Iso42001
Sushegaad/Claude-Skills-Governance-Risk-and-Compliance
Expert ISO 42001 AI Management System (AIMS) compliance advisor.
Agent skill
Validating AI Ethics And Fairness
by jeremylongshore in jeremylongshore/tons-of-skills-marketplace
Validate AI/ML models and datasets for bias, fairness, and ethical concerns.
Install Validating AI Ethics And Fairness
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace validating-ai-ethics-and-fairness --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/.curated/validating-ai-ethics-and-fairness .claude/skills/validating-ai-ethics-and-fairness && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "validating-ai-ethics-and-fairness" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/validating-ai-ethics-and-fairness into .claude/skills/validating-ai-ethics-and-fairness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validating-ai-ethics-and-fairness", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/validating-ai-ethics-and-fairnessType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace validating-ai-ethics-and-fairness --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/.curated/validating-ai-ethics-and-fairness .agents/skills/validating-ai-ethics-and-fairness && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "validating-ai-ethics-and-fairness" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/validating-ai-ethics-and-fairness into .agents/skills/validating-ai-ethics-and-fairness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validating-ai-ethics-and-fairness", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace validating-ai-ethics-and-fairness --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/.curated/validating-ai-ethics-and-fairness .cursor/skills/validating-ai-ethics-and-fairness && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "validating-ai-ethics-and-fairness" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/validating-ai-ethics-and-fairness into .cursor/skills/validating-ai-ethics-and-fairness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validating-ai-ethics-and-fairness", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/jeremylongshore/tons-of-skills-marketplace.git --path skills/.curated/validating-ai-ethics-and-fairness--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace validating-ai-ethics-and-fairness --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/.curated/validating-ai-ethics-and-fairness .gemini/skills/validating-ai-ethics-and-fairness && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "validating-ai-ethics-and-fairness" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/validating-ai-ethics-and-fairness into .gemini/skills/validating-ai-ethics-and-fairness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validating-ai-ethics-and-fairness", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install jeremylongshore/tons-of-skills-marketplace validating-ai-ethics-and-fairnessInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/.curated/validating-ai-ethics-and-fairness .github/skills/validating-ai-ethics-and-fairness && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "validating-ai-ethics-and-fairness" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/validating-ai-ethics-and-fairness into .github/skills/validating-ai-ethics-and-fairness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validating-ai-ethics-and-fairness", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install jeremylongshore/tons-of-skills-marketplace validating-ai-ethics-and-fairness --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/jeremylongshore/tons-of-skills-marketplace.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/.curated/validating-ai-ethics-and-fairness .opencode/skills/validating-ai-ethics-and-fairness && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "validating-ai-ethics-and-fairness" agent skill from https://github.com/jeremylongshore/tons-of-skills-marketplace/tree/main/skills/.curated/validating-ai-ethics-and-fairness into .opencode/skills/validating-ai-ethics-and-fairness/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "validating-ai-ethics-and-fairness", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
validating-ai-ethics-and-fairness- GitHub stars
- 2.8k
- Token cost
- ~1.5k tokens
- SKILL.md length
- 662 words
- Files
- 10 (incl. scripts, references, assets)
- Skills in repo
- 3,342
- Repo updated
- First seen
- Licence
- MIT
At a glance
Validate AI/ML models and datasets for bias, fairness, and ethical concerns.
- Works in 10 steps: Load the model predictions and ground… → Define the protected attributes and… → Compute representation statistics: group… → …
- Auditing AI systems for ethical compliance
- SKILL.md covers Overview, Prerequisites, Instructions and Output, plus 3 more sections
- Runs Python scripts from its folder; calls pip
What it does
Validating AI Ethics And Fairness is an agent skill from jeremylongshore/tons-of-skills-marketplace. Validate AI/ML models and datasets for bias, fairness, and ethical concerns. Use when auditing AI systems for ethical compliance, fairness assessment, or bias detection. Trigger with phrases like "evaluate model fairness", "check for bias", or "validate AI ethics".
Its SKILL.md is about 1.5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 12 other files, including scripts, reference files and assets (for example `assets/README.md`, `assets/report_template.md` and `references/README.md`). Compatibility notes: Designed for Claude Code
It sits in Legal & Compliance, covering AI governance and Machine learning. The repository describes itself as: Model-agnostic agent-skills platform with a harness-free canonical layer, verified adapters, and the ccpi package manager. Explore at tonsofskills.com. The licence is MIT.
When your agent uses it
- Auditing AI systems for ethical compliance
- Fairness assessment
- With phrases like evaluate model fairness
- Validate AI ethics
Example prompts
- “evaluate model fairness”
- “check for bias”
- “validate AI ethics”
- “/validating-ai-ethics-and-fairness”
Requirements
- Python 3
- Compatibility (from SKILL.md): Designed for Claude Code
- Pre-approved tools (allowed-tools): Read, Write, Edit, Grep, Glob, Bash(python:*)
Workflow steps
10 steps, taken from the first numbered list in SKILL.md.
- Load the model predictions and ground truth dataset using the Read tool; verify schema includes sensitive attribute columns
- Define the protected attributes and privileged/unprivileged group definitions for the fairness analysis
- Compute representation statistics: group counts, class label distributions, and feature coverage per demographic segment
- Calculate core fairness metrics using Fairlearn or AIF360
- Apply four-fifths rule: flag any metric where the ratio falls below 0.80 as potential adverse impact
- Classify each finding by severity: low (ratio 0.90-1.0), medium (0.80-0.90), high (0.70-0.80), critical (below 0.70)
- Identify proxy variables by computing correlation between non-protected features and sensitive attributes
- Generate mitigation recommendations: resampling, reweighting, threshold adjustment, or in-processing constraints (e.g…
- Produce a compliance assessment mapping findings to IEEE Ethically Aligned Design, EU Ethics Guidelines for Trustworthy AI, and ACM Code…
- Document all ethical decisions, trade-offs, and residual risks in a structured audit report
What it can do on your machine
Read from SKILL.md and the folder at commit cfae287. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves these tools, so the agent can use them without asking each time:
ReadWriteEditGrepGlobBash(python:*)
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 4 files in scripts/ (Python), which the agent can run.
Shell commands in SKILL.md call:
pip
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):
fairlearn.orgpair-code.github.ioethicsinaction.ieee.org
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.
Compatibility
Designed for Claude Code
From compatibility in the SKILL.md frontmatter.
Context cost
Validating AI Ethics And Fairness loads about 1.5k tokens when it runs, and up to ~1.5k if it reads all its reference files. Until then it costs about 75 tokens; SKILL.md has 662 words of instructions outside code blocks.
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
Auto-check passed
The automated check found no risky patterns in SKILL.md.
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); the scripts in this folder are not scanned.
SKILL.md
The full file from jeremylongshore/tons-of-skills-marketplace at commit cfae287, republished under its MIT licence (© jeremylongshore). 662 words, ~1,516 tokens.
Download SKILL.mdSave it as
.claude/skills/validating-ai-ethics-and-fairness/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.- name
- validating-ai-ethics-and-fairness
- description
- Validate AI/ML models and datasets for bias, fairness, and ethical concerns. Use when auditing AI systems for ethical compliance, fairness assessment, or bias detection. Trigger with phrases like "evaluate model fairness", "check for bias", or "validate AI ethics".
- allowed-tools
- Read, Write, Edit, Grep, Glob, Bash(python:*)
- compatibility
- Designed for Claude Code
- version
- 1.24.0
- author
- Jeremy Longshore <jeremy@intentsolutions.io>
- license
- MIT
- tags
- ai, compliance, audit
AI Ethics Validator
Overview
Validate AI/ML models and datasets for bias, fairness, and ethical compliance using quantitative fairness metrics and structured audit workflows.
Prerequisites
- Python 3.9+ with Fairlearn >= 0.9 (
pip install fairlearn) - IBM AI Fairness 360 toolkit (
pip install aif360) for comprehensive bias analysis - pandas, NumPy, and scikit-learn for data manipulation and model evaluation
- Model predictions (probabilities or binary labels) and corresponding ground truth labels
- Demographic attribute columns (age, gender, race, etc.) accessible under appropriate data governance
- Optional: Google What-If Tool for interactive fairness exploration on TensorFlow models
Instructions
- Load the model predictions and ground truth dataset using the Read tool; verify schema includes sensitive attribute columns
- Define the protected attributes and privileged/unprivileged group definitions for the fairness analysis
- Compute representation statistics: group counts, class label distributions, and feature coverage per demographic segment
- Calculate core fairness metrics using Fairlearn or AIF360:
- Demographic parity ratio (selection rate parity across groups)
- Equalized odds difference (TPR and FPR parity)
- Equal opportunity difference (TPR parity only)
- Predictive parity (precision parity across groups)
- Calibration scores per group (predicted probability vs observed outcome)
- Apply four-fifths rule: flag any metric where the ratio falls below 0.80 as potential adverse impact
- Classify each finding by severity: low (ratio 0.90-1.0), medium (0.80-0.90), high (0.70-0.80), critical (below 0.70)
- Identify proxy variables by computing correlation between non-protected features and sensitive attributes
- Generate mitigation recommendations: resampling, reweighting, threshold adjustment, or in-processing constraints (e.g.,
ExponentiatedGradientfrom Fairlearn) - Produce a compliance assessment mapping findings to IEEE Ethically Aligned Design, EU Ethics Guidelines for Trustworthy AI, and ACM Code of Ethics
- Document all ethical decisions, trade-offs, and residual risks in a structured audit report
Output
- Fairness metric dashboard: per-group values for demographic parity, equalized odds, equal opportunity, predictive parity, and calibration
- Severity-classified findings table: metric name, affected groups, ratio value, severity level, recommended action
- Representation analysis: group sizes, class distributions, feature coverage gaps
- Proxy variable report: features correlated with protected attributes above threshold (r > 0.3)
- Mitigation plan: ranked strategies with expected fairness improvement and accuracy trade-off estimates
- Compliance matrix: pass/fail against IEEE, EU, and ACM ethical guidelines with evidence citations
Show full SKILL.md (307 more words)Show less
Error Handling
| Error | Cause | Solution |
|---|---|---|
| Insufficient group sample size | Fewer than 30 observations in a demographic group | Aggregate related subgroups; use bootstrap confidence intervals; flag metric as unreliable |
| Missing sensitive attributes | Protected attribute columns absent from dataset | Apply proxy detection via correlated features; request attribute access under data governance approval |
| Conflicting fairness criteria | Demographic parity and equalized odds contradict | Document the impossibility theorem trade-off; prioritize the metric most aligned with the deployment context |
| Data quality failures | Inconsistent encoding or null values in attribute columns | Standardize categorical encodings; impute or exclude nulls; validate with schema checks before analysis |
| Model output format mismatch | Predictions not in expected probability or binary format | Convert logits to probabilities via sigmoid; binarize at the decision threshold before metric computation |
Examples
Scenario 1: Hiring Model Audit -- Validate a resume-screening classifier for gender and age bias. Compute demographic parity across male/female groups and age buckets (18-30, 31-50, 51+). Apply the four-fifths rule. Finding: female selection rate at 0.72 of male rate (critical severity). Recommend reweighting training samples and adjusting the decision threshold.
Scenario 2: Credit Scoring Fairness -- Assess a credit approval model for racial disparate impact. Calculate equalized odds (TPR and FPR) across racial groups. Finding: FPR for Group A is 2.1x Group B (high severity). Recommend in-processing constraint using ExponentiatedGradient with FalsePositiveRateParity.
Scenario 3: Healthcare Risk Prediction -- Evaluate a patient risk model for age and socioeconomic bias. Compute calibration curves per group. Finding: model overestimates risk for low-income patients by 15%. Recommend recalibration using Platt scaling per subgroup with post-deployment monitoring for fairness drift.
Resources
- Fairlearn Documentation -- bias detection, mitigation algorithms, MetricFrame API
- AI Fairness 360 (AIF360) -- comprehensive fairness toolkit with 70+ metrics
- Google What-If Tool -- interactive fairness exploration
- EU Ethics Guidelines for Trustworthy AI -- regulatory framework
- IEEE Ethically Aligned Design -- technical ethics standards
- Impossibility theorem reference: Chouldechova (2017) on incompatibility of fairness criteria
© jeremylongshore, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 9 other files (scripts, references, assets) in skills/.curated/validating-ai-ethics-and-fairness of jeremylongshore/tons-of-skills-marketplace.
- SKILL.md
- assets/README.md
- assets/example_dataset.csv
- assets/example_model.pkl
- assets/report_template.md
- references/README.md
- scripts/README.md
- scripts/generate_report.py
- scripts/validate_dataset.py
- scripts/validate_model.py
Open the folder on GitHubat commit cfae287
Compare with similar skills
Validating AI Ethics And Fairness next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Validating AI Ethics And Fairness this skilljeremylongshore/tons-of-skills-marketplace | 2.8k | — | ~1.5k | Automated safety check: Pass | MIT | |
| Iso42001Sushegaad/Claude-Skills-Governance-Risk-and-Compliance | 946 | 1 repos | ~3.7k | Automated safety check: Pass | MIT | |
| AI Risk Managementbriiirussell/cybersecurity-skills | 413 | — | ~3.7k | Automated safety check: Notes | MIT | |
| EU AI Act System Inventoryanthropics/claude-for-legal | 9.6k | 3 repos | ~2.8k | Automated safety check: Pass | Apache-2.0 | |
| Eu AI Act Readinessseb1n/awesome-ai-agent-skills | 206 | — | ~3.3k | Automated safety check: Pass | MIT | |
| AI GovernanceHack23/cia | 239 | — | ~1.4k | Automated safety check: Pass | Apache-2.0 |
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Questions about Validating AI Ethics And Fairness
What does Validating AI Ethics And Fairness do?
Validate AI/ML models and datasets for bias, fairness, and ethical concerns. Validating AI Ethics And Fairness is an agent skill from jeremylongshore/tons-of-skills-marketplace. Validate AI/ML models and datasets for bias, fairness, and ethical concerns.
When should I use Validating AI Ethics And Fairness?
Validating AI Ethics And Fairness fits situations like: auditing AI systems for ethical compliance; fairness assessment; with phrases like evaluate model fairness; validate AI ethics.
How do I install Validating AI Ethics And Fairness in Claude Code?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a claude-code`. Or copy the skill folder (skills/.curated/validating-ai-ethics-and-fairness in jeremylongshore/tons-of-skills-marketplace) into .claude/skills/validating-ai-ethics-and-fairness in your project. Claude Code loads it when a task matches its description.
How do I install Validating AI Ethics And Fairness in Codex?
Run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a codex`. Or copy the skill folder (skills/.curated/validating-ai-ethics-and-fairness in jeremylongshore/tons-of-skills-marketplace) into .agents/skills/validating-ai-ethics-and-fairness in your project. Codex loads it when a task matches its description.
Can I use Validating AI Ethics And Fairness in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add jeremylongshore/tons-of-skills-marketplace --skill validating-ai-ethics-and-fairness -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/validating-ai-ethics-and-fairness, .gemini/skills/validating-ai-ethics-and-fairness, .github/skills/validating-ai-ethics-and-fairness and .opencode/skills/validating-ai-ethics-and-fairness in your project.
What does Validating AI Ethics And Fairness need to run?
Going by SKILL.md and its folder, Validating AI Ethics And Fairness needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3. Its frontmatter pre-approves these tools: Read, Write, Edit, Grep, Glob, Bash(python:*). Compatibility (from SKILL.md): Designed for Claude Code.
Does Validating AI Ethics And Fairness access the network?
SKILL.md names 3 domains. As links in the text: fairlearn.org, pair-code.github.io and ethicsinaction.ieee.org. This is read from the text; nothing was executed.
Is Validating AI Ethics And Fairness safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Validating AI Ethics And Fairness use?
Validating AI Ethics And Fairness is published under the MIT licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Validating AI Ethics And Fairness use?
About 1.5k tokens (SKILL.md is roughly 6.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 16 tokens, read only when the agent opens those files.
What are the alternatives to Validating AI Ethics And Fairness?
Skills that share tags, products or a category with Validating AI Ethics And Fairness: Iso42001 (Sushegaad/Claude-Skills-Governance-Risk-and-Compliance, 946 stars), AI Risk Management (briiirussell/cybersecurity-skills, 413 stars), EU AI Act System Inventory (anthropics/claude-for-legal, 9.6k stars) and Eu AI Act Readiness (seb1n/awesome-ai-agent-skills, 206 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Validating AI Ethics And Fairness?
jeremylongshore (a GitHub user) maintains it in jeremylongshore/tons-of-skills-marketplace, which has 2,827 GitHub stars. The repository holds 3,342 skills in this directory. The repository was last updated on October 10, 2026.
Source: jeremylongshore/tons-of-skills-marketplace on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.