Cekura Platform Onboarding

Purpose

Walk a new user through the complete Cekura setup — from account creation to their first successful test run. This is an interactive, step-by-step guide. At each phase, confirm with the user before proceeding and help them with the actual API calls or UI steps.

Performing Platform Actions

When this skill suggests creating, listing, updating, or evaluating something on Cekura, prefer using available platform tools over describing API calls or dashboard steps. In Claude Code with the Cekura plugin installed, these tools are auto-configured and handle authentication, parameter validation, and error handling for you. Fall back to direct API endpoints or dashboard guidance only when no tools are available in the current session.

How to Use This Skill

This is an interactive walkthrough, not a reference doc. Guide the user through each phase conversationally:

1. Ask where they are in the process (some may already have an account/agent)
2. Skip phases they've already completed
3. Use the Cekura API or dashboard to perform actions on their behalf
4. Validate each step before moving to the next
5. Hand off to specialized skills (cekura-metric-design, cekura-eval-design) when appropriate

Phase 1: Account & Project Setup

1.1 Verify Account Access

Ask the user:

• "Do you already have a Cekura account?"
• "Do you have an API key, or do you sign in via OAuth?"

If they have an API key, verify it works by calling the metrics list endpoint. A successful response (even empty) confirms the key is valid.

If they don't have an account, direct them to sign up at https://dashboard.cekura.ai/sign-up and create a project.

For Claude Code plugin users: If platform operations aren't working, run /setup-mcp to configure API access.

1.2 Project Setup

Ask: "Do you already have a project, or do we need to create one?"

If creating: Create the project via the Cekura dashboard or projects API.

Project organization guidance:

• Small teams: single project for multiple agents
• Enterprises: separate projects by team and environment (staging vs production)
• Each project gets its own metrics, evaluators, and observability data

Phase 2: Agent Configuration

2.1 Create or Connect an Agent

Ask: "Do you already have a voice AI agent deployed? What provider (VAPI, Retell, LiveKit, ElevenLabs, custom)?"

If they have an agent: Get the agent details and create it on Cekura with the agent name, project ID, and description.

For detailed agent setup (provider integration, mock tools, KB, dynamic variables), hand off to the cekura-create-agent skill.

Critical: Agent description is essential. It enables automatic evaluator generation and powers metrics that reference {{agent.description}}. Ask the user to paste their agent's full system prompt.

2.2 Provider Integration

Based on their provider, guide them through connecting:

VAPI:

• Need: VAPI API Key + Assistant ID
• In Cekura: Agent Settings → Provider → VAPI → enter credentials

Retell:

• Need: Retell API Key + Assistant ID
• In Cekura: Agent Settings → Provider → Retell → enter credentials
• Optionally enable auto-sync of prompts (every 30s)

LiveKit:

• Need: LiveKit agent deployment details
• Calls include metadata.raw_metrics for latency tracking

Self-hosted / Custom (reached via SIP, WebSocket, or chat):

• These are assistant_provider: "self_hosted" agents — SIP / WebSocket / chat are connection modes, not providers.
• Guide based on their specific setup.
• Refer to https://docs.cekura.ai/documentation/integrations/ for provider-specific docs.

2.3 Dynamic Variables (if applicable)

Ask: "Does your agent use dynamic variables — per-call data like customer names, account IDs, or configuration flags?"

If yes:

• Cekura auto-detects {{variableName}} patterns in the agent description
• These become available in metrics as {{dynamic_variables.keyName}}
• Useful for multi-agent flows where each node has its own system prompt

2.4 Mock Tools (if applicable)

Ask: "Does your agent call external APIs or tools during calls?"

If yes:

• Auto-fetch from provider (recommended): Cekura pulls tool definitions automatically
• Manual setup: Add tool names, descriptions, and input/output mappings
• Mock tools let you test without hitting real backends
• See cekura-eval-design skill for detailed mock tool configuration

Phase 3: Metrics Setup

3.1 Enable Pre-defined Metrics

Always recommend selecting ALL pre-defined metrics for comprehensive analysis:

Guide: "Go to your project's Metrics section and enable all pre-defined metrics. This gives you a comprehensive baseline."

Two-step activation: Metrics must be (1) toggled on at the project level AND (2) attached to individual evaluators.

3.2 Custom Metrics (optional, defer to later)

For first-time users, skip custom metrics initially. Once they have test results, they can use the cekura-metric-design skill to create targeted custom metrics.

Phase 4: First Evaluators

4.1 Auto-Generate Evaluators (Recommended)

The fastest path to first tests — use the scenario auto-generation endpoint:

{
  "agent_id": <agent_id>,
  "num_scenarios": 10,
  "personalities": [<personality_id>],
  "generate_expected_outcomes": true,
  "tool_ids": ["TOOL_END_CALL", "TOOL_END_CALL_ON_TRANSFER"]
}

Poll progress, then review the generated scenarios.

After generation, check:

• Are instructions specific and behavioral?
• Are expected outcomes concise and achievable?
• Are the right tools enabled?
• For non-English agents: PATCH scenario_language to correct code

4.2 Review and Supplement

Common gaps in auto-generated evals:

• Red-team / adversarial scenarios
• Edge cases specific to the client's domain
• Multi-language coverage
• Tool failure scenarios

Hand off to the cekura-eval-design skill for designing more targeted evaluators.

4.3 Attach Metrics

Every evaluator needs metrics attached. At minimum:

• Expected Outcome — Did the agent achieve the scenario's goal?
• Infrastructure Issues — Connection drops, silence, non-response

Use bulk-add via actions → modify scenarios in the UI.

Phase 5: First Test Run

5.1 Execute

Run the scenarios with the agent ID and scenario IDs:

{
  "agent_id": <agent_id>,
  "scenarios": [<scenario_ids>],
  "frequency": 1
}

Start with 5-10 scenarios for the first run. Voice calls take 1-3 minutes each.

5.2 Monitor

Check results via the results endpoint. Each run includes:

• Full transcript
• Audio recording
• Metric scores
• Expected outcome pass/fail

5.3 Review Results

Guide the user through interpreting results:

• 70-80% pass rate is realistic for a first iteration
• Review failures to identify: misunderstandings, missing info, technical issues
• 90-95% after refinement is the target
• Don't aim for 100% — real conversations are unpredictable

Phase 6: What's Next

After first successful test run, point the user to:

Documentation

• Public docs: https://docs.cekura.ai
• LLM-friendly docs: https://docs.cekura.ai/llms.txt
• Concepts: https://docs.cekura.ai/documentation/key-concepts/
• Integrations: https://docs.cekura.ai/documentation/integrations/

See references/api-quickstart.md for the essential endpoints used during onboarding.