skills/model-debugging/model-debugging/SKILL.md
Debug and diagnose model errors in Pollinations services. Analyze logs, find error patterns, identify affected users. For taking action on user tiers, see tier-management skill.
npx skillsauth add aiagentskills/skills model-debuggingInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Use this skill when:
Related skill: Use tier-management to upgrade users or check balances after identifying issues here.
Why does the Model Monitor show high error rates when models work fine manually?
The Model Monitor at https://monitor.pollinations.ai shows all real-world traffic, including:
openai-audio without modalities param)When you test manually with a valid secret key (sk_), you bypass auth/quota issues, so models appear to work fine.
Key insight: High 401/402/403/400 rates are expected from real-world usage. Focus investigation on 500/504 errors.
User Request → enter.pollinations.ai (Cloudflare Worker)
↓
Logs to Cloudflare Workers Observability
↓
Events stored in D1 database
↓
Batched to Tinybird (async, 100-500 events)
↓
Model Monitor queries Tinybird (model_health.pipe)
Structured Logging: enter.pollinations.ai uses LogTape with:
requestId: Unique per request (passed to downstream via x-request-id header)status, body: Full error response from downstream servicesmethod, routePath, userAgent, ipAddressView current model health at: https://monitor.pollinations.ai
# Via enter.pollinations.ai worker (requires wrangler)
cd enter.pollinations.ai
npx wrangler d1 execute pollinations-db --remote --command "SELECT model_requested, response_status, error_message, COUNT(*) as count FROM event WHERE response_status >= 400 AND created_at > datetime('now', '-1 hour') GROUP BY model_requested, response_status, error_message ORDER BY count DESC LIMIT 20"
cd enter.pollinations.ai
wrangler tail --format json | tee logs.jsonl
# Or with formatting:
wrangler tail --format json | npx tsx scripts/format-logs.ts
# Real-time logs
ssh enter-services "sudo journalctl -u image-pollinations.service -f"
# Last 3 minutes
ssh enter-services "sudo journalctl -u image-pollinations.service --since '3 minutes ago' --no-pager" > image-service-logs.txt
# Recent errors only
ssh enter-services "sudo journalctl -u image-pollinations.service -p err -n 50"
# Real-time logs
ssh enter-services "sudo journalctl -u text-pollinations.service -f"
# Last 3 minutes
ssh enter-services "sudo journalctl -u text-pollinations.service --since '3 minutes ago' --no-pager" > text-service-logs.txt
Error: getaddrinfo ENOTFOUND gptimagemain1-resource.cognitiveservices.azure.com
Cause: Azure Content Safety resource deleted or misconfigured
Impact: Fail-open (content proceeds without safety check)
Fix: Create new Azure Content Safety resource and update .env:
AZURE_CONTENT_SAFETY_ENDPOINT=https://<new-resource>.cognitiveservices.azure.com/
AZURE_CONTENT_SAFETY_API_KEY=<new-key>
Error: Content rejected due to sexual/hate/violence content detection
Cause: Azure's content moderation blocking prompts/images
Impact: 400 error returned to user
Fix: User error - prompt violates content policy
Error: Provided image is not valid
Cause: User passing unsupported image URL (e.g., Google Drive links)
Impact: 400 error returned to user
Fix: User error - need direct image URL
Error: No active translate servers available
Cause: Translation service unavailable
Impact: Prompts not translated (non-fatal)
Fix: Check translation service status
Error: Invalid value for audio.voice
Cause: User requesting unsupported voice name
Impact: 400 error returned to user
Fix: User error - use supported voices: alloy, echo, fable, onyx, nova, shimmer, coral, verse, ballad, ash, sage, etc.
Error: No video data in response
Cause: Vertex AI returned empty video response
Impact: 500 error
Fix: Check Vertex AI quota/status, may be transient
ssh enter-services "cat /home/ubuntu/pollinations/image.pollinations.ai/.env | grep -E 'AZURE|GOOGLE|CLOUDFLARE'"
Key variables:
AZURE_CONTENT_SAFETY_ENDPOINT - Azure Content Safety API endpointAZURE_CONTENT_SAFETY_API_KEY - Azure Content Safety API keyGOOGLE_PROJECT_ID - Google Cloud project for Vertex AIAZURE_MYCELI_FLUX_KONTEXT_ENDPOINT - Azure Kontext model endpointssh enter-services "cat /home/ubuntu/pollinations/text.pollinations.ai/.env | grep -E 'AZURE|OPENAI|GOOGLE'"
Secrets are stored encrypted with SOPS:
image.pollinations.ai/secrets/env.jsontext.pollinations.ai/secrets/env.jsonTo update:
# Decrypt, edit, re-encrypt
sops image.pollinations.ai/secrets/env.json
# Deploy to server
sops --output-type dotenv -d image.pollinations.ai/secrets/env.json > /tmp/image.env
scp /tmp/image.env enter-services:/home/ubuntu/pollinations/image.pollinations.ai/.env
rm /tmp/image.env
# Restart service
ssh enter-services "sudo systemctl restart image-pollinations.service"
# Count errors by type
grep -i "error" image-service-logs.txt | grep -oE "(Azure Flux Kontext|Vertex AI|No active translate|getaddrinfo ENOTFOUND)" | sort | uniq -c | sort -rn
# Find content filter rejections
grep -i "Content rejected" image-service-logs.txt | sort | uniq -c
# Check DNS resolution on server
ssh enter-services "nslookup gptimagemain1-resource.cognitiveservices.azure.com"
| Model | Backend | Common Issues |
|-------|---------|---------------|
| flux | Azure/Replicate | Rate limits, content filter |
| kontext | Azure Flux Kontext | Content filter (strict) |
| nanobanana | Vertex AI Gemini | Invalid image URLs, content filter |
| seedream-pro | ByteDance ARK | NSFW filter, API key issues |
| veo | Vertex AI | Quota, empty responses |
| openai-audio | Azure OpenAI | Invalid voice names |
| deepseek | DeepSeek API | Rate limits, API key |
The enter.pollinations.ai worker has structured logging enabled. You can query logs programmatically via the Cloudflare Workers Observability API.
# From wrangler.toml
grep account_id enter.pollinations.ai/wrangler.toml
# Or from existing .env
grep CLOUDFLARE_ACCOUNT_ID image.pollinations.ai/.env
Via Cloudflare Dashboard:
Workers Observability ReadThe token is stored in SOPS-encrypted secrets:
enter.pollinations.ai/secrets/env.jsonCLOUDFLARE_OBSERVABILITY_TOKENTo add/update:
# Step 1: Decrypt to temp file
cd /path/to/pollinations
sops -d enter.pollinations.ai/secrets/env.json > /tmp/env.json
# Step 2: Add the token (use jq)
jq '. + {"CLOUDFLARE_OBSERVABILITY_TOKEN": "your_token"}' /tmp/env.json > /tmp/env_updated.json
# Step 3: Re-encrypt (must rename to match .sops.yaml pattern)
cp /tmp/env_updated.json /tmp/env.json
sops -e /tmp/env.json > enter.pollinations.ai/secrets/env.json
# Step 4: Cleanup
rm /tmp/env.json /tmp/env_updated.json
# Verify
sops -d enter.pollinations.ai/secrets/env.json | jq 'keys'
Note: The .sops.yaml config requires filenames matching env.json$ pattern.
POST https://api.cloudflare.com/client/v4/accounts/{account_id}/workers/observability/telemetry/query
# Extract credentials from encrypted secrets
ACCOUNT_ID=$(sops -d enter.pollinations.ai/secrets/env.json | jq -r '.CLOUDFLARE_ACCOUNT_ID')
API_TOKEN=$(sops -d enter.pollinations.ai/secrets/env.json | jq -r '.CLOUDFLARE_OBSERVABILITY_TOKEN')
This endpoint works and shows what fields are available:
curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/keys" \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{"timeframe": {"from": '$(( $(date +%s) - 86400 ))'000, "to": '$(date +%s)'000}, "datasets": ["workers"]}' | jq '.result[:10]'
Note: The /query endpoint requires a saved queryId. For ad-hoc queries, use the Cloudflare Dashboard Query Builder or wrangler tail.
# This format requires a saved query ID
# Query errors with status >= 400
curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/query" \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"timeframe": {
"from": '$(( $(date +%s) - 900 ))'000,
"to": '$(date +%s)'000
},
"parameters": {
"datasets": ["workers"],
"filters": [
{"key": "$workers.scriptName", "operation": "eq", "type": "string", "value": "enter-pollinations-ai"},
{"key": "$metadata.statusCode", "operation": "gte", "type": "number", "value": 400}
],
"calculations": [{"operator": "count"}],
"groupBys": [
{"type": "string", "value": "$metadata.statusCode"},
{"type": "string", "value": "$metadata.error"}
],
"limit": 50
}
}' | jq '.result.events.events[:20]'
curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/query" \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"timeframe": {
"from": '$(( $(date +%s) - 3600 ))'000,
"to": '$(date +%s)'000
},
"parameters": {
"datasets": ["workers"],
"filters": [
{"key": "$workers.scriptName", "operation": "eq", "type": "string", "value": "enter-pollinations-ai"},
{"key": "$metadata.statusCode", "operation": "gte", "type": "number", "value": 400}
],
"calculations": [{"operator": "count"}],
"groupBys": [
{"type": "string", "value": "model"},
{"type": "string", "value": "$metadata.statusCode"}
],
"limit": 100
}
}' | jq '.result.calculations[0].aggregates'
curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/query" \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"timeframe": {
"from": '$(( $(date +%s) - 900 ))'000,
"to": '$(date +%s)'000
},
"parameters": {
"datasets": ["workers"],
"filters": [
{"key": "$workers.scriptName", "operation": "eq", "type": "string", "value": "enter-pollinations-ai"},
{"key": "$metadata.statusCode", "operation": "gte", "type": "number", "value": 500}
],
"limit": 20
}
}' | jq '.result.events.events[] | {
timestamp: .timestamp,
statusCode: ."$metadata".statusCode,
error: ."$metadata".error,
message: ."$metadata".message,
requestId: ."$workers".requestId,
url: ."$metadata".url
}'
curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/keys" \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"timeframe": {
"from": '$(( $(date +%s) - 3600 ))'000,
"to": '$(date +%s)'000
},
"datasets": ["workers"],
"filters": [
{"key": "$workers.scriptName", "operation": "eq", "type": "string", "value": "enter-pollinations-ai"}
]
}' | jq '.result.keys'
The worker uses LogTape for structured logging with these key fields:
Downstream errors are logged with:
log.warn("Chat completions error {status}: {body}", {
status: response.status,
body: responseText,
});
For aggregated model health stats, query Tinybird directly:
# Get model health stats (last 5 minutes)
curl "https://api.europe-west2.gcp.tinybird.co/v0/pipes/model_health.json?token=$TINYBIRD_TOKEN" | jq '.data'
# Get detailed error breakdown
curl "https://api.europe-west2.gcp.tinybird.co/v0/pipes/model_errors.json?token=$TINYBIRD_TOKEN" | jq '.data'
The Tinybird token is a read-only public token found in:
apps/model-monitor/src/hooks/useModelMonitor.jsCheck Model Monitor - https://monitor.pollinations.ai
Query Cloudflare Logs - Use the API queries above
Correlate with Request ID - If you have a specific request ID:
# Filter by request ID
curl -s "https://api.cloudflare.com/client/v4/accounts/$ACCOUNT_ID/workers/observability/telemetry/query" \
-H "Authorization: Bearer $API_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"timeframe": {"from": '$(( $(date +%s) - 86400 ))'000, "to": '$(date +%s)'000},
"parameters": {
"datasets": ["workers"],
"filters": [
{"key": "$workers.requestId", "operation": "eq", "type": "string", "value": "REQUEST_ID_HERE"}
],
"limit": 100
}
}' | jq '.result.events.events'
Check Backend Logs - If error is from downstream service:
# Image service
ssh enter-services "sudo journalctl -u image-pollinations.service --since '5 minutes ago'"
# Text service
ssh enter-services "sudo journalctl -u text-pollinations.service --since '5 minutes ago'"
Test Model Directly - Verify if model is actually broken:
TOKEN=$(grep ENTER_API_TOKEN_REMOTE enter.pollinations.ai/.testingtokens | cut -d= -f2)
# Test text model
curl -s 'https://gen.pollinations.ai/v1/chat/completions' \
-H "Authorization: Bearer $TOKEN" \
-H 'Content-Type: application/json' \
-d '{"model": "MODEL_NAME", "messages": [{"role": "user", "content": "Test"}]}' \
-w "\nHTTP: %{http_code}\n"
# Test image model
curl -s 'https://gen.pollinations.ai/image/test?model=MODEL_NAME&width=256&height=256' \
-H "Authorization: Bearer $TOKEN" \
-w "\nHTTP: %{http_code}\n" -o /dev/null
What works:
/telemetry/keys - List available log fields ✅/telemetry/values - Get unique values for a field ✅enter.pollinations.ai/secrets/env.json ✅Limitations:
/telemetry/query requires a saved queryId from the dashboardwrangler tail for real-time logsTinybird provides pre-aggregated model health stats and raw event data.
apps/model-monitor/src/hooks/useModelMonitor.jsenter.pollinations.ai/observability/.tinyb (in token field)# Public read-only token from apps/model-monitor
TINYBIRD_TOKEN="p.eyJ1IjogImFjYTYzZjc5LThjNTYtNDhlNC05NWJjLWEyYmFjMTY0NmJkMyIsICJpZCI6ICJmZTRjODM1Ni1iOTYwLTQ0ZTYtODE1Mi1kY2UwYjc0YzExNjQiLCAiaG9zdCI6ICJnY3AtZXVyb3BlLXdlc3QyIn0.Wc49vYoVYI_xd4JSsH_Fe8mJk7Oc9hx0IIldwc1a44g"
# Get model health (last 5 min)
curl -s "https://api.europe-west2.gcp.tinybird.co/v0/pipes/model_health.json?token=$TINYBIRD_TOKEN" | jq '.data'
For querying the raw generation_event datasource, use the admin token from .tinyb:
# Get admin token from .tinyb file
TINYBIRD_ADMIN_TOKEN=$(jq -r '.token' enter.pollinations.ai/observability/.tinyb)
# Find users with frequent 403 errors (last 24 hours)
curl -s "https://api.europe-west2.gcp.tinybird.co/v0/sql?token=$TINYBIRD_ADMIN_TOKEN" \
--data-urlencode "q=SELECT user_id, user_github_username, user_tier, count() as error_403_count
FROM generation_event
WHERE response_status = 403
AND start_time > now() - interval 24 hour
AND user_id != ''
AND user_id != 'undefined'
GROUP BY user_id, user_github_username, user_tier
ORDER BY error_403_count DESC
LIMIT 20"
# Find users with 500 errors (actual backend issues)
curl -s "https://api.europe-west2.gcp.tinybird.co/v0/sql?token=$TINYBIRD_ADMIN_TOKEN" \
--data-urlencode "q=SELECT user_github_username, model_requested, error_message, count() as error_count
FROM generation_event
WHERE response_status >= 500
AND start_time > now() - interval 24 hour
GROUP BY user_github_username, model_requested, error_message
ORDER BY error_count DESC
LIMIT 20"
# Check specific user's recent errors
curl -s "https://api.europe-west2.gcp.tinybird.co/v0/sql?token=$TINYBIRD_ADMIN_TOKEN" \
--data-urlencode "q=SELECT start_time, response_status, model_requested, error_message
FROM generation_event
WHERE user_github_username = 'USERNAME_HERE'
AND start_time > now() - interval 24 hour
ORDER BY start_time DESC
LIMIT 50"
The generation_event datasource is defined in enter.pollinations.ai/observability/datasources/generation_event.datasource and includes:
user_id, user_github_username, user_tierresponse_status, error_message, error_response_codemodel_requested, model_usedtotal_price, total_coststart_time, end_time, response_timeHelper scripts for common debugging tasks. Run from repo root.
# Find users with >10 403 errors in last 24 hours
.claude/skills/model-debugging/scripts/find-403-users.sh 24 10
# Filter by tier (e.g., only spore users)
.claude/skills/model-debugging/scripts/find-403-users.sh 24 10 spore
# Find 500+ errors grouped by user/model/message
.claude/skills/model-debugging/scripts/find-500-errors.sh 24
# See a user's recent errors
.claude/skills/model-debugging/scripts/check-user-errors.sh superbrainai 24
| Model | Type | Endpoint | Status |
|-------|------|----------|--------|
| openai | text | POST /v1/chat/completions | ✅ |
| openai-fast | text | POST /v1/chat/completions | ✅ |
| openai-large | text | POST /v1/chat/completions | ✅ |
| openai-audio | text | GET /text/{prompt}?model=openai-audio&voice=alloy | ✅ (MP3) |
| claude | text | POST /v1/chat/completions | ✅ |
| gemini-fast | text | POST /v1/chat/completions | ✅ |
| flux | image | GET /image/{prompt} | ✅ |
| nanobanana-pro | image | GET /image/{prompt} | ✅ |
| seedream-pro | image | GET /image/{prompt} | ✅ |
| seedance-pro | video | GET /image/{prompt} | ✅ (MP4) |
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