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 agentmods add rules/hamzaamjad/cursor-rules/430-80-20-prioritization-data-analyticsgit clone --depth 1 https://github.com/hamzaamjad/cursor-rulesWrote 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/rules/hamzaamjad/cursor-rules/430-80-20-prioritization-data-analytics)<a href="https://agentmods.dev/rules/hamzaamjad/cursor-rules/430-80-20-prioritization-data-analytics"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/430-80-20-prioritization-data-analytics.svg" alt="Measured on agentmods" 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 | $0.00010 | $0.00928 |
| Opus 5 | $0.00005 | $0.00464 |
| Sonnet 5 | $0.00002 | $0.00186 |
| Haiku 4.5 | $0.00001 | $0.00093 |
Grade A, and why
430-80-20-prioritization-data-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 — 114 lines — stays where its author put it; the contents beside it link to each section on GitHub.
80-20 Data Analytics Prioritization
Purpose: Focus on 20% of analytics tasks delivering 80% of value.
ROI Scoring Framework
def calculate_roi(task):
# Score each 1-5
metrics = {
'revenue_impact': task.revenue_delta, # Direct revenue effect
'process_efficiency': task.hours_saved, # Hours × users × cost
'data_quality': task.error_reduction, # Error % × business impact
'scalability': task.growth_headroom # Capacity for growth
}
# Weighted impact score
impact = (
metrics['revenue_impact'] * 0.4 +
metrics['process_efficiency'] * 0.3 +
metrics['data_quality'] * 0.2 +
metrics['scalability'] * 0.1
)
# ROI = Impact / Effort
return impact / task.effort_score
Decision Matrix
| Impact/Effort | Low Effort (1-2) | High Effort (3-5) |
|---|---|---|
| High Impact (4-5) | 🎯 DO FIRST | 📅 PLAN CAREFULLY |
| Low Impact (1-3) | ⚡ QUICK WINS | ❌ AVOID |
Scoring Guide
Impact Metrics (1-5)
Revenue Impact:
5: >$1M or >5% revenue
4: $500K-1M or 2-5%
3: $100-500K or 1-2%
2: $10-100K or <1%
1: <$10K or indirect
Process Efficiency:
5: >2000 hours/year saved
4: 500-2000 hours/year
3: 100-500 hours/year
2: 20-100 hours/year
1: <20 hours/year
Effort Scale
- 1: Hours (trivial)
- 2: Days (simple)
- 3: Weeks (moderate)
- 4: Months (complex)
- 5: Quarters (major)
Application Examples
Churn Model
task = {
'revenue_impact': 5, # $1.2M ARR recovery
'process_efficiency': 3, # 200 hours/year saved
'data_quality': 4, # 8% error reduction
'scalability': 3, # Handles 2x growth
'effort_score': 3 # 3 weeks development
}
# ROI = 4.3 / 3 = 1.43 → HIGH PRIORITY
Report Formatting
task = {
'revenue_impact': 1, # No direct impact
'process_efficiency': 1, # 10 hours/year
'data_quality': 1, # Cosmetic only
'scalability': 1, # Not relevant
'effort_score': 2 # 2 days work
}
# ROI = 1.0 / 2 = 0.5 → LOW PRIORITY
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 · 114 lines · 10 tokens per session scan A 47485faa2c11
430-80-20-prioritization-data-analytics is a cursor rule published in the GitHub repository hamzaamjad/cursor-rules (2 stars, last pushed 1y ago), licensed MIT. It adds 10 tokens to every session and 928 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 2026-08-31.
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