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-improvementgit 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-improvement)<a href="https://agentmods.dev/skills/cekura-ai/cekura-skills/cekura-metric-improvement"><img src="https://agentmods.dev/badge/skills/cekura-ai/cekura-skills/cekura-metric-improvement/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-improvement"><img src="https://agentmods.dev/badge/skills/cekura-ai/cekura-skills/cekura-metric-improvement.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 115 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00080 | $0.02385 |
| Opus 5 | $0.00040 | $0.01192 |
| Sonnet 5 | $0.00016 | $0.00477 |
| Haiku 4.5 | $0.00008 | $0.00238 |
Grade A, and why
cekura-metric-improvement scanned grade A with 0 findings 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cekura skill verification tag:
ack:cekura-metric-improvement:6t4d5mWhen 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-improvement", verification_tag="ack:cekura-metric-improvement:6t4d5m", and plugin_version="0.16". It returns immediately and lets Cekura see which skills are in use.
Cekura Metric Improvement (Labs Workflow)
Purpose
Guide the metric improvement cycle: identify misaligned metric results, leave structured feedback, run the labs improvement pipeline, and validate changes. This workflow transforms metric quality from initial draft to production-ready through systematic iteration.
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.
Manual Fix First, Then Labs
When metrics have systemic issues (high false-fail rates), do NOT jump straight to labs feedback. Instead:
- Read failure explanations and categorize root causes (e.g., cross-pollination from other flows, extra_questions flagged, end-of-call protocol violations, should-be-N/A cases)
- Write manual prompt fixes targeting the dominant failure categories — add SCOPE & FOCUS, DO NOT FLAG, narrow FAILURE CONDITIONS
- PATCH the updated descriptions via API
- Re-evaluate a sample of 20-30 calls per metric to validate the fixes
- THEN use labs feedback for remaining edge cases that manual fixes didn't catch
What ships with it
3 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.
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 cadccf28b5ac
- 3d ago Changed a864070e0e13
- 8d ago Changed 3ec0bd3d86dc
- 12d ago First seen · 212 lines · 80 tokens per session scan A 392e3f64cfcf
cekura-metric-improvement is a skill published in the GitHub repository cekura-ai/cekura-skills (7 stars, last pushed yesterday), licensed MIT. It adds 80 tokens to every session and 2,385 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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