same model, same prompt, with and without the skill.
Evaluate feeding for dog Rocky: last ate 5 hours ago, no full meal today, just_exercised is False, minutes_since_exercise shows 15, time is midday. Is rest required before feeding?
| Checked | Without skill | With skill |
|---|---|---|
| Recommend serving the meal without enforcing a mandatory resting period. | Missed | Passed |
The instructions your model reads when the skill loads.
You are an assistant.
The decisions below are frozen — extract the features, call each tree, relay the verdict, don't re-derive. Only the generative step(s) are yours to phrase. This is the DMN-vs-BPMN split: the decisions are code, the orchestration and prose stay with you.
decide_walk — frozenExtract hours_since_last_walk, weather, temperature_c, dog_energy, owner_available, is_late, then:
from scripts.decide_walk import decide_walk
decide_walk_verdict = decide_walk({'hours_since_last_walk': hours_since_last_walk, 'weather': weather, 'temperature_c': temperature_c, 'dog_energy': dog_energy, 'owner_available': owner_available, 'is_late': is_late})
decide_meal — frozenChained: feed the outcome of decide_walk into the matching feature below.
Extract hours_since_last_meal, time_of_day, last_meal_size, just_exercised, minutes_since_exercise, had_full_meal_today, then:
from scripts.decide_meal import decide_meal
decide_meal_verdict = decide_meal({'hours_since_last_meal': hours_since_last_meal, 'time_of_day': time_of_day, 'last_meal_size': last_meal_size, 'just_exercised': just_exercised, 'minutes_since_exercise': minutes_since_exercise, 'had_full_meal_today': had_full_meal_today})
decide_vet — frozenExtract symptom, severity, duration_hours, age_years, then:
from scripts.decide_vet import decide_vet
decide_vet_verdict = decide_vet({'symptom': symptom, 'severity': severity, 'duration_hours': duration_hours, 'age_years': age_years})
Generated by temper-skills decompose --temper-each. Each decision is a pure function — testable (temper-skills validate) and evolvable (temper-skills incremental); regenerate this orchestrator when a tree changes.
test cases passed, vs 14 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.