What is 002-pareto-prioritization?

A set of prioritisation rules based on the 80/20 idea: a small number of causes often create most of the impact.

Use it to rank tasks or recommendations by expected benefit, effort, time saved, or risk reduced.

It helps teams focus on the biggest risks and highest-value improvements instead of spreading effort across minor issues.

002-pareto-prioritization — Cursor rule for Cursor by hamzaamjad: A set of prioritisation rules based on the 80/20…

Install

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.

agentmods

npx agentmods add rules/hamzaamjad/cursor-rules/002-pareto-prioritization

Clone the repo

git clone --depth 1 https://github.com/hamzaamjad/cursor-rules

Made for: Cursor.

Wrote 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.

README.md

[![agentmods](https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/002-pareto-prioritization.svg)](https://agentmods.dev/rules/hamzaamjad/cursor-rules/002-pareto-prioritization)

Your own site

<a href="https://agentmods.dev/rules/hamzaamjad/cursor-rules/002-pareto-prioritization"><img src="https://agentmods.dev/badge/rules/hamzaamjad/cursor-rules/002-pareto-prioritization.svg" alt="Measured on agentmods" height="20"></a>

Per session 1,521 This file is loaded in full into every session.

When invoked 1,521 The same file — it is already loaded in full.

Security scan A 0 findings. Scan, not verified.

Origin original No closer match found in the catalogue.

Token cost

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	$0.01521	$0.01521
Opus 5	$0.00760	$0.00760
Sonnet 5	$0.00304	$0.00304
Haiku 4.5	$0.00152	$0.00152

Measured 5d ago against content hash 50cea1dfa270, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-06, from the pricing page.

Security

Grade A, and why

002-pareto-prioritization 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 5d 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.

rules/000-core/002-pareto-prioritization.mdc · 61 lines

How it starts

The opening of the file, as written. The whole thing — 61 lines — stays where its author put it; the contents beside it link to each section on GitHub.

80-20-prioritization.mdc

Purpose: To guide AI assistants and developers in focusing efforts and recommendations on the most impactful elements, aligning with the Pareto principle (80/20 rule). Ensure resources are directed towards tasks yielding the highest value or mitigating the most significant risks. Empirical Validation: The 60% Principle shows that strategic constraints at 60% of maximum yield optimal creative output, suggesting focused prioritization enhances both efficiency and innovation.
Requirements:
- Identify and highlight the top 1-3 drivers or risks associated with a problem or proposal.
- Rank recommendations explicitly based on estimated Return on Investment (ROI) or impact/effort ratio.
- Quantitative ROI Calculation:
  - Time ROI: (Hours Saved × Frequency) / Implementation Hours
  - Risk ROI: (Potential Loss Prevented × Probability) / Mitigation Cost
  - Performance ROI: (% Improvement × Business Value) / Development Cost
  - Target minimum ROI: 3:1 for immediate implementation
- Clearly state the criteria or heuristics used for ranking (e.g., estimated time saved, revenue generated, risk reduction level, user impact).
- Avoid detailed exploration or implementation of low-impact features or optimizations unless specifically requested.
- Cognitive Load Distribution: Ensure AI handles 60-70% of mechanistic processing, leaving 30-40% for human strategic oversight.
Validation:
- Check: Does the output explicitly list top drivers/risks separately or at the beginning?
- Check: Are recommendations presented in a ranked or prioritized list?
- Check: Is the basis for ranking (ROI, impact/effort, etc.) mentioned?
- Check: Does the depth of detail correspond to the priority of the item?
Examples:
- Scenario: Proposing database optimizations.
  - Weak: "We could optimize the users table index and rewrite the orders query."
  - Improved: "1. Optimize orders query (High ROI): Addresses primary source of user-reported latency (est. 80% of slowdown). Effort: Medium. 2. Optimize users table index (Low ROI): Minor performance gain for admin view. Effort: Low. Priority is the orders query."
- Scenario: Analyzing technical debt.
  - Weak: "There's tech debt in auth and logging."
  - Improved: "Top Risk: Outdated auth library (High - Security Vulnerability). Recommendation (Highest ROI): Upgrade auth library immediately (Effort: High). Other Issue: Inconsistent logging format (Medium - Hinders Debugging). Recommendation (Medium ROI): Refactor logging module next quarter (Effort: Medium)."
Changes: Added explicit requirements for stating ranking criteria, refined purpose, provided concrete validation checks, and added comparative examples for different scenarios.
Source References: .cursor/rules/80-20-prioritization.mdc; Stack Overflow: Estimating ROI for Tech Debt; LinkedIn: Measure ROI of Refactoring# 80-20-prioritization.mdc
Purpose: To guide AI assistants and developers in focusing efforts and recommendations on the most impactful elements, aligning with the Pareto principle (80/20 rule). Ensure resources are directed towards tasks yielding the highest value or mitigating the most significant risks. Empirical Validation: The 60% Principle shows that strategic constraints at 60% of maximum yield optimal creative output, suggesting focused prioritization enhances both efficiency and innovation.
Requirements:
- Identify and highlight the top 1-3 drivers or risks associated with a problem or proposal.
- Rank recommendations explicitly based on estimated Return on Investment (ROI) or impact/effort ratio.

Read the full file on GitHub · 61 lines

Changes

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.

5d ago First seen · 61 lines · 1,521 tokens per session scan A 50cea1dfa270

Subscribe to this mod's changes

002-pareto-prioritization is a cursor rule published in the GitHub repository hamzaamjad/cursor-rules (2 stars, last pushed 1y ago), licensed MIT. It adds 1,521 tokens to every session, about $0.0076 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.

Planning Productivity

002-pareto-prioritization

Getting it into your agent

Wrote this? Show the measurements

What it costs to keep this loaded

Grade A, and why

Nothing flagged

How it starts

80-20-prioritization.mdc

What this file has done since we first saw it

Other cursor rules, from other repositories

reflection-basic

cursor-fable-rules

git-workflow

feedback-enhanced

122-swift-development

ponytail