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 dailyaiagents-cpu/dailyai-os --skill model-warmth-keepergit clone --depth 1 https://github.com/dailyaiagents-cpu/dailyai-osWrote 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/dailyaiagents-cpu/dailyai-os/model-warmth-keeper)<a href="https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/model-warmth-keeper"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/model-warmth-keeper/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/dailyaiagents-cpu/dailyai-os/model-warmth-keeper"><img src="https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/model-warmth-keeper.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 | $0.00058 | $0.01903 |
| Opus 5 | $0.00029 | $0.00951 |
| Sonnet 5 | $0.00012 | $0.00381 |
| Haiku 4.5 | $0.00006 | $0.00190 |
Grade A, and why
model-warmth-keeper scanned grade A with 2 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
import urllib.request, json, time, pathlib Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run([ How it starts
The opening of the file, as written. The whole thing — 171 lines — stays where its author put it; the contents beside it link to each section on GitHub.
model-warmth-keeper
Why this exists
2026-04-28 NIGHT-1 run logged: Ollama qwen3.6/qwen3.5 hit llm-idle-timeout watchdog. Models eviction-time out during idle, next dispatch waits 30+s for warmup, occasionally that warmup itself OOMs because two models race for memory. This skill keeps the primary specialists warm with cheap 1-token pings.
Models pinged
| Provider | Endpoint | Models |
|---|---|---|
| Ollama | http://127.0.0.1:11434/api/generate | qwen3.6:latest, qwen3.5:latest, qwen3.5:27B |
| LM Studio | http://127.0.0.1:1234/v1/chat/completions | qwen/qwen3-coder-next |
Procedure
Step 1 — Time-of-day guard
import datetime
hour = datetime.datetime.now().hour
# Skip pings during 00:00–05:59 CT (Mac is set to America/Chicago).
# That's "off hours" — pings can be skipped to save battery / power.
if hour < 6:
print("[warmth-keeper] off-hours, skipping ping cycle")
raise SystemExit(0)
Step 2 — Ping each model
import urllib.request, json, time, pathlib
LOG = pathlib.Path.home() / ".openclaw/logs/warmth-keeper.log"
LOG.parent.mkdir(parents=True, exist_ok=True)
def log(msg):
with open(LOG, "a") as f:
f.write(f"{datetime.datetime.utcnow().isoformat()}Z {msg}\n")
OLLAMA_MODELS = ["qwen3.6:latest", "qwen3.5:latest", "qwen3.5:27B"]
LMSTUDIO_MODELS = ["qwen/qwen3-coder-next"]
def ping_ollama(model):
body = json.dumps({"model": model, "prompt": "OK", "stream": False, "options": {"num_predict": 1}}).encode()
req = urllib.request.Request("http://127.0.0.1:11434/api/generate", data=body,
headers={"Content-Type": "application/json"}, method="POST")
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=15) as r:
r.read()
return ("ok", round(time.time() - t0, 3), None)
except Exception as e:
return ("fail", round(time.time() - t0, 3), str(e)[:120])
def ping_lmstudio(model):
body = json.dumps({
"model": model,
"messages": [{"role": "user", "content": "OK"}],
"max_tokens": 1,
"stream": False
}).encode()
req = urllib.request.Request("http://127.0.0.1:1234/v1/chat/completions", data=body,
headers={"Content-Type": "application/json"}, method="POST")
t0 = time.time()
try:
with urllib.request.urlopen(req, timeout=15) as r:
r.read()
return ("ok", round(time.time() - t0, 3), None)
except Exception as e:
return ("fail", round(time.time() - t0, 3), str(e)[:120])
failures = []
for m in OLLAMA_MODELS:
status, dt, err = ping_ollama(m)
log(f"ollama {m} {status} {dt}s {err or ''}")
if status == "fail":
failures.append(("ollama", m, err))
for m in LMSTUDIO_MODELS:
status, dt, err = ping_lmstudio(m)
log(f"lmstudio {m} {status} {dt}s {err or ''}")
if status == "fail":
failures.append(("lmstudio", m, err))
What ships with it
1 file 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.
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.
- 10d ago First seen · 171 lines · 58 tokens per session scan A d27d3195c2b1
model-warmth-keeper is a skill published in the GitHub repository dailyaiagents-cpu/dailyai-os (0 stars, last pushed 4mo ago), licensed MIT. It adds 58 tokens to every session and 1,903 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 2 findings (makes network calls, runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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