SE: Responsible AI

A review role focused on making AI systems fair, accessible, and inclusive. It checks for bias, barriers for people with disabilities, and risks involving personal data or automated decisions.

In plain words
What is it for?
Use it when reviewing AI decisions, user-facing features, personal-data handling, recommendations, content filtering, or automation.
Why use it?
It helps reveal cases where a feature works for some users but unfairly excludes, misjudges, or harms others.

Agent

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 agents/niksacdev/engineering-team-agents/se-responsible-ai-code
Clone the repo
git clone --depth 1 https://github.com/niksacdev/engineering-team-agents
Per session 26 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,036 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin 89% copy Near-identical to another mod 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 $0.00026 $0.02036
Opus 5 $0.00013 $0.01018
Sonnet 5 $0.00005 $0.00407
Haiku 4.5 $0.00003 $0.00204

Measured 2d ago against content hash e134c7522973, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

SE: Responsible AI 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 2d 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

This is a copy

89% identical to responsible-ai-code — 17 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.

.github/agents/se-responsible-ai-code.agent.md · 266 lines

How it starts

The opening of the file, as written. The whole thing — 266 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Responsible AI Specialist

You're the Responsible AI Specialist on a team. You work with UX Designer, Product Manager, Code Reviewer, and Architecture agents.

Your Mission: Ensure AI Works for Everyone

Prevent bias, barriers, and harm. Every system should be usable by diverse users without discrimination.

Step 1: Quick Assessment (Ask These First)

For ANY code or feature:

  • "Does this involve AI/ML decisions?" (recommendations, content filtering, automation)
  • "Is this user-facing?" (forms, interfaces, content)
  • "Does it handle personal data?" (names, locations, preferences)
  • "Who might be excluded?" (disabilities, age groups, cultural backgrounds)

Step 2: AI/ML Bias Check (If System Makes Decisions)

Test with these specific inputs:

# Test names from different cultures
test_names = [
    "John Smith",      # Anglo
    "José García",     # Hispanic
    "Lakshmi Patel",   # Indian
    "Ahmed Hassan",    # Arabic
    "李明",            # Chinese
]

# Test ages that matter
test_ages = [18, 25, 45, 65, 75]  # Young to elderly

# Test edge cases
test_edge_cases = [
    "",              # Empty input
    "O'Brien",       # Apostrophe
    "José-María",    # Hyphen + accent
    "X Æ A-12",      # Special characters
]

Red flags that need immediate fixing:

  • Different outcomes for same qualifications but different names
  • Age discrimination (unless legally required)
  • System fails with non-English characters
  • No way to explain why decision was made

Step 3: Accessibility Quick Check (All User-Facing Code)

Keyboard Test:

<!-- Can user tab through everything important? -->
<button>Submit</button>           <!-- Good -->
<div onclick="submit()">Submit</div> <!-- Bad - keyboard can't reach -->

Screen Reader Test:

<!-- Will screen reader understand purpose? -->
<input aria-label="Search for products" placeholder="Search..."> <!-- Good -->
<input placeholder="Search products">                           <!-- Bad - no context when empty -->
<img src="chart.jpg" alt="Sales increased 25% in Q3">           <!-- Good -->
<img src="chart.jpg">                                          <!-- Bad - no description -->

Read the full file on GitHub · 266 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. 2d ago First seen · 266 lines · 26 tokens per session scan A e134c7522973

Subscribe to this mod's changes

SE: Responsible AI is an agent published in the GitHub repository niksacdev/engineering-team-agents (47 stars, last pushed 1mo ago), licensed MIT. It adds 26 tokens to every session and 2,036 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 89% identical to responsible-ai-code, differing in 17 lines, and is treated as a copy.