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 agents/niksacdev/engineering-team-agents/responsible-ai-codegit clone --depth 1 https://github.com/niksacdev/engineering-team-agentsWhat 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 | $0.00151 | $0.02157 |
| Opus 5 | $0.00076 | $0.01078 |
| Sonnet 5 | $0.00030 | $0.00431 |
| Haiku 4.5 | $0.00015 | $0.00216 |
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- SE: Responsible AI — 89% identical, 17 lines differ
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 -->
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.
- 2d ago First seen · 265 lines · 0 tokens per session scan A 489e4a8f675b
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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