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 aAAaqwq/AGI-Super-Team --skill campaign-analyticsgit clone --depth 1 https://github.com/aAAaqwq/AGI-Super-TeamWrote 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/aaaaqwq/agi-super-team/campaign-analytics)<a href="https://agentmods.dev/skills/aaaaqwq/agi-super-team/campaign-analytics"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/campaign-analytics/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/aaaaqwq/agi-super-team/campaign-analytics"><img src="https://agentmods.dev/badge/skills/aaaaqwq/agi-super-team/campaign-analytics.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 | $0.00023 | $0.04378 |
| Opus 5 | $0.00012 | $0.02189 |
| Sonnet 5 | $0.00005 | $0.00876 |
| Haiku 4.5 | $0.00002 | $0.00438 |
Grade A, and why
campaign-analytics 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 4d 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 — 390 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Campaign Analytics
Production-grade campaign performance analysis with multi-touch attribution modeling, funnel conversion analysis, and ROI calculation. Three Python CLI tools provide deterministic, repeatable analytics using standard library only -- no external dependencies, no API calls, no ML models.
Table of Contents
- Capabilities
- Input Requirements
- Output Formats
- How to Use
- Scripts
- Reference Guides
- Best Practices
- Limitations
Capabilities
- Multi-Touch Attribution: Five attribution models (first-touch, last-touch, linear, time-decay, position-based) with configurable parameters
- Funnel Conversion Analysis: Stage-by-stage conversion rates, drop-off identification, bottleneck detection, and segment comparison
- Campaign ROI Calculation: ROI, ROAS, CPA, CPL, CAC metrics with industry benchmarking and underperformance flagging
- A/B Test Support: Templates for structured A/B test documentation and analysis
- Channel Comparison: Cross-channel performance comparison with normalized metrics
- Executive Reporting: Ready-to-use templates for campaign performance reports
Input Requirements
All scripts accept a JSON file as positional input argument. See assets/sample_campaign_data.json for complete examples.
Reusable assets:
assets/ab_test_template.mdassets/campaign_report_template.mdassets/channel_comparison_template.mdassets/expected_output.jsonassets/sample_campaign_data.json
Attribution Analyzer
{
"journeys": [
{
"journey_id": "j1",
"touchpoints": [
{"channel": "organic_search", "timestamp": "2025-10-01T10:00:00", "interaction": "click"},
{"channel": "email", "timestamp": "2025-10-05T14:30:00", "interaction": "open"},
{"channel": "paid_search", "timestamp": "2025-10-08T09:15:00", "interaction": "click"}
],
"converted": true,
"revenue": 500.00
}
]
}
What ships with it
11 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.
- assets/ab_test_template.md 3.4 KB
- assets/campaign_report_template.md 3.9 KB
- assets/channel_comparison_template.md 4.9 KB
- assets/expected_output.json 3.8 KB
- assets/sample_campaign_data.json 4.6 KB
- references/attribution-models-guide.md 9.4 KB
- references/campaign-metrics-benchmarks.md 8.8 KB
- references/funnel-optimization-framework.md 11 KB
- scripts/attribution_analyzer.py 12 KB runs code
- scripts/campaign_roi_calculator.py 18 KB runs code
- scripts/funnel_analyzer.py 11 KB runs code
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.
- 4d ago First seen · 390 lines · 23 tokens per session scan A 8a0a548e5fc7
campaign-analytics is a skill published in the GitHub repository aAAaqwq/AGI-Super-Team (91 stars, last pushed 2d ago), licensed MIT. It adds 23 tokens to every session and 4,378 once invoked, about $0.0001 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 2026-09-05.
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eldar
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Suggest the most likely next workflow action based on current context.
review-deep
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