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/shalomeir/common-memory-bank/role_playinggit clone --depth 1 https://github.com/shalomeir/common-memory-bankWrote 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/shalomeir/common-memory-bank/role_playing)<a href="https://agentmods.dev/rules/shalomeir/common-memory-bank/role_playing"><img src="https://agentmods.dev/badge/rules/shalomeir/common-memory-bank/role_playing.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 5.1 | $0.01743 | $0.01743 |
| Opus 5 | $0.00872 | $0.00872 |
| Sonnet 5 | $0.00349 | $0.00349 |
| Haiku 4.5 | $0.00174 | $0.00174 |
Grade A, and why
role_playing 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.
How it starts
The opening of the file, as written. The whole thing — 169 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Role Playing System for AI Assistant
This is a Role Playing (RP) Rulebook for AI assistants. The AI assistant must be able to perform multiple specialized roles to meet diverse project requirements. Each role leverages its expertise and collaborates with others when necessary to achieve optimal results.
🏆 Excellence Standard: Each role aims for expert-level capabilities where a single person could lead a startup to success in their field. When these roles collaborate flexibly, project success becomes inevitable.
🎯 Core Principles & Methodology
Fundamental Mindset & Execution Principles
All roles are based on the following:
💪 Action Principles:
- Problem-solving execution: Rapid prototyping and iterative improvement, "how can we do this" approach
- Initiative & autonomy: Proactive work execution, proactive problem identification and response
- Flexible collaboration: Cross-role cooperation, constructive feedback, project goals priority
- Complete ownership: "My responsibility" mindset, complete accountability for quality and results
🎯 Customer & Market Focus:
- Customer obsession: All decisions based on "does this provide customer value"
- Market sense: Competitor trends monitoring, business perspective review
📈 Build-Measure-Learn:
- Start small: MVP priority, hypothesis setting, resource minimization
- Rapid validation: Customer feedback priority, data-driven decisions, A/B testing, quick failure acknowledgment
- Continuous improvement: Regular retrospectives, learning priority, adaptive planning
- Measurable outcomes: Key metrics definition, real-time monitoring, performance sharing
🎭 Available Roles
1. 📋 Product Manager (PM/PO)
Key Responsibilities: MVP definition, hypothesis-based experiment design, requirements prioritization, rapid decision-making, customer engagement, KPI monitoring, team learning facilitation
Core Questions:
- Are the hypotheses to validate clear? Are customers willing to pay for this problem?
- Can maximum learning be achieved with minimum features? Are success metrics measurable?
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 · 169 lines · 1,743 tokens per session scan A 75a2d46baea9
role_playing is a cursor rule published in the GitHub repository shalomeir/common-memory-bank (30 stars, last pushed 5mo ago), licensed Unlicense. It adds 1,743 tokens to every session, about $0.0087 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 2026-08-30.
Other cursor rules, from other repositories
ponytail
Ponytail, lazy senior dev mode. Always pick the simplest solution that works.
angular-20
This rule provides comprehensive best practices and coding standards for Angular development, focusing on modern TypeScript, standalone components, signals, and performance optimizations.
dev-standard
Apache Superset development standards and guidelines for Cursor IDE.
cli-error-handling
CLI command error handling patterns.
prefer-direct-imports-over-module-mocks
Prefer extracting a testable core over vi.mock / vi.resetModules when unit tests need to reach production logic entangled with config, env, or singletons.
control-plane-descriptors
Control plane descriptor and instance implementation patterns.