Production API Tester

Production API Tester

Skill for testing the production research API in live environments. Enables optimization loops: create strategy → test in production → analyze with Langfuse → iterate.

When to Use This Skill

• "Test the daily_news_briefing strategy in production"
• "Create a test task and execute it"
• "Run the full optimization loop for my new legal research strategy"
• "Check if the production API is healthy"
• "Clean up test tasks after experimentation"
• "Monitor execution status and retrieve results"

What This Skill Does

5 Core Functions:

1. Create Test Tasks - Subscribe test emails to research topics
2. Execute Research - Trigger batch or single task execution
3. Monitor Status - Poll for completion and retrieve results
4. Analyze Results - Link to Langfuse traces for performance analysis
5. Cleanup - Delete test tasks after validation

Required Setup

Environment Variables

# Production API Configuration
export PROD_API_URL="https://webresearchagent.replit.app"  # Production URL
export PROD_API_KEY="your_api_key_here"  # Your API secret key
export CALLBACK_URL="https://webhook.site/​your-unique-url"  # Webhook receiver URL

# Optional: Override for local testing
export PROD_API_URL="http://localhost:8000"  # Test locally

Getting Your API Key:

• User will provide this
• Store in environment or pass via --api-key flag

Setting Up Webhook Receiver:

• Option 1: Use webhook.site (https://webhook.site) - get instant unique URL
• Option 2: Use Langdock webhook (if using Langdock integration)
• Option 3: Run local webhook receiver (provided in helpers)

Workflow

Use Case 1: Test Single Strategy (Quick Test)

Goal: Execute a strategy once and check results

Step 1: Create Test Task

cd /​home/​user/​web_research_agent/​.claude/​skills/​production-api-tester/​helpers

# Create test task
python3 create_test_task.py \
  --api-key "$PROD_API_KEY" \
  --email "[email protected]" \
  --topic "AI developments in healthcare" \
  --frequency "daily" \
  --output /​tmp/​api_test/​task.json

Output:

{
  "id": "abc123",
  "email": "[email protected]",
  "research_topic": "AI developments in healthcare",
  "frequency": "daily",
  "schedule_time": "09:00",
  "is_active": true,
  "created_at": "2025-11-09T10:00:00Z",
  "last_run_at": null
}

Step 2: Execute Research

# Execute batch (all daily tasks)
python3 execute_batch.py \
  --api-key "$PROD_API_KEY" \
  --frequency "daily" \
  --callback-url "$CALLBACK_URL" \
  --output /​tmp/​api_test/​execution.json

Output:

{
  "status": "running",
  "frequency": "daily",
  "tasks_found": 1,
  "started_at": "2025-11-09T10:01:00Z"
}

Step 3: Monitor Results

# Option A: Poll webhook endpoint
python3 check_webhook.py \
  --webhook-url "$CALLBACK_URL" \
  --wait-for-results \
  --timeout 300

# Option B: Check task status
python3 get_task.py \
  --api-key "$PROD_API_KEY" \
  --task-id "abc123" \
  --output /​tmp/​api_test/​task_status.json

Step 4: Link to Langfuse

# Get Langfuse trace for this execution
python3 link_to_langfuse.py \
  --task-id "abc123" \
  --email "[email protected]" \
  --output /​tmp/​api_test/​langfuse_link.json

Output:

{
  "task_id": "abc123",
  "trace_query": {
    "metadata_filter": {
      "user_email": "[email protected]",
      "research_topic": "AI developments in healthcare"
    },
    "time_range": "last_1_hour"
  },
  "langfuse_url": "https://cloud.langfuse.com/​project/​.../​traces?filter=...",
  "trace_count": 1,
  "latest_trace_id": "xyz789"
}

Step 5: Cleanup

# Delete test task
python3 delete_task.py \
  --api-key "$PROD_API_KEY" \
  --task-id "abc123"

---

Use Case 2: Full Optimization Loop (Strategy Development)

Goal: Test new strategy → analyze performance → iterate

This combines strategy-builder skill with production-api-tester skill.

Loop Iteration:

cd /​home/​user/​web_research_agent/​.claude/​skills/​production-api-tester/​helpers

# 1. Generate/​update strategy (using strategy-builder skill)
python3 ../​../​strategy-builder/​helpers/​generate_strategy.py \
  --slug "legal/​court_cases_de" \
  --category "legal" \
  --time-window "month" \
  --depth "comprehensive" \
  --output /​tmp/​optimization_loop/​strategy_v1.yaml

# 2. Validate strategy
python3 ../​../​strategy-builder/​helpers/​validate_strategy.py \
  --strategy /​tmp/​optimization_loop/​strategy_v1.yaml

# 3. Deploy strategy to database
# (Manual: save to strategies/, add to index.yaml, migrate to DB)

# 4. Create test task for this strategy
python3 create_test_task.py \
  --api-key "$PROD_API_KEY" \
  --email "[email protected]" \
  --topic "Datenschutz DSGVO Verstoß" \
  --frequency "daily" \
  --output /​tmp/​optimization_loop/​task.json

# 5. Execute research
TASK_ID=$(jq -r '.id' /​tmp/​optimization_loop/​task.json)
python3 execute_batch.py \
  --api-key "$PROD_API_KEY" \
  --frequency "daily" \
  --callback-url "$CALLBACK_URL" \
  --output /​tmp/​optimization_loop/​execution.json

# 6. Wait for completion and get results
python3 wait_for_completion.py \
  --task-id "$TASK_ID" \
  --api-key "$PROD_API_KEY" \
  --timeout 600 \
  --output /​tmp/​optimization_loop/​results.json

# 7. Link to Langfuse trace
python3 link_to_langfuse.py \
  --task-id "$TASK_ID" \
  --email "[email protected]" \
  --output /​tmp/​optimization_loop/​langfuse.json

# 8. Retrieve and analyze Langfuse trace
TRACE_ID=$(jq -r '.latest_trace_id' /​tmp/​optimization_loop/​langfuse.json)
python3 ../​../​langfuse-optimization/​helpers/​retrieve_single_trace.py \
  "$TRACE_ID" \
  --filter-essential \
  --output /​tmp/​optimization_loop/​trace.json

# 9. Analyze performance
python3 ../​../​strategy-builder/​helpers/​analyze_strategy_performance.py \
  --traces /​tmp/​optimization_loop/​trace.json \
  --strategy "legal/​court_cases_de" \
  --output /​tmp/​optimization_loop/​performance.json

# 10. Review recommendations and iterate
cat /​tmp/​optimization_loop/​performance.json
# Apply fixes to strategy YAML, repeat from step 1

---

Use Case 3: Batch Testing (Multiple Strategies)

Goal: Test multiple strategies in parallel

Step 1: Create Multiple Test Tasks

cd /​home/​user/​web_research_agent/​.claude/​skills/​production-api-tester/​helpers

# Create tasks for different strategies
python3 batch_create_tasks.py \
  --api-key "$PROD_API_KEY" \
  --tasks-file /​tmp/​batch_test/​tasks_config.json \
  --output /​tmp/​batch_test/​created_tasks.json

tasks_config.json:

[
  {
    "email": "[email protected]",
    "research_topic": "AI regulation updates",
    "frequency": "daily",
    "strategy_hint": "daily_news_briefing"
  },
  {
    "email": "[email protected]",
    "research_topic": "Tesla stock analysis",
    "frequency": "daily",
    "strategy_hint": "financial_research"
  },
  {
    "email": "[email protected]",
    "research_topic": "GDPR compliance updates",
    "frequency": "daily",
    "strategy_hint": "legal/​court_cases_de"
  }
]

Step 2: Execute All

# Execute daily batch
python3 execute_batch.py \
  --api-key "$PROD_API_KEY" \
  --frequency "daily" \
  --callback-url "$CALLBACK_URL"

Step 3: Monitor All

# Wait for all tasks to complete
python3 monitor_batch.py \
  --api-key "$PROD_API_KEY" \
  --tasks-file /​tmp/​batch_test/​created_tasks.json \
  --timeout 900 \
  --output /​tmp/​batch_test/​batch_results.json

Step 4: Analyze All

# Generate comparison report
python3 compare_strategies.py \
  --results /​tmp/​batch_test/​batch_results.json \
  --output /​tmp/​batch_test/​comparison.json

Output: Comparison of latency, success rate, error types across strategies

Step 5: Cleanup All

# Delete all test tasks
python3 batch_delete_tasks.py \
  --api-key "$PROD_API_KEY" \
  --tasks-file /​tmp/​batch_test/​created_tasks.json

---

Use Case 4: Health Check & Monitoring

Goal: Verify production API is healthy

cd /​home/​user/​web_research_agent/​.claude/​skills/​production-api-tester/​helpers

# Quick health check
python3 health_check.py \
  --api-url "$PROD_API_URL"

# Extended monitoring
python3 health_check.py \
  --api-url "$PROD_API_URL" \
  --continuous \
  --interval 60 \
  --duration 3600

Output:

✓ API is healthy
  Status: online
  Database: connected
  Langfuse: enabled
  Response time: 234ms

---

Helper Tools Reference

1. create_test_task.py

Purpose: Create research task subscription

Usage:

python3 create_test_task.py \
  --api-key "$PROD_API_KEY" \
  [--api-url "$PROD_API_URL"] \
  --email "[email protected]" \
  --topic "Research topic" \
  --frequency daily|weekly|monthly \
  [--schedule-time "09:00"] \
  --output /​tmp/​task.json

Output: Task object with ID for tracking

2. execute_batch.py

Purpose: Trigger batch research execution

Usage:

python3 execute_batch.py \
  --api-key "$PROD_API_KEY" \
  [--api-url "$PROD_API_URL"] \
  --frequency daily|weekly|monthly \
  --callback-url "https://webhook.site/​..." \
  --output /​tmp/​execution.json

Output: Execution status (running, tasks_found, started_at)

3. get_task.py

Purpose: Retrieve task details

Usage:

python3 get_task.py \
  --api-key "$PROD_API_KEY" \
  [--api-url "$PROD_API_URL"] \
  --task-id "abc123" \
  --output /​tmp/​task.json

4. list_tasks.py

Purpose: List all research tasks

Usage:

python3 list_tasks.py \
  --api-key "$PROD_API_KEY" \
  [--api-url "$PROD_API_URL"] \
  [--email "[email protected]"] \
  [--frequency daily] \
  --output /​tmp/​tasks.json

5. delete_task.py

Purpose: Delete research task

Usage:

python3 delete_task.py \
  --api-key "$PROD_API_KEY" \
  [--api-url "$PROD_API_URL"] \
  --task-id "abc123"

6. wait_for_completion.py

Purpose: Poll task until completion

Usage:

python3 wait_for_completion.py \
  --api-key "$PROD_API_KEY" \
  --task-id "abc123" \
  [--timeout 600] \
  [--poll-interval 10] \
  --output /​tmp/​results.json

7. link_to_langfuse.py

Purpose: Find Langfuse trace for task execution

Usage:

python3 link_to_langfuse.py \
  --task-id "abc123" \
  --email "[email protected]" \
  [--time-range "last_1_hour"] \
  --output /​tmp/​langfuse_link.json

Output:

• Trace query parameters
• Langfuse dashboard URL
• Latest trace ID

8. health_check.py

Purpose: Check API health

Usage:

python3 health_check.py \
  [--api-url "$PROD_API_URL"] \
  [--continuous] \
  [--interval 60] \
  [--duration 3600]

9. webhook_receiver.py (Local Testing)

Purpose: Run local webhook receiver for testing

Usage:

# Start local webhook receiver
python3 webhook_receiver.py \
  --port 8080 \
  --output-dir /​tmp/​webhooks

# Use as callback URL
export CALLBACK_URL="http://localhost:8080/​webhook"

Features:

• Logs all incoming webhooks
• Saves payloads to disk
• Provides ngrok-style public URL (if using tunneling)

---

Integration with Other Skills

With strategy-builder Skill

Optimization Loop:

1. strategy-builder: analyze_research_query.py
   → Determine if new strategy needed

2. strategy-builder: generate_strategy.py
   → Create strategy YAML

3. strategy-builder: validate_strategy.py
   → Validate structure

4. [Manual: Deploy strategy to database]

5. production-api-tester: create_test_task.py
   → Create test subscription

6. production-api-tester: execute_batch.py
   → Run research

7. production-api-tester: wait_for_completion.py
   → Get results

8. production-api-tester: link_to_langfuse.py
   → Find trace

9. strategy-builder: analyze_strategy_performance.py
   → Analyze performance

10. [Iterate: Apply fixes and repeat]

With langfuse-optimization Skill

Performance Deep Dive:

1. production-api-tester: execute_batch.py
   → Generate new traces

2. langfuse-optimization: retrieve_traces_and_observations.py
   → Get detailed trace data

3. langfuse-optimization: [analyze and fix configs]
   → Optimize style.yaml, template.yaml, tools.yaml

With langfuse-advanced-filters Skill

Targeted Analysis:

1. production-api-tester: Create multiple test tasks
2. production-api-tester: Execute batch
3. langfuse-advanced-filters: query_with_filters.py
   → Filter by specific criteria (e.g., latency > 10s)
4. langfuse-advanced-filters: analyze_filtered_results.py
   → Identify patterns

---

Common Patterns

Pattern 1: Rapid Iteration Testing

# Loop for quick iterations
for i in {1..5}; do
  echo "Iteration $i"

  # Modify strategy (manual or automated)

  # Test
  python3 create_test_task.py ... --output /​tmp/​test_$i/​task.json
  TASK_ID=$(jq -r '.id' /​tmp/​test_$i/​task.json)
  python3 execute_batch.py ...
  python3 wait_for_completion.py --task-id "$TASK_ID" --output /​tmp/​test_$i/​results.json

  # Analyze
  python3 link_to_langfuse.py --task-id "$TASK_ID" --output /​tmp/​test_$i/​trace.json

  # Cleanup
  python3 delete_task.py --task-id "$TASK_ID"

  echo "Iteration $i complete. Review /​tmp/​test_$i/"
  sleep 5
done

Pattern 2: A/​B Testing Strategies

# Test strategy A
python3 create_test_task.py --email "[email protected]" --topic "AI news" --output /​tmp/​ab_test/​task_a.json

# Test strategy B (different strategy slug via topic classification)
python3 create_test_task.py --email "[email protected]" --topic "AI regulation detailed analysis" --output /​tmp/​ab_test/​task_b.json

# Execute both
python3 execute_batch.py --frequency daily

# Compare results
python3 compare_tasks.py \
  --task-a /​tmp/​ab_test/​task_a.json \
  --task-b /​tmp/​ab_test/​task_b.json \
  --output /​tmp/​ab_test/​comparison.json

Pattern 3: Regression Testing

# Before deploying changes to production, test current vs new

# 1. Baseline (current production strategy)
python3 create_test_task.py --email "[email protected]" --topic "Test topic" --output /​tmp/​regression/​baseline_task.json
python3 execute_batch.py ...
# Save results

# 2. Make changes to strategy

# 3. Test new version
python3 create_test_task.py --email "[email protected]" --topic "Test topic" --output /​tmp/​regression/​new_task.json
python3 execute_batch.py ...
# Compare results

# 4. Validate no regressions
python3 validate_regression.py \
  --baseline /​tmp/​regression/​baseline_results.json \
  --new /​tmp/​regression/​new_results.json \
  --output /​tmp/​regression/​regression_report.json

---

Tips & Best Practices

1. Use Unique Test Emails

Always use identifiable test email addresses:

• test-strategy-{strategy_name}@example.com
• dev-{your_name}@example.com
• Never use real user emails for testing

2. Clean Up Test Tasks

Always delete test tasks after validation:

# List all test tasks
python3 list_tasks.py --api-key "$PROD_API_KEY" | grep "test-"

# Bulk delete
python3 batch_delete_tasks.py --pattern "test-*"

3. Use Webhook.site for Quick Tests

For quick tests without setting up infrastructure:

1. Go to https://webhook.site
2. Copy your unique URL
3. Use as CALLBACK_URL
4. View results in browser

4. Tag Test Executions

When creating test tasks, use descriptive topics:

# Good
--topic "[TEST] AI news - strategy_v2_iteration_3"

# Bad
--topic "test"

This makes Langfuse traces easier to find and filter.

5. Automate Full Loop

Create a script for the full optimization loop:

#!/​bin/​bash
# optimize_strategy.sh

STRATEGY_SLUG=$1
TEST_TOPIC=$2

# Generate → Validate → Deploy → Test → Analyze → Report
...

6. Monitor API Rate Limits

Production API may have rate limits:

• Wait between batch executions
• Use --poll-interval to avoid overwhelming the API
• Check API response headers for rate limit info

---

Troubleshooting

"Authentication failed":

• Verify PROD_API_KEY is set correctly
• Check API key is active in production database
• Ensure X-API-Key header is sent

"Webhook not receiving results":

• Verify CALLBACK_URL is publicly accessible
• Check webhook receiver logs
• Use webhook.site for debugging
• Ensure URL doesn't have trailing slash inconsistencies

"Task execution times out":

• Increase --timeout parameter
• Check production logs for errors
• Verify strategy is valid and doesn't have infinite loops
• Check Langfuse for ERROR level traces

"Cannot find Langfuse trace":

• Wait 30-60 seconds for trace to be indexed
• Verify metadata fields match (email, topic)
• Check time range is wide enough
• Use --time-range "last_1_day" for safety

"Health check fails":

• Verify PROD_API_URL is correct
• Check if API is deployed and running
• Verify network connectivity
• Check API logs for startup errors

---

Security Considerations

1. API Key Management

DO:

• Store API key in environment variables
• Use separate API keys for testing vs production
• Rotate keys regularly

DON'T:

• Commit API keys to git
• Share API keys in logs or screenshots
• Use production API key for automated testing

2. Test Data

DO:

• Use fake/​test email addresses
• Use non-sensitive research topics
• Mark test tasks clearly

DON'T:

• Use real user data for testing
• Test with sensitive/​confidential topics
• Leave test tasks in production database

3. Webhook Security

DO:

• Use HTTPS for webhook URLs
• Validate webhook payloads
• Log webhook failures

DON'T:

• Expose webhook receiver without authentication
• Trust webhook data without validation
• Store sensitive data in webhook logs

---

Success Criteria

Good production testing should:

1. ✅ Use isolated test tasks (identifiable emails)
2. ✅ Clean up after completion
3. ✅ Link to Langfuse traces for analysis
4. ✅ Document results for comparison
5. ✅ Enable rapid iteration (< 5 min per cycle)
6. ✅ Validate before deploying to real users

---

Remember: This skill is about safe production testing, not replacing proper staging environments. Use it for:

• Strategy validation
• Performance profiling
• Regression testing
• Optimization loops

For high-risk changes, always test locally first using run_daily_briefing.py.