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 andycungkrinx91/konoha --skill building-role-mining-for-rbac-optimizationgit clone --depth 1 https://github.com/andycungkrinx91/konohaWrote 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/andycungkrinx91/konoha/building-role-mining-for-rbac-optimization)<a href="https://agentmods.dev/skills/andycungkrinx91/konoha/building-role-mining-for-rbac-optimization"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/building-role-mining-for-rbac-optimization/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/andycungkrinx91/konoha/building-role-mining-for-rbac-optimization"><img src="https://agentmods.dev/badge/skills/andycungkrinx91/konoha/building-role-mining-for-rbac-optimization.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00040 | $0.02191 |
| Opus 5.5 | $0.00016 | $0.00876 |
| Sonnet 5.5 | $0.00008 | $0.00438 |
| Haiku 4.5 | $0.00004 | $0.00219 |
Grade A, and why
building-role-mining-for-rbac-optimization 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 today.
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.
This is a copy
100% identical to building-role-mining-for-rbac-optimization — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 261 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Building Role Mining for RBAC Optimization
Overview
Role mining is the process of analyzing existing user-permission assignments to discover optimal roles for a Role-Based Access Control (RBAC) system. Organizations accumulate excessive permissions over time through job changes, project assignments, and ad-hoc access grants, leading to "role explosion" where thousands of granular roles exist with significant overlap. Role mining uses data analysis -- including clustering algorithms, formal concept analysis, and graph-based methods -- to consolidate permissions into a minimal set of roles that accurately represent business functions while enforcing least privilege.
When to Use
- When deploying or configuring building role mining for rbac optimization capabilities in your environment
- When establishing security controls aligned to compliance requirements
- When building or improving security architecture for this domain
- When conducting security assessments that require this implementation
Prerequisites
- Export of current user-permission assignments (CSV/database)
- Identity governance platform or directory service access
- Python 3.9+ with pandas, scikit-learn, numpy
- Understanding of organizational structure and job functions
- Stakeholder access for role validation workshops
Core Concepts
Role Mining Approaches
| Approach | Description | Best For |
|---|---|---|
| Bottom-Up | Analyze existing permissions to discover common patterns | Large datasets with organic permission growth |
| Top-Down | Design roles from business requirements and job descriptions | Greenfield RBAC or organizational restructuring |
| Hybrid | Combine bottom-up analysis with top-down business validation | Most production environments |
Role Mining Algorithms
1. Permission Clustering: Group users with similar permission sets using k-means or hierarchical clustering. Users in the same cluster share a common role.
2. Formal Concept Analysis (FCA): Mathematical framework that identifies complete set of concepts (user groups sharing exact permission sets) from a binary user-permission matrix.
What ships with it
6 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.
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.
- today First seen · 261 lines · 40 tokens per session scan A 6d8df79f1a10
building-role-mining-for-rbac-optimization is a skill published in the GitHub repository andycungkrinx91/konoha (12 stars, last pushed today), licensed MIT. It adds 40 tokens to every session and 2,191 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to building-role-mining-for-rbac-optimization, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
building-role-mining-for-rbac-optimization
Apply bottom-up and top-down role mining techniques to discover optimal RBAC roles from existing user-permission assignments, reducing role explosion and enforcing least privilege.
building-role-mining-for-rbac-optimization
Apply bottom-up and top-down role mining techniques, including clustering algorithms and formal concept analysis, to discover optimal RBAC roles from existing user-permission assignments, consolidating overlapping roles and enforcing least privilege. Use when an identity program needs to reduce role explosion or…
building-role-mining-for-rbac-optimization
Apply bottom-up and top-down role mining techniques to discover optimal RBAC roles from existing user-permission assignments, reducing role explosion and enforcing least privilege.
building-role-mining-for-rbac-optimization
Apply bottom-up and top-down role mining techniques to discover optimal RBAC roles from existing user-permission assignments, reducing role explosion and enforcing least privilege.
implementing-rbac-hardening-for-kubernetes
Harden Kubernetes Role-Based Access Control by implementing least-privilege policies, auditing role bindings, eliminating cluster-admin sprawl, and integrating external identity providers.
auditing-kubernetes-rbac-privilege-escalation
Find over-permissive RBAC roles and service-account token abuse paths in Kubernetes using kubectl auth can-i, rbac-police, kubectl-who-can, and rakkess during authorized cluster security reviews.