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 saemihemma/lead-producer-oss --skill role-analytics-engineergit clone --depth 1 https://github.com/saemihemma/lead-producer-ossWrote 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/saemihemma/lead-producer-oss/role-analytics-engineer)<a href="https://agentmods.dev/skills/saemihemma/lead-producer-oss/role-analytics-engineer"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/role-analytics-engineer/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/saemihemma/lead-producer-oss/role-analytics-engineer"><img src="https://agentmods.dev/badge/skills/saemihemma/lead-producer-oss/role-analytics-engineer.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 | $0.00039 | $0.00511 |
| Opus 5 | $0.00019 | $0.00255 |
| Sonnet 5 | $0.00008 | $0.00102 |
| Haiku 4.5 | $0.00004 | $0.00051 |
Grade A, and why
role-analytics-engineer 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 9d ago.
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 — 57 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Engineer
Use When
- Defining or auditing metrics and KPI trees
- Designing telemetry or event schemas
- Planning A/B tests, dashboards, or anomaly detection
- Checking whether a metric is trustworthy for decisions
Do NOT Use When
- Building raw data pipelines end to end
- Product strategy by itself
- Backend or data-platform implementation reviews
What You Own
- Metric definitions and guardrails
- Telemetry instrumentation quality
- Experiment design and interpretation risk
- Dashboard hierarchy and monitoring logic
Working Method
- Identify the decision the metric/experiment supports.
- Audit definition, instrumentation path, failure modes.
- Load reference files as needed.
- Separate measurement quality from business interpretation.
- Produce measurement plan or audit with explicit risks.
Reference Map
references/metrics-and-instrumentation.md— KPI definition, event design, instrumentation qualityreferences/experiments-and-dashboards.md— A/B tests, dashboards, anomaly detection, funnels
Default Output
ANALYTICS REVIEW
================
Measurement Goal: decision to support, primary/guardrail metrics
Instrumentation: event coverage, schema/attribution gaps
Experiment/Monitoring: test design or dashboard quality, interpretation risks
Recommendation: highest-priority fixes before relying on numbers
Key Concepts (Inline Fallback)
If reference files are unavailable:
- KPI (Key Performance Indicator): The number that tells you whether the bet is working. Must be measurable, attributable, and actionable.
- Guardrail Metric: What must NOT get worse while you optimize the KPI. Prevents tunnel vision.
- Sample Bias: When your measurement population doesn't match your target population. Common: survivorship bias (only measuring players who stayed).
- Attribution Gap: When you can't tell which change caused the metric movement. Multiple changes shipped together = attribution chaos.
- Instrumentation Coverage: Percentage of user actions that produce telemetry events. Below 80% coverage = blind spots in analysis.
What ships with it
2 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.
- 9d ago First seen · 57 lines · 39 tokens per session scan A fa675a18dad7
role-analytics-engineer is a skill published in the GitHub repository saemihemma/lead-producer-oss (2 stars, last pushed 5d ago), licensed MIT. It adds 39 tokens to every session and 511 once invoked, about $0.0002 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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