Build check model availability and implement fallback chains. Use when building resilient systems or handling model outages. Trigger with phrases like 'openrouter availability', 'openrouter fallback', 'openrouter model down', 'openrouter health check'.
npxskills add jeremylongshore/claude-code-plugins-plus-skills--skill openrouter-model-availabilityLoading…
OpenRouter's /api/v1/models endpoint is the source of truth for model availability. Models can be temporarily unavailable, have degraded performance, or be permanently removed. This skill covers querying model status, building health probes, tracking availability over time, and automating failover.
OPENROUTER_API_KEY for live probes (the catalog query itself needs no auth) — see the openrouter-install-auth skill for setupcurl and jq for the catalog status queries and the cron monitoring scriptrequests (pip install openai requests) for the health-check servicemax_tokens: 1 probe costs roughly $0.0001curl -s https://openrouter.ai/api/v1/models | jq ... per Query Model Status — pull context_length and per-million pricing without spending any tokens.check_model_exists() from Catalog-Based Availability Check; on a miss it calls find_similar() to suggest same-provider replacements.probe_model() from Health Check Service — a max_tokens: 1 request that returns a HealthStatus with available, latency_ms, and checked_at.check_critical_models(), which logs OK/FAIL plus latency per model.*/5 * * * * cron job appending timestamped status lines to /var/log/openrouter-health.log.# Check if specific models exist and their status
curl -s https://openrouter.ai/api/v1/models | jq '[.data[] | select(
.id == "anthropic/claude-3.5-sonnet" or
.id == "openai/gpt-4o" or
.id == "openai/gpt-4o-mini"
) | {
id,
context_length,
prompt_per_M: ((.pricing.prompt | tonumber) * 1000000),
completion_per_M: ((.pricing.completion | tonumber) * 1000000)
}]'
# List all available models (just IDs)
curl -s https://openrouter.ai/api/v1/models | jq '[.data[].id] | sort'
# Count models by provider
curl -s https://openrouter.ai/api/v1/models | jq '[.data[].id | split("/")[0]] | group_by(.) | map({provider: .[0], count: length}) | sort_by(-.count)'
import os, time, logging
from datetime import datetime, timezone
from dataclasses import dataclass
import requests
from openai import OpenAI, APIError, APITimeoutError
log = logging.getLogger("openrouter.health")
@dataclass
class HealthStatus:
model: str
available: bool
latency_ms: float
checked_at: str
error: str = ""
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
timeout=15.0,
default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "health-check"},
)
def probe_model(model_id: str) -> HealthStatus:
"""Send a minimal request to test model availability."""
start = time.monotonic()
try:
response = client.chat.completions.create(
model=model_id,
messages=[{"role": "user", "content": "hi"}],
max_tokens=1, # Minimal cost
)
latency = (time.monotonic() - start) * 1000
return HealthStatus(
model=model_id, available=True, latency_ms=round(latency, 1),
checked_at=datetime.now(timezone.utc).isoformat(),
)
except (APIError, APITimeoutError) as e:
latency = (time.monotonic() - start) * 1000
return HealthStatus(
model=model_id, available=False, latency_ms=round(latency, 1),
checked_at=datetime.now(timezone.utc).isoformat(),
error=str(e),
)
def check_critical_models() -> list[HealthStatus]:
"""Probe all critical models."""
CRITICAL_MODELS = [
"anthropic/claude-3.5-sonnet",
"openai/gpt-4o",
"openai/gpt-4o-mini",
"google/gemini-2.0-flash-001",
]
results = []
for model in CRITICAL_MODELS:
status = probe_model(model)
log.info(f"{'OK' if status.available else 'FAIL'} {model} ({status.latency_ms}ms)")
results.append(status)
return results
def check_model_exists(model_id: str) -> dict:
"""Check if a model exists in the catalog (no API call cost)."""
resp = requests.get("https://openrouter.ai/api/v1/models")
models = {m["id"]: m for m in resp.json()["data"]}
if model_id in models:
m = models[model_id]
return {
"exists": True,
"context_length": m["context_length"],
"pricing": m["pricing"],
}
return {"exists": False, "suggestion": find_similar(model_id, models)}
def find_similar(model_id: str, models: dict) -> list[str]:
"""Find models with similar names (for migration when model is removed)."""
prefix = model_id.split("/")[0]
return [m for m in models if m.startswith(prefix)][:5]
#!/bin/bash
# Run as cron job: */5 * * * * /path/to/check_models.sh
MODELS=("anthropic/claude-3.5-sonnet" "openai/gpt-4o" "openai/gpt-4o-mini")
LOG_FILE="/var/log/openrouter-health.log"
for MODEL in "${MODELS[@]}"; do
START=$(date +%s%N)
HTTP_CODE=$(curl -s -o /dev/null -w "%{http_code}" \
https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-d "{\"model\":\"$MODEL\",\"messages\":[{\"role\":\"user\",\"content\":\"ping\"}],\"max_tokens\":1}" \
--max-time 15)
END=$(date +%s%N)
LATENCY=$(( (END - START) / 1000000 ))
STATUS="OK"
[ "$HTTP_CODE" != "200" ] && STATUS="FAIL"
echo "$(date -u +%Y-%m-%dT%H:%M:%SZ) $STATUS $MODEL $HTTP_CODE ${LATENCY}ms" >> "$LOG_FILE"
done
HealthStatus records per probed model: available, latency_ms, an ISO-8601 checked_at timestamp, and the error string when a model is down{"exists": True, "context_length": ..., "pricing": ...} on a hit, or {"exists": False, "suggestion": [...]} listing similar model IDs on a miss2026-07-02T14:05:01Z OK anthropic/claude-3.5-sonnet 200 842ms, one per critical model every 5 minutesCheck that a critical model is still in the catalog before spending tokens on a probe:
curl -s https://openrouter.ai/api/v1/models | jq '[.data[] | select(
.id == "anthropic/claude-3.5-sonnet") | {id, context_length}]'
# [{"id": "anthropic/claude-3.5-sonnet", "context_length": 200000}]
Then run the Python health sweep — run_health_checks() in references/examples.md prints [OK] anthropic/claude-3.5-sonnet: 842.3ms per model, a 3/3 models healthy summary, and the mapped fallback (e.g. openai/gpt-4-turbo) for any failure. More worked examples: references/examples.md.
| Error | Cause | Fix |
|---|---|---|
| Model not in catalog | Model renamed or removed | Use find_similar() to find replacement |
| Health check timeout (>15s) | Model overloaded or cold-starting | Distinguish slow vs down; increase timeout for probes |
| False positive down | Transient network issue | Require 2-3 consecutive failures before alerting |
| 402 on health check | Credits exhausted | Health checks cost ~$0.0001 each; ensure adequate credits |
max_tokens: 1) -- budget for monitoring/api/v1/models on every request
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Create or update AgentSkills. Use when designing, structuring, or packaging skills with scripts, references, and assets.
Use CodexBar CLI local cost usage to summarize per-model usage for Codex or Claude, including the current (most recent) model or a full model breakdown. Trigger when asked for model-level usage/cost data from codexbar, or when you need a scriptable per-model summary from codexbar cost JSON.
Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in (single or multi-account), or reading/injecting/running secrets via op.