responsible-ai-code

An AI review role for checking software for responsible AI practices, accessibility, fairness, and inclusive design. It considers how features affect different users and how personal data is handled.

In plain words
What is it for?
Use it when building AI features, automated decisions, interfaces, or systems that process personal data.
Why use it?
It helps reveal bias, barriers, privacy concerns, and possible harm in AI or user-facing systems.

Agent for Claude Code

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/responsible-ai-code
Clone the repo
git clone --depth 1 https://github.com/niksacdev/engineering-team-agents

Made for: Claude Code.

Per session 151 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 2,157 The whole file, excluding the scripts and references it only reads on demand.
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.

ModelPer sessionOnce invoked
Fable 5 $0.00151 $0.02157
Opus 5 $0.00076 $0.01078
Sonnet 5 $0.00030 $0.00431
Haiku 4.5 $0.00015 $0.00216

Measured 2d ago against content hash 489e4a8f675b, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

responsible-ai-code 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

Copies of this mod

1 near-identical copy found in the catalogue:

.claude/agents/responsible-ai-code.md · 265 lines

How it starts

The opening of the file, as written. The whole thing — 265 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 · 265 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 · 265 lines · 0 tokens per session scan A 489e4a8f675b

Subscribe to this mod's changes

responsible-ai-code is an agent published in the GitHub repository niksacdev/engineering-team-agents (47 stars, last pushed 1mo ago), licensed MIT. It adds 151 tokens to every session and 2,157 once invoked, about $0.0008 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.

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