model-warmth-keeper

model-warmth-keeper is a skill for Claude Code, Codex from dailyaiagents-cpu/dailyai-os. It costs 58 tokens per session (1,903 once invoked), scanned A, original, MIT.

A scheduled service that sends small requests to selected local language models every four minutes during business hours. A language model is software that generates text or code; these requests keep models loaded and ready.

In plain words
What is it for?
Use it to keep specified Ollama and LM Studio models warm, skip overnight checks, record activity, and alert through Telegram after a prolonged outage.
Why use it?
It reduces delays caused by models shutting down while idle and needing time or memory to start again.

Skill for Claude CodeCodex

Which agent this was written for is unclear — built for openclaw. Also seen: built for openclaw.

Good fit Use it to keep specified Ollama and LM Studio models warm, skip overnight checks, record activity, and alert through Telegram after a prolonged outage.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/dailyaiagents-cpu/dailyai-os/model-warmth-keeper
Install

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.

Any agent
npx skills add dailyaiagents-cpu/dailyai-os --skill model-warmth-keeper
Clone the repo
git clone --depth 1 https://github.com/dailyaiagents-cpu/dailyai-os

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for model-warmth-keeper

README.md
[![agentmods](https://agentmods.dev/badge/skills/dailyaiagents-cpu/dailyai-os/model-warmth-keeper/github.svg)](https://agentmods.dev/skills/dailyaiagents-cpu/dailyai-os/model-warmth-keeper)
Your own site
<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.

agentmods 80×15 button for model-warmth-keeper

Your own site · 80×15
<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>
Per session 58 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,903 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 2 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00058 $0.01903
Opus 5.5 $0.00023 $0.00761
Sonnet 5.5 $0.00012 $0.00381
Haiku 4.5 $0.00006 $0.00190

Measured 2mo ago against content hash d27d3195c2b1, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

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 2mo 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([
skills/model-warmth-keeper/SKILL.md · 171 lines

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))

Read the full file on GitHub · 171 lines

Files

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.

Changes

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.

  1. 2mo ago First seen · 171 lines · 58 tokens per session scan A d27d3195c2b1

Subscribe to this mod's changes

model-warmth-keeper is a skill published in the GitHub repository dailyaiagents-cpu/dailyai-os (0 stars, last pushed 6mo ago), licensed MIT. It adds 58 tokens to every session and 1,903 once invoked, about $0.0002 per session on Opus 5.5. 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.

Related

model-warmth-keeper compared with skills from other repositories

model-warmth-keeper next to 6 skills from other repositories
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model-warmth-keeperdailyaiagents-cpu/dailyai-osSkill06mo agoMITA58Claude Code, Codex
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