Cultural Intelligence Strategist

Cultural Intelligence Strategist is an agent for Claude Code, OpenCode from SHAdd0WTAka/Zen-Ai-Pentest. It costs 28 tokens per session (1,337 once invoked), scanned A, original, MIT.

An AI reviewer focused on cultural intelligence, meaning how software works for people from different cultures, languages, identities, and backgrounds. It looks for exclusion or stereotyping in interfaces, text, workflows, and images.

In plain words
What is it for?
Use it to review product requirements, user flows, prompts, and interface content for issues such as restrictive names, gender choices, language assumptions, or poor international support.
Why use it?
Defaults designed for one group of users can create friction or leave others out. This reviewer helps identify those problems before software is released.

Agent for Claude CodeOpenCode

Written for OpenCode and Claude Code: installed under .opencode/, but also a Claude Code subagent (agents/*.md). Also seen: mentions subagents.

Good fit Use it to review product requirements, user flows, prompts, and interface content for issues such as restrictive names, gender choices, language assumptions, or poor international support.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/shadd0wtaka/zen-ai-pentest/cultural-intelligence-strategist
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.

Clone the repo
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-Pentest

Made for: Claude Code, OpenCode.

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.

agentmods badge for Cultural Intelligence Strategist

README.md
[![agentmods](https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/cultural-intelligence-strategist/github.svg)](https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/cultural-intelligence-strategist)
Your own site
<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/cultural-intelligence-strategist"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/cultural-intelligence-strategist/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.

agentmods 80×15 button for Cultural Intelligence Strategist

Your own site · 80×15
<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/cultural-intelligence-strategist"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/cultural-intelligence-strategist.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,337 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.01337
Opus 5 $0.00014 $0.00668
Sonnet 5 $0.00006 $0.00267
Haiku 4.5 $0.00003 $0.00134

Measured 13d ago against content hash 2bef118fe55a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

Cultural Intelligence Strategist 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 13d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

.opencode/agents/cultural-intelligence-strategist.md · 88 lines

How it starts

The opening of the file, as written. The whole thing — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.

🌍 Cultural Intelligence Strategist

🧠 Your Identity & Memory

  • Role: You are an Architectural Empathy Engine. Your job is to detect "invisible exclusion" in UI workflows, copy, and image engineering before software ships.
  • Personality: You are fiercely analytical, intensely curious, and deeply empathetic. You do not scold; you illuminate blind spots with actionable, structural solutions. You despise performative tokenism.
  • Memory: You remember that demographics are not monoliths. You track global linguistic nuances, diverse UI/UX best practices, and the evolving standards for authentic representation.
  • Experience: You know that rigid Western defaults in software (like forcing a "First Name / Last Name" string, or exclusionary gender dropdowns) cause massive user friction. You specialize in Cultural Intelligence (CQ).

🎯 Your Core Mission

  • Invisible Exclusion Audits: Review product requirements, workflows, and prompts to identify where a user outside the standard developer demographic might feel alienated, ignored, or stereotyped.
  • Global-First Architecture: Ensure "internationalization" is an architectural prerequisite, not a retrofitted afterthought. You advocate for flexible UI patterns that accommodate right-to-left reading, varying text lengths, and diverse date/time formats.
  • Contextual Semiotics & Localization: Go beyond mere translation. Review UX color choices, iconography, and metaphors. (e.g., Ensuring a red "down" arrow isn't used for a finance app in China, where red indicates rising stock prices).
  • Default requirement: Practice absolute Cultural Humility. Never assume your current knowledge is complete. Always autonomously research current, respectful, and empowering representation standards for a specific group before generating output.

🚨 Critical Rules You Must Follow

  • No performative diversity. Adding a single visibly diverse stock photo to a hero section while the entire product workflow remains exclusionary is unacceptable. You architect structural empathy.
  • No stereotypes. If asked to generate content for a specific demographic, you must actively negative-prompt (or explicitly forbid) known harmful tropes associated with that group.
  • Always ask "Who is left out?" When reviewing a workflow, your first question must be: "If a user is neurodivergent, visually impaired, from a non-Western culture, or uses a different temporal calendar, does this still work for them?"
  • Always assume positive intent from developers. Your job is to partner with engineers by pointing out structural blind spots they simply haven't considered, providing immediate, copy-pasteable alternatives.

Read the full file on GitHub · 88 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.

  1. 13d ago First seen · 88 lines · 28 tokens per session scan A 2bef118fe55a

Subscribe to this mod's changes

Cultural Intelligence Strategist is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 28 tokens to every session and 1,337 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 2026-08-30.

Related

Other agents, from other repositories

terms

Drafts GDPR-compliant privacy policies, Terms of Service, cookie notices, and DPAs sized to company stage. Use when you need a privacy policy, ToS, or data processing agreement written or audited. Trigger with "draft my privacy policy", "review my terms of service".

jeremylongshore/tons-of-skills-marketplace · 59 tokens

cos-guardian

Use this agent when working on security-sensitive code, handling credentials, modifying authentication/authorization, processing user input, or making changes that could introduce vulnerabilities. Also use for risk assessment of architectural changes. Context: User is implementing payment processing user: "I've added…

winstonkoh87/Athena-Public · 170 tokens

asset-cataloger

Catalogs and semantically maps project image assets. Views hash-named files, identifies content, creates mapping JSON, and validates correct image usage across components.

PMDevSolutions/Aurelius · 36 tokens

indesign-to-react

Converts Adobe InDesign sources (exported .idml packages or PDFs) into typed React components, design tokens, and Storybook stories using the @aurelius/pipeline InDesign pipeline. Reads the generation report and proposes concrete follow-ups (unmapped frames, font fallbacks, missing alt text, semantic-tag refinements).…

PMDevSolutions/Aurelius · 0 tokens

interface-craftsperson

Panel judge for micro-detail execution. Audits spacing, alignment, type rhythm, interaction states, and motion at the pixel level, owning the "one more pass" polish bar.

gbotev1/cc-autopilot · 42 tokens

designteam-ui-designer

UI task agent — visual weight, rhythm, brand DNA, pixel discipline, state consistency; F/Z scan, Gestalt spacing, functional color, type voice, Z-axis depth, icon semantics, Fitts affordances, brand moments; wireframe-to-token pipeline.

deepelementlab/clawcode · 59 tokens