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/KIMISKI33/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/kimiski33/awesome-copilot/se-responsible-ai-code)<a href="https://agentmods.dev/agents/kimiski33/awesome-copilot/se-responsible-ai-code"><img src="https://agentmods.dev/badge/agents/kimiski33/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/kimiski33/awesome-copilot/se-responsible-ai-code"><img src="https://agentmods.dev/badge/agents/kimiski33/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.
This is a copy
100% identical to SE: Responsible AI — 0 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.
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 KIMISKI33/awesome-copilot (1 stars, last pushed 1mo ago), 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. It is 100% identical to SE: Responsible AI, differing in 0 lines, and is treated as a copy.
Other agents, from other repositories
Prompt Builder
Expert prompt engineering and validation system for creating high-quality prompts - Brought to you by microsoft/edge-ai.
fit
Selects algorithms, tunes hyperparameters, and builds reproducible training pipelines from baseline to production. Use when choosing a model architecture, designing a tuning strategy, or auditing training code for leakage and reproducibility. Trigger with "design training pipeline", "tune model hyperparameters".
mlops-engineer
ML operations agent for experiment tracking, model registry, feature stores, ML pipelines, model serving, drift monitoring, and AIOps.
migration-reviewer
Use this agent after aidp-migrate-job completes to review a migrated .ipynb for correctness (NOT just "did it run"). Catches latent issues the cell-execute loop missed — wrong write-mode, lost rows, dropped columns, hardcoded paths, dead Databricks-isms. Outputs a structured review report.
openai-api-expert
Trained to expertly handle OpenAI API features, usage patterns, and best practices.
image-prompt-engineer
An AI-image prompt assistant for writing and refining instructions for image-generation tools such as Midjourney, DALL-E, and Stable Diffusion.