Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add CyrilLeMat/temper-skills --skill dog-daygit clone --depth 1 https://github.com/CyrilLeMat/temper-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cyrillemat/temper-skills/dog-day)<a href="https://agentmods.dev/skills/cyrillemat/temper-skills/dog-day"><img src="https://agentmods.dev/badge/skills/cyrillemat/temper-skills/dog-day/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cyrillemat/temper-skills/dog-day"><img src="https://agentmods.dev/badge/skills/cyrillemat/temper-skills/dog-day.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00063 | $0.00918 |
| Opus 5 | $0.00032 | $0.00459 |
| Sonnet 5 | $0.00013 | $0.00184 |
| Haiku 4.5 | $0.00006 | $0.00092 |
Grade A, and why
dog-day scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 12d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 46 lines — stays where its author put it; the contents beside it link to each section on GitHub.
dog-day — orchestrator (tempered by temper-skills)
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.
1. decide_walk — frozen
Extract 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})
- gray zone: measured temperature_c overrides the label — >30°C always skips; the 'heat' label only skips when temperature is unmeasured (None). snow/cold carry no branch (source is silent) and degrade to normal_walk.
- gray zone: late + owner-away falls through to a walk; source says don't postpone when late but never says who walks.
2. decide_meal — frozen
Chained: 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})
- gray zone: Evening skip keys on the whole-day had_full_meal_today (added via schema re-gate r1); last_meal_size=='full' is only the fallback when the flag is None. Overlap case [evening + already_full + just_exercised + minutes<30] resolves to treat_only, not wait_then_full_meal, because promising a full meal would violate the 'a treat at most' cap — the four-outcome set can't say 'wait, then a treat', so offer the treat once the dog settles.
- gray zone: Within 30 min of exercise (or unknown timing) rest first; None minutes_since_exercise is treated as still-resting (conservative, honors 'never feed within 30 min').
- gray zone: just_exercised is the governing exercise predicate; a stale minutes_since_exercise with just_exercised False is correctly ignored.
- gray zone: Ate nothing today -> a real meal, not the token light_meal default.
What ships with it
12 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/decide_meal.schema.py 880 B runs code
- assets/decide_meal.validation.jsonl 5.7 KB
- assets/decide_vet.schema.py 551 B runs code
- assets/decide_vet.validation.jsonl 4.3 KB
- assets/decide_walk.schema.py 750 B runs code
- assets/decide_walk.validation.jsonl 4.2 KB
- scripts/decide_meal.py 2.2 KB runs code
- scripts/decide_vet.py 1.2 KB runs code
- scripts/decide_walk.py 1.3 KB runs code
- scripts/test_decide_meal.py 5.0 KB runs code
- scripts/test_decide_vet.py 3.6 KB runs code
- scripts/test_decide_walk.py 3.8 KB runs code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 12d ago First seen · 46 lines · 63 tokens per session scan A ff253917aba6
dog-day is a skill published in the GitHub repository CyrilLeMat/temper-skills (4 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 63 tokens to every session and 918 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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