Responsible AI Guide

What it does

When your agent uses it

• Tasks that involve LLM guardrails

Example prompts

• “Use the responsible-ai-guide skill to resource for trustworthy, fair, and ethical AI research. An agent skill from wentorai/research-plugins”
• “/responsible-ai-guide”

Requirements

• Python 3

Workflow steps

5 steps, taken from the first numbered list in SKILL.md.

1. 1Bias auditing: Check models for demographic biases
2. 2Compliance: EU AI Act and regulatory requirements
3. 3Model documentation: Model cards and impact assessments
4. 4Research ethics: Ethical considerations for AI research
5. 5Course material: Teach responsible AI principles

What it can do on your machine

Read from SKILL.md and the folder at commit bf44b3c. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are python and markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  Links to these hosts (documentation or services it may open):

  • github.com
  • fairlearn.org
  • artificialintelligenceact.eu

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Responsible AI Guide loads about 976 tokens when it runs. Until then it costs about 19 tokens; SKILL.md has 143 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~19

When it runs · the whole SKILL.md, loaded when a task matches

~976

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from wentorai/research-plugins at commit bf44b3c, republished under its MIT licence (© wentorai). 143 words, ~976 tokens.

name

responsible-ai-guide

description

Resources for trustworthy, fair, and ethical AI research

Responsible AI
├── Fairness
│   ├── Bias detection (data, model, outcome)
│   ├── Fairness metrics (demographic parity, equalized odds)
│   ├── Bias mitigation (pre/in/post-processing)
│   └── Intersectional fairness
├── Explainability
│   ├── Feature attribution (SHAP, LIME, IG)
│   ├── Concept-based (TCAV, concept bottleneck)
│   ├── Counterfactual explanations
│   └── Mechanistic interpretability
├── Privacy
│   ├── Differential privacy
│   ├── Federated learning
│   ├── Membership inference attacks
│   └── Machine unlearning
├── Robustness
│   ├── Adversarial attacks/defenses
│   ├── Distribution shift
│   ├── Uncertainty quantification
│   └── Out-of-distribution detection
├── Safety & Alignment
│   ├── RLHF and preference learning
│   ├── Constitutional AI
│   ├── Red teaming
│   └── Guardrails and filters
└── Governance
    ├── Model cards
    ├── Datasheets for datasets
    ├── AI impact assessments
    └── Regulatory compliance (EU AI Act)

from fairlearn.metrics import MetricFrame
from sklearn.metrics import accuracy_score, recall_score

# Assess fairness across demographic groups
metrics = MetricFrame(
    metrics={
        "accuracy": accuracy_score,
        "recall": recall_score,
    },
    y_true=y_test,
    y_pred=y_pred,
    sensitive_features=demographics,
)

print("Overall:")
print(metrics.overall)
print("\nBy group:")
print(metrics.by_group)
print("\nDifference (max - min):")
print(metrics.difference())

### Foundations
1. "Fairness and Machine Learning" (Barocas, Hardt, Narayanan)
2. "Datasheets for Datasets" (Gebru et al., 2021)
3. "Model Cards for Model Reporting" (Mitchell et al., 2019)

### Fairness
4. "On Fairness and Calibration" (Pleiss et al., 2017)
5. "Fairness Through Awareness" (Dwork et al., 2012)

### Explainability
6. "A Unified Approach to Interpreting Model Predictions" (SHAP)
7. "Why Should I Trust You?" (LIME, Ribeiro et al., 2016)

### Safety
8. "Constitutional AI" (Bai et al., 2022)
9. "Red Teaming Language Models" (Perez et al., 2022)
10. "Scaling Monosemanticity" (Anthropic, 2024)

© wentorai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/domains/ai-ml/responsible-ai-guide of wentorai/research-plugins.

Open the folder on GitHubat commit bf44b3c

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in wentorai/research-plugins, which our catalogue first saw on October 7, 2026.

• brycewang-stanford/Auto-Empirical-Research-Skillsskills/43-wentorai-research-plugins/skills/domains/ai-ml/responsible-ai-guide