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/ssdeanx/ssd-ai/se-responsible-ai-codegit clone --depth 1 https://github.com/ssdeanx/ssd-aiWrote 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/ssdeanx/ssd-ai/se-responsible-ai-code)<a href="https://agentmods.dev/agents/ssdeanx/ssd-ai/se-responsible-ai-code"><img src="https://agentmods.dev/badge/agents/ssdeanx/ssd-ai/se-responsible-ai-code.svg" alt="Measured on agentmods" 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 | $0.00013 | $0.00740 |
| Sonnet 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 5d 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.
- 5d ago First seen · 200 lines · 26 tokens per session scan A e893e9c3c9c3
SE: Responsible AI is an agent published in the GitHub repository ssdeanx/ssd-ai (3 stars, last pushed 8mo ago), licensed MIT. It adds 26 tokens to every session and 1,481 once invoked, about $0.0001 per session on Opus 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
tool-developer
Builds new UEFN Toolbelt tools autonomously. Audits the registry for duplicates, writes the tool, bumps counts, runs drift check, and gives the user exact test instructions.
verse-deployer
Verse codegen and error-fix loop for UEFN Toolbelt. Handles Phases 5–7 of the pipeline — write Verse, deploy, read build errors, fix, repeat until SUCCESS.
release-manager
Owns the end-to-end modelcontextprotocol/csharp-sdk release process, orchestrating the prepare-release and publish-release skills (and the bump-version and breaking-changes skills they build on) across five stages: prepare (assess SemVer, bump the version, run ApiCompat/ApiDiff, review docs, draft release notes, open…
step-generator
Generate test steps from natural language descriptions. Use when: (1) User describes a test scenario in plain English, (2) Complex UI interactions need step-by-step breakdown, (3) User wants to add steps but doesn't know the exact format.
Plan
Analyze a user task and return a clear, ordered implementation plan before coding.
ux-analyst
Expert in usability and user journey analysis. Use this agent to map user flows, identify friction points, audit onboarding, and evaluate whether the UI makes sense from the perspective of someone trying to accomplish a goal — not just someone reading the code. Use proactively when adding new features, redesigning…