cekura-generate-scenarios

Convert flagged production call logs into clustered evaluator scenarios for Cekura voice agents.

7|2|Updated Mar 6, 2026
One-click install
npx skills add https://github.com/cekura-ai/cekura-skills --skill cekura-generate-scenarios-cekura-ai
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: cekura-generate-scenarios
Source: https://github.com/cekura-ai/cekura-skills/tree/main/cekura/skills/cekura-generate-scenarios
Command: npx skills add https://github.com/cekura-ai/cekura-skills --skill cekura-generate-scenarios-cekura-ai

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Production voice agents fail in ways that only surface in real calls, and manually turning those failures into reproducible regression tests is slow and error-prone. This Skill converts flagged call logs into evaluator scenarios on the Cekura platform so every production failure becomes a test the next regression run can catch. ## Core Features & Use Cases - Failure clustering: Groups flagged call logs by failure mode (hallucination, drop, tool_error, workflow_miss, refusal) into 3-8 scenario clusters, each grounded in verbatim evidence quotes from real transcripts. - Dual write paths: Creates turn-by-turn conditional_actions replays directly via the scenarios API, or generates free-form instruction scenarios server-side from call logs, with dedup against existing scenarios. - Single-call fast path: Reproduces one specific call log ID (or observe URL) as a single evaluator scenario, including test profile with camelCase dynamic variables, metric selection (reuse, predefined copy, or create), and evaluator phone attachment. - Use Case: After triaging a week of production calls with cekura-flag-call-log-failures, hand the flagged set to this Skill to draft one scenario per failure cluster, confirm the proposals, and create them with matching caller personalities and metrics. ## Quick Start Ask the assistant to create evaluator scenarios on Cekura agent 12345 from the flagged call logs produced by the failure-triage step, then review and approve the proposed scenario set before anything is written.

Frequently Asked Questions about cekura-generate-scenarios

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I turn failed production calls into test scenarios?

Flag the failing calls first (normally with cekura-flag-call-log-failures), then this Skill clusters them by failure mode and drafts one evaluator scenario per cluster. After you approve the proposed set, it creates the scenarios via the Cekura scenarios API with matching personalities, test profiles, and metrics.

How do I create a scenario from a single call log ID?

Provide the call log ID or a dashboard observe URL and the Skill uses its single-call fast path: it retrieves the transcript, pins the focal failure turn, builds a turn-by-turn conditional_actions replay, and attaches a test profile, metric, and evaluator phone after your confirmation.

Does this skill triage or mine call logs itself?

No. It expects an already-flagged set of calls, typically the output of cekura-flag-call-log-failures or a user-supplied list of call IDs. If you have not triaged yet, it directs you to run the flagging skill first rather than re-implementing triage.

What is the difference between conditional_actions and instruction scenarios?

conditional_actions scenarios replay a fixed turn-by-turn caller path and are created directly via scenarios_create, suiting workflow_miss, tool_error, and drop failures. instruction scenarios are free-form and must be generated server-side from call logs, suiting hallucination, drift, refusal, and safety failures.

Why does my generated scenario fail with missing dynamic variables?

The Cekura outbound-call trigger reads dynamic variables from the attached test profile, not the scenario's own values, and ElevenLabs agents hard-fail when they are missing. Attach a test profile whose information sets both camelCase and lowercase keys for every placeholder in the agent prompt.

When should I create a new metric versus reuse an existing one?

Creating a new metric is the last resort. First reuse an existing metric on the agent via metrics_list, then check the predefined metrics catalog and copy a matching template into the project; only author a new llm_judge metric when neither fits the failure mode.