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 seb1n/awesome-ai-agent-skills --skill analytics-reportinggit clone --depth 1 https://github.com/seb1n/awesome-ai-agent-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/seb1n/awesome-ai-agent-skills/analytics-reporting)<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/analytics-reporting"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/analytics-reporting/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/seb1n/awesome-ai-agent-skills/analytics-reporting"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/analytics-reporting.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00043 | $0.02484 |
| Opus 5 | $0.00022 | $0.01242 |
| Sonnet 5 | $0.00009 | $0.00497 |
| Haiku 4.5 | $0.00004 | $0.00248 |
Grade A, and why
analytics-reporting 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- analytics-reporting — 97% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 127 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics Reporting
This skill enables an AI agent to generate detailed marketing analytics reports that go beyond raw numbers to deliver actionable insights. The agent collects data across traffic, engagement, conversion, and revenue metrics, applies attribution models to understand channel contribution, performs funnel and cohort analysis, and produces executive-ready reports with clear recommendations. The output helps marketing teams make data-driven decisions about budget allocation, campaign optimization, and strategy shifts.
Workflow
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Define reporting scope and KPIs. Clarify the report type (monthly overview, campaign-specific, channel deep-dive) and time period. Establish the primary KPIs to track: traffic metrics (sessions, unique visitors, pageviews), engagement metrics (bounce rate, time on page, pages per session), conversion metrics (conversion rate, leads generated, cost per acquisition), and revenue metrics (customer lifetime value, return on ad spend, marketing-attributed revenue).
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Collect data from all sources. Pull data from web analytics (Google Analytics, Plausible), search console (impressions, clicks, average position), advertising platforms (Google Ads, Meta Ads, LinkedIn Ads), email marketing (Mailchimp, SendGrid), CRM (HubSpot, Salesforce), and social media analytics (native platform insights). Normalize date ranges and metric definitions across sources to ensure comparability.
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Analyze trends and identify patterns. Compare current period metrics against previous period and year-over-year baselines. Calculate growth rates, identify statistically significant changes, and flag anomalies (traffic spikes from viral content, drops from algorithm updates or site outages). Segment data by channel, device, geography, and user cohort to uncover hidden patterns.
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Apply attribution modeling. Move beyond last-click attribution to understand the full customer journey. Apply multi-touch models — linear (equal credit), time-decay (more credit to recent touchpoints), or data-driven (algorithmic) — to evaluate how each channel contributes to conversions. This prevents over-investing in bottom-funnel channels while starving the awareness channels that feed the pipeline.
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 · 127 lines · 43 tokens per session scan A d3a125b3b24d
analytics-reporting is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (179 stars, last pushed 1mo ago), licensed MIT. It adds 43 tokens to every session and 2,484 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 2026-09-03.
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