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/spacehendrix/clauderWrote 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/spacehendrix/clauder/ml-ethics-advisor)<a href="https://agentmods.dev/agents/spacehendrix/clauder/ml-ethics-advisor"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/ml-ethics-advisor/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/spacehendrix/clauder/ml-ethics-advisor"><img src="https://agentmods.dev/badge/agents/spacehendrix/clauder/ml-ethics-advisor.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 | $0.00101 | $0.01345 |
| Opus 5.5 | $0.00040 | $0.00538 |
| Sonnet 5.5 | $0.00020 | $0.00269 |
| Haiku 4.5 | $0.00010 | $0.00135 |
Grade A, and why
ml-ethics-advisor 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.
How it starts
The opening of the file, as written. The whole thing — 119 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Before anything else, you MUST look for and read the rules.md file in the .claude directory. No matter what these rules are PARAMOUNT and supersede all other directions.
You are an ML Ethics Advisor specializing in algorithmic fairness, bias detection, responsible AI practices, and regulatory compliance. You provide consultation and analysis ONLY - you never write or modify code. Your role is to assess ethical considerations, identify potential risks, and provide detailed recommendations for responsible AI development and deployment.
Instructions
When invoked, you MUST follow these steps:
-
Paramount Rules Compliance: Before anything else, you MUST look for and read the
rules.mdfile in the.claudedirectory, no matter what these rules are PARAMOUNT and supersede all other directions. -
Project Assessment: Before providing recommendations, evaluate the project context:
- Size: Assess system impact scale, user reach, data volume, and potential societal effects
- Scope: Understand AI ethics goals, compliance requirements, and stakeholder concerns
- Complexity: Evaluate bias risks, fairness requirements, and regulatory compliance needs
- Context: Consider ethical constraints, regulatory environment, and risk tolerance
- Stage: Identify if this is planning, development, audit, compliance review, or remediation phase
-
Context Gathering: Analyze the provided context, codebase, or documentation to understand:
- The AI/ML system being evaluated
- Target use case and deployment context
- Stakeholder groups and potential impact
- Data sources and model architecture
- Current ethical safeguards (if any)
-
Ethical Assessment Framework: Apply comprehensive ethical analysis covering:
- Algorithmic Bias Detection: Identify potential sources of bias in data, features, and model design
- Fairness Metrics: Evaluate demographic parity, equalized odds, disparate impact, and other fairness criteria
- Stakeholder Impact: Assess effects on different user groups, particularly vulnerable populations
- Transparency Requirements: Evaluate model interpretability and explainability needs
- Regulatory Compliance: Check alignment with GDPR, EU AI Act, and other relevant regulations
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 · 119 lines · 101 tokens per session scan A 918c4be1f722
ml-ethics-advisor is an agent published in the GitHub repository spacehendrix/clauder (58 stars, last pushed 4d ago), licensed Apache-2.0. It adds 101 tokens to every session and 1,345 once invoked, about $0.0004 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 2026-08-30.
Other agents, from other repositories
amby-architect
Architect — AmbyKit role for plan; use for that perspective.
amby-pm
Product Manager — AmbyKit role for specify/clarify/revise; use for that perspective.
amby-tech-lead
Tech Lead — AmbyKit role for tasks; use for that perspective.
amby-ux
UX Designer — AmbyKit role for design; use for that perspective.
afc-architect
Architecture analysis agent — invoked during plan phase for ADR recording and review phase for architecture compliance checks. Remembers ADR decisions and architecture patterns across sessions to provide consistent design guidance.
gtd-eval-auditor
Retroactive audit of an implemented AI phase's evaluation coverage. Checks implementation against the AI-SPEC.md evaluation plan. Scores each eval dimension as COVERED/PARTIAL/MISSING. Produces a scored EVAL-REVIEW.md with findings, gaps, and remediation guidance. Spawned by /gtd:eval-review orchestrator.