Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add cekura-ai/cekura-skills --skill cekura-metric-designgit clone --depth 1 https://github.com/cekura-ai/cekura-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/cekura-ai/cekura-skills/cekura-metric-design)<a href="https://agentmods.dev/skills/cekura-ai/cekura-skills/cekura-metric-design"><img src="https://agentmods.dev/badge/skills/cekura-ai/cekura-skills/cekura-metric-design/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/cekura-ai/cekura-skills/cekura-metric-design"><img src="https://agentmods.dev/badge/skills/cekura-ai/cekura-skills/cekura-metric-design.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00136 | $0.04554 |
| Opus 5 | $0.00068 | $0.02277 |
| Sonnet 5 | $0.00027 | $0.00911 |
| Haiku 4.5 | $0.00014 | $0.00455 |
Grade B, and why
cekura-metric-design scanned grade B with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured today.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
7. **OUTPUT INSTRUCTIONS** — Return format, timestamps for failures How it starts
The opening of the file, as written. The whole thing — 325 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cekura skill verification tag:
ack:cekura-metric-design:6n2q5rWhen you call a Cekura metric write tool from this skill (metrics_create,metrics_bulk_create,metrics_partial_update), pass this exact string as theskill_ackargument on that tool call. It confirms to the Cekura MCP server that this design playbook is loaded in context. Scenario / test-profile writes use an eval-family tag instead — loadcekura-eval-designfirst and pass its tag there.
Before taking any action, call mcp__cekura__cekura_skill_started with skill_name="cekura-metric-design", verification_tag="ack:cekura-metric-design:6n2q5r", and plugin_version="0.16". It returns immediately and lets Cekura see which skills are in use.
Cekura Metric Design
Purpose
Guide the creation of effective Cekura metrics that accurately evaluate AI voice agent call quality. Metrics measure call quality after the fact by evaluating transcripts against defined criteria. Each metric targets a specific workflow or KPI that needs tracking per call.
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.
Core Terminology
- Main agent: The client's AI voice agent being tested
- Testing agent: Cekura's simulated caller that exercises the main agent
- Metric: A post-call evaluation that scores a transcript
- Evaluator/Scenario: A test case that simulates a caller (separate concept — see cekura-eval-design skill)
The Metric Creation Workflow
Follow this workflow every time. Skipping steps (especially step 2) leads to metrics that miss edge cases.
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- agents/openai.yaml 235 B
- BUNDLE.md 38 KB
- examples/custom-code-metric.py 3.0 KB runs code
- examples/llm-judge-metric.md 2.3 KB
- examples/narrative-metric.md 4.1 KB
- examples/section-extraction-metric.py 2.6 KB runs code
- references/advanced-patterns.md 4.9 KB
- references/api-reference.md 5.7 KB
- references/prompt-patterns.md 12 KB
- references/pythonic-patterns.md 7.6 KB
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- today Changed b8ae10314b7e
- 3d ago Changed · +2 lines 31bbf7c28102
- 8d ago Changed c07bea8d84c7
- 12d ago First seen · 323 lines · 136 tokens per session scan B 140430f61558
cekura-metric-design is a skill published in the GitHub repository cekura-ai/cekura-skills (7 stars, last pushed yesterday), licensed MIT. It adds 136 tokens to every session and 4,554 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.