Awesome GitHub Copilot is a community collection of custom agents, instructions, skills, hooks, workflows, plugins, and configuration for GitHub Copilot. It helps Copilot users customize coding and development tasks. Catalogue entries are individual Copilot add-ons from this collection.
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.
git clone --depth 1 https://github.com/github/awesome-copilotWrote 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/agents/github/awesome-copilot/se-responsible-ai-code)<a href="https://agentmods.dev/agents/github/awesome-copilot/se-responsible-ai-code"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/se-responsible-ai-code/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/agents/github/awesome-copilot/se-responsible-ai-code"><img src="https://agentmods.dev/badge/agents/github/awesome-copilot/se-responsible-ai-code.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 | $0.00026 | $0.01481 |
| Opus 5.5 | $0.00010 | $0.00592 |
| Sonnet 5.5 | $0.00005 | $0.00296 |
| Haiku 4.5 | $0.00003 | $0.00148 |
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 1mo 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
3 near-identical copies found in the catalogue:
- SE: Responsible AI — 100% identical, 0 lines differ
- SE: Responsible AI — 100% identical, 0 lines differ
- SE: Responsible AI — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 200 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Responsible AI Specialist
Prevent bias, barriers, and harm. Every system should be usable by diverse users without discrimination.
Your Mission: Ensure AI Works for Everyone
Build systems that are accessible, ethical, and fair. Test for bias, ensure accessibility compliance, protect privacy, and create inclusive experiences.
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.
- 1mo ago First seen · 200 lines · 26 tokens per session scan A e893e9c3c9c3
SE: Responsible AI is an agent published in the GitHub repository github/awesome-copilot (40,285 stars, last pushed today), licensed MIT. It adds 26 tokens to every session and 1,481 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-09-03.
Other agents, from other repositories
ndv-accessibility
Accessibility auditor. Use when auditing for WCAG violations, ARIA errors, keyboard navigation failures, screen reader compatibility, contrast ratios, or any question about whether all users can access and operate the interface. Hyperempathic universal design cognition — processes every interface from every user's…
ndv-design
Design judgment specialist. Use when UI code, components, or flows need visual and UX assessment — or when a design decision needs principled justification. Reads code as its rendered visual output. The broken hierarchy, the absent affordance, the interaction that taxes working memory beyond its limit — these register…
aiox-ux-design-expert
🎨 UX/UI Designer & Design System Architect (Uma). Complete design workflow - user research, wireframes, design systems, token extraction, component building, and quality assurance Activate with /aiox-ux-design-expert or spawnsubagent subagenttype="aiox-ux-design-expert".
prompt-ux-designer
Designs the prompt input experience for AI features — input pattern, suggestion system, feedback loop, history, and constraints. Use before implementing any AI prompt input or chat interface.
taste-judge
Final-gate taste judge running a 3-lens internal panel over what rubrics cannot capture. Use SPARINGLY on high-stakes work (signature designs, hero copy, brand directions, architecture choices, must-be-right documents), normally after a deliverable has passed two clean verifier sweeps. Also use to rank best-of-N…
ux-researcher
UX researcher for personas, task analysis, usability studies, field work, and prototype critique.