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 skills add hollandkevint/data-product-operator --skill ethical-risk-assessmentgit clone --depth 1 https://github.com/hollandkevint/data-product-operatorWrote 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/skills/hollandkevint/data-product-operator/ethical-risk-assessment)<a href="https://agentmods.dev/skills/hollandkevint/data-product-operator/ethical-risk-assessment"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/ethical-risk-assessment/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/skills/hollandkevint/data-product-operator/ethical-risk-assessment"><img src="https://agentmods.dev/badge/skills/hollandkevint/data-product-operator/ethical-risk-assessment.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 | $0.00083 | $0.00939 |
| Opus 5 | $0.00042 | $0.00469 |
| Sonnet 5 | $0.00017 | $0.00188 |
| Haiku 4.5 | $0.00008 | $0.00094 |
Grade A, and why
ethical-risk-assessment 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 11d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
The Fifth Risk
Standard product risk frameworks cover four risks: value, usability, feasibility, and business viability (see data-product-thinking). Data products add a fifth: ethical data risk.
Ethical data risk asks: Can we build this without bias, privacy violations, or unintended harm?
This risk is owned by the Data Lead, who has veto authority. A technically correct model that produces biased outcomes is worse than no model. This is not optional and not delegated to a compliance review.
Ethics Canvas
Before committing to any ML/AI feature, complete this structured evaluation:
- Training data: What are the sources? What biases exist in the collection method? Which populations are overrepresented or underrepresented?
- Features used: Which input variables could proxy for protected classes? (ZIP code proxies for race; insurance type proxies for income)
- Accuracy by group: Does model performance vary across demographic groups? Test across race, gender, age, and geography at minimum.
- Known limitations: What does the model NOT do well? Document before shipping, not after complaints.
- Transparency requirements: Can you explain to a user how the model reached its conclusion? If not, the model should not make autonomous decisions.
CRITICAL: Complete the ethics canvas before writing a line of model code. Discovering bias after deployment is 10x more expensive than preventing it.
Bias Testing Protocol
Run bias audits quarterly or before any model update:
- Define protected classes for your domain (race, gender, age, geography, insurance status, language)
- Split test data by each protected class
- Compare model accuracy, false positive rate, and false negative rate across groups
- Flag any disparity greater than a defined threshold (e.g., 5% accuracy difference)
- If flagged, investigate root cause and apply calibration before production
Real example: A propensity scoring model showed 8% accuracy disparity for Black patients. Root cause was underrepresentation in training data. Calibration layer added before production deployment.
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.
- 11d ago First seen · 77 lines · 83 tokens per session scan A 3c80774882c8
ethical-risk-assessment is a skill published in the GitHub repository hollandkevint/data-product-operator (3 stars, last pushed yesterday), licensed MIT. It adds 83 tokens to every session and 939 once invoked, about $0.0004 per session on Opus 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 2026-08-31.
Other skills, from other repositories
feifei-skills
Invoke Fei-Fei Li — Human-Centered AI & Ethics. Use for responsible AI reviews, ethical impact assessments, human oversight design, and fairness analysis. Sets Claude into the Fei-Fei Li persona for the current conversation.
plagiarism-checker
Scans lyrics for phrases that may match existing songs using web search and LLM knowledge. Use before release to check for unintentional borrowing.
ai-ml-governance
Governs models and AI systems in production — intended use, evaluation, monitoring, human oversight, documentation, and the decision to deploy or retire. Use this before deploying a model or AI feature, when defining evaluation criteria, when a model's behavior has drifted, when assessing AI risk or regulatory…
employment-compliance
Covers the employment rules that carry real penalties — exempt and non-exempt classification, overtime and hours, employee versus contractor status, work authorization and recordkeeping, accommodation requests, and the notices and retention obligations that go with them. Use this to classify a role, review a…
tax-ebookkeeping-context
Background information about Japan’s Electronic Bookkeeping Act, which sets rules for storing accounting records, scanned documents, and data received through electronic transactions.
prompt-engineer
Use when the user asks to improve, optimize, rewrite, debug, or shorten a prompt, or asks why a prompt is producing bad output. Do NOT use for writing a Claude Code SKILL.md — that needs skill structure rules, not prompt techniques.