Borrowing it
Nothing to install: this file belongs to timothywarner-org/copilot-cert-prep. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/timothywarner-org/copilot-cert-prep/main/.github/prompts/gh300-rai-questions.prompt.mdgit clone --depth 1 https://github.com/timothywarner-org/copilot-cert-prepWrote 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/commands/timothywarner-org/copilot-cert-prep/gh300-rai-questions)<a href="https://agentmods.dev/commands/timothywarner-org/copilot-cert-prep/gh300-rai-questions"><img src="https://agentmods.dev/badge/commands/timothywarner-org/copilot-cert-prep/gh300-rai-questions/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/commands/timothywarner-org/copilot-cert-prep/gh300-rai-questions"><img src="https://agentmods.dev/badge/commands/timothywarner-org/copilot-cert-prep/gh300-rai-questions.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 | $0.00020 | $0.00553 |
| Opus 5.5 | $0.00008 | $0.00221 |
| Sonnet 5.5 | $0.00004 | $0.00111 |
| Haiku 4.5 | $0.00002 | $0.00055 |
Grade A, and why
gh300-rai-questions 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 — 52 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GH-300 Responsible AI Practice Question
Generate ONE original, exam-realistic GH-300 practice question on responsible AI principles.
Scope
This prompt targets the Use GitHub Copilot responsibly (15-20%) domain, specifically:
- Understand responsible AI principles (risks and limitations, ethical and responsible usage, harms and mitigation).
- Validate and operate AI tools (validating output, operating Copilot responsibly).
Use this skill
You must follow the workspace skill gh300-item-creator, and in particular its Recipe: responsible AI principle items section for grounding, the six principles, and the RAI-specific distractor traps. Apply the same delivery rules (Phase 1 / Phase 2 interactive flow).
Inputs (from chat)
- Principle: ${input:principle:fairness | reliability and safety | privacy and security | inclusiveness | transparency | accountability (or leave blank and the agent picks one)}
- Bloom: ${input:bloom:Remember | Understand | Apply | Analyze}
- Difficulty: ${input:difficulty:easy | medium | hard}
Grounding and validation rules
- Ground the correct behavior in the Responsible AI with GitHub Copilot Learn module via the Microsoft Learn MCP server (
microsoft_docs_fetchon the six-principles and mitigate-risks units). - Cite the matching Microsoft Learn unit URL in the Phase 2 References section.
- Do not attribute Azure AI Foundry or Copilot Studio responsible AI controls to GitHub Copilot, except deliberately as a distractor.
Key rules
- Randomize the correct answer position across A, B, C, D.
- Randomize the fictional company name from
references/fictional-companies.md. - Follow all style rules from
references/style-guide.md. - Use plausible, parallel distractors. Do not force an obvious unrelated-product choice.
- No contractions. No trick wording. No fake features.
Output contract
Follow the Phase 1 and Phase 2 Markdown formats in the gh300-item-creator skill. Use a single-line stem and A: through D: choices. Add principle to metadata when useful. Deliver the question first, then wait before revealing the answer, rationale, or primary-source references.
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 Changed · -31 lines 5ea0e278097e
- 1mo ago First seen · 83 lines · 20 tokens per session scan A 1df105e5f7d6
gh300-rai-questions is a command published in the GitHub repository timothywarner-org/copilot-cert-prep (97 stars, last pushed 1mo ago), licensed MIT. It adds 20 tokens to every session and 553 once invoked, about $0.0001 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 2026-09-08.
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