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.
git 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/commands/cekura-ai/cekura-skills/create-metric)<a href="https://agentmods.dev/commands/cekura-ai/cekura-skills/create-metric"><img src="https://agentmods.dev/badge/commands/cekura-ai/cekura-skills/create-metric/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/commands/cekura-ai/cekura-skills/create-metric"><img src="https://agentmods.dev/badge/commands/cekura-ai/cekura-skills/create-metric.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 | $0.00018 | $0.01708 |
| Opus 5 | $0.00009 | $0.00854 |
| Sonnet 5 | $0.00004 | $0.00342 |
| Haiku 4.5 | $0.00002 | $0.00171 |
Grade B, and why
create-metric 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.
- Output instructions with timestamp requirements How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cekura skill verification tag:
ack:create-metric:5p4w7hWhen you call a Cekura metric write tool from this command (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.
Tracking (do this first)
Before doing anything else, call mcp__cekura__cekura_skill_started with
skill_name="create-metric", verification_tag="ack:create-metric:5p4w7h", and plugin_version="0.16". If a conversation/session ID is available (e.g. you
were invoked from Cekura sandbox), also pass it as conversation_id. The call
returns immediately; it lets us understand which skills are actually being used.
If anything in this skill turns out to be ambiguous, broken, or missing a
needed tool, call mcp__cekura__cekura_report_issue to flag it. Use this
LIBERALLY — even severity="low" reports are valuable feedback.
Create or Update a Cekura Metric
Create a new metric or update an existing one on the Cekura platform. The metric-design skill provides detailed guidance — load it for comprehensive patterns.
Determine Mode: Create or Update
- Create: User says "create", "new", "add", or describes a metric to build
- Update: User provides a metric ID, says "update", "edit", "change", or wants to modify an existing metric
For updates: fetch the current metric with mcp__cekura__metrics_retrieve and show the user the current state (name, description/prompt, eval type, trigger config). Then ask what to change. Apply changes with mcp__cekura__metrics_partial_update — only send the fields being changed, not the full payload. After updating, fetch again to verify.
Process (Create)
- Check baseline metrics first: Before creating custom metrics, verify the agent has the baseline predefined metrics enabled:
- Expected Outcome — checks if the agent achieved the scenario's expected result
- Infrastructure Issues — flags silent periods, connection drops, agent non-response
- Tool Call Success — monitors tool call success/failure
- Latency — measures response time
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 8130c7abb24c
- 3d ago Changed 27bc6f0fc2cc
- 8d ago Changed e5e4702a087c
- 12d ago First seen · 119 lines · 18 tokens per session scan B 33dbaa1f3e0c
create-metric is a command published in the GitHub repository cekura-ai/cekura-skills (7 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 1,708 once invoked, about $0.0001 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 commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.