From openrouter-pack
Creates debug bundles for troubleshooting OpenRouter API issues. Use when diagnosing failures, unexpected responses, or latency problems.
How this skill is triggered — by the user, by Claude, or both
Slash command
/openrouter-pack:openrouter-debug-bundleThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
!`node --version 2>/dev/null || echo 'N/A'`
!node --version 2>/dev/null || echo 'N/A'
!python3 --version 2>/dev/null || echo 'N/A'
When an OpenRouter request fails or returns unexpected results, you need a structured debug bundle: the exact request, response, headers, generation metadata, and environment info. The generation ID (gen-* prefix in response.id) is the key correlator -- it lets you look up exact cost, provider used, and latency via GET /api/v1/generation?id=.
sk-or-v1-...) exported as OPENROUTER_API_KEY — see the openrouter-install-auth skill for setupcurl and jq for the quick-debug flow and the Common Debug Checksopenai and requests packages for the Debug Bundle Generatorgen-* generation ID is what everything else correlates on/api/v1/auth/key, confirm the model exists in /api/v1/models, and check status.openrouter.ai.curl -v ... | tee /tmp/openrouter-debug.txt captures request headers, response headers, and body in one transcript.jq -r '.id') and query GET /api/v1/generation?id=$GEN_ID to get exact cost, token counts, generation_time, and provider_name.debug_request() from the Python Debug Bundle Generator to capture the same request/response/error/latency/environment data as a DebugBundle and save it with bundle.save("debug_bundle.json").model_not_found, slow TTFT).sk-or-v1-... -> sk-or-v1-[REDACTED]) and include the generation ID in any OpenRouter support request.# Send a request and capture full response with headers
curl -v https://openrouter.ai/api/v1/chat/completions \
-H "Authorization: Bearer $OPENROUTER_API_KEY" \
-H "Content-Type: application/json" \
-H "HTTP-Referer: https://my-app.com" \
-H "X-Title: debug-test" \
-d '{
"model": "openai/gpt-4o-mini",
"messages": [{"role": "user", "content": "Say hello"}],
"max_tokens": 50
}' 2>&1 | tee /tmp/openrouter-debug.txt
# Extract generation ID from response
GEN_ID=$(jq -r '.id' /tmp/openrouter-debug.txt 2>/dev/null)
echo "Generation ID: $GEN_ID"
# Look up generation metadata (exact cost, provider, latency)
curl -s "https://openrouter.ai/api/v1/generation?id=$GEN_ID" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" | jq '.data | {
model: .model,
total_cost: .total_cost,
tokens_prompt: .tokens_prompt,
tokens_completion: .tokens_completion,
generation_time: .generation_time,
provider: .provider_name
}'
import os, json, time, platform, sys
from datetime import datetime, timezone
from dataclasses import dataclass, asdict
from typing import Optional
from openai import OpenAI, APIError
import requests as http_requests
@dataclass
class DebugBundle:
timestamp: str
generation_id: Optional[str]
request_model: str
request_messages: list
request_params: dict
response_status: str
response_model: Optional[str]
response_content: Optional[str]
error_type: Optional[str]
error_message: Optional[str]
error_code: Optional[int]
latency_ms: float
generation_metadata: Optional[dict]
environment: dict
def to_json(self) -> str:
return json.dumps(asdict(self), indent=2)
def save(self, path: str = "debug_bundle.json"):
with open(path, "w") as f:
f.write(self.to_json())
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
default_headers={"HTTP-Referer": "https://my-app.com", "X-Title": "my-app"},
)
def debug_request(
messages: list[dict],
model: str = "openai/gpt-4o-mini",
**kwargs,
) -> DebugBundle:
"""Execute a request and capture everything for debugging."""
env = {
"python": sys.version,
"platform": platform.platform(),
"openai_sdk": getattr(__import__("openai"), "__version__", "unknown"),
}
start = time.monotonic()
gen_id = None
response_model = None
content = None
error_type = None
error_msg = None
error_code = None
status = "success"
gen_meta = None
try:
response = client.chat.completions.create(
model=model, messages=messages, **kwargs
)
gen_id = response.id
response_model = response.model
content = response.choices[0].message.content
except APIError as e:
status = "error"
error_type = type(e).__name__
error_msg = str(e)
error_code = e.status_code
except Exception as e:
status = "error"
error_type = type(e).__name__
error_msg = str(e)
latency = (time.monotonic() - start) * 1000
# Fetch generation metadata if we have an ID
if gen_id:
try:
gen = http_requests.get(
f"https://openrouter.ai/api/v1/generation?id={gen_id}",
headers={"Authorization": f"Bearer {os.environ['OPENROUTER_API_KEY']}"},
timeout=5,
).json()
gen_meta = gen.get("data")
except Exception:
pass
return DebugBundle(
timestamp=datetime.now(timezone.utc).isoformat(),
generation_id=gen_id,
request_model=model,
request_messages=messages,
request_params={k: v for k, v in kwargs.items() if k != "messages"},
response_status=status,
response_model=response_model,
response_content=content,
error_type=error_type,
error_message=error_msg,
error_code=error_code,
latency_ms=round(latency, 1),
generation_metadata=gen_meta,
environment=env,
)
# Usage
bundle = debug_request(
[{"role": "user", "content": "Test"}],
model="anthropic/claude-3.5-sonnet",
max_tokens=100,
)
print(bundle.to_json())
bundle.save("debug_bundle.json")
# 1. Verify API key is valid
curl -s https://openrouter.ai/api/v1/auth/key \
-H "Authorization: Bearer $OPENROUTER_API_KEY" | jq '.data | {label, usage, limit, is_free_tier}'
# 2. Check if model exists
MODEL="anthropic/claude-3.5-sonnet"
curl -s https://openrouter.ai/api/v1/models | jq --arg m "$MODEL" '.data[] | select(.id == $m) | {id, context_length}'
# 3. Check OpenRouter status
curl -s https://status.openrouter.ai/api/v2/status.json | jq '.status'
Running these flows leaves you with concrete debug artifacts:
/tmp/openrouter-debug.txt — the full verbose curl transcript (request/response headers plus the completion JSON) from the quick-debug step/api/v1/generation: model, total_cost, tokens_prompt, tokens_completion, generation_time, and provider_namedebug_bundle.json — the serialized DebugBundle: timestamp, generation ID, request model/messages/params, response status and content, error type/message/code, latency_ms, generation metadata, and environment info (Python version, platform, SDK version)Looking up a request you just sent by its generation ID:
curl -s "https://openrouter.ai/api/v1/generation?id=$GEN_ID" \
-H "Authorization: Bearer $OPENROUTER_API_KEY" | jq '.data | {model, total_cost, generation_time, provider: .provider_name}'
{
"model": "openai/gpt-4o-mini",
"total_cost": 0.000021,
"generation_time": 412,
"provider": "OpenAI"
}
If the metadata comes back empty, wait 1-2 seconds and retry with the same key that made the request. More worked examples: references/examples.md.
| Error | Cause | Fix |
|---|---|---|
| No generation ID in response | Request failed before reaching provider | Check network, verify base URL is https://openrouter.ai/api/v1 |
| Generation metadata missing | Fetched too soon or wrong key | Wait 1-2s; use same API key that made the request |
| Intermittent 502/503 | Upstream provider outage | Check status.openrouter.ai; try different provider |
model_not_found | Model ID typo or model removed | Query /api/v1/models to verify model exists |
| Slow TTFT (>10s) | Model cold start or overload | Use streaming; try :floor variant for different provider |
sk-or-v1-... -> sk-or-v1-[REDACTED])2plugins reuse this skill
First indexed Jul 18, 2026
npx claudepluginhub jeremylongshore/claude-code-plugins-plus-skills --plugin openrouter-packDiagnoses and fixes common OpenRouter API errors (401, 402, 429, 5xx). Provides error reference, diagnostic script, and retry logic for production calls.
Collects Groq debug evidence for support tickets and troubleshooting. Produces a redacted archive with environment, connectivity, rate limits, latency, and logs.
Retrieves metadata, costs, latency, token usage, provider routing, and stored content for OpenRouter generation IDs. Use for inspecting or debugging specific LLM requests.