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/skills/gh300-lab-creator/SKILL.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/skills/timothywarner-org/copilot-cert-prep/gh300-lab-creator)<a href="https://agentmods.dev/skills/timothywarner-org/copilot-cert-prep/gh300-lab-creator"><img src="https://agentmods.dev/badge/skills/timothywarner-org/copilot-cert-prep/gh300-lab-creator/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/timothywarner-org/copilot-cert-prep/gh300-lab-creator"><img src="https://agentmods.dev/badge/skills/timothywarner-org/copilot-cert-prep/gh300-lab-creator.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 | $0.00063 | $0.00663 |
| Opus 5.5 | $0.00025 | $0.00265 |
| Sonnet 5.5 | $0.00013 | $0.00133 |
| Haiku 4.5 | $0.00006 | $0.00066 |
Grade A, and why
gh300-lab-creator 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 — 90 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: gh300.practice_exercises.micro.validated
Description: Create short GH-300 practice exercises (10-20 minutes) that are executable and self-validating.
Grounding
Required sources:
- Microsoft Learn (primary truth source for feature and policy behavior; access via the Microsoft Learn MCP server)
- Microsoft Learn code samples for syntax when applicable
Guardrails
- Keep the exercise within GH-300 scope.
- Prefer low-risk actions that can run in a local repo or demo org.
- No contractions.
- Always include rollback or cleanup steps.
- Always use current terminology.
Bundled assets
Use the skill-local bundle to keep outputs consistent and auditable.
resources/source-pack.mdresources/microsoft-voice-principles.mdresources/lab-quality-checklist.mdscripts/validate-output.js
Fictional company randomization (non-negotiable)
Use fictional company names from references/fictional-companies.md for any scenario context. Randomize the company selection.
Timebox guidance
An exercise should contain no more than 12 steps total across all tasks. If content exceeds that, split into two exercises.
Workflow
- Choose a single GH-300 objective from
references/gh300-objectives.mdand state it at the top. - Ground the intended behavior in Microsoft Learn.
- Draft steps using one primary surface (VS Code, GitHub.com, or CLI).
- Verify command syntax if commands are included.
- Add validation gates after each major step.
- Add rollback that exactly reverses the changes.
- Run the checks in
resources/lab-quality-checklist.mdand applyscripts/validate-output.jsrules before final delivery.
Output format
exercise:
title: "<Action + focus area>"
objective: "<One sentence outcome tied to GH-300>"
skill_area: "<GH-300 skill area>"
estimated_time: "<10-20 min>"
prerequisites:
- "<Workspace and account requirements>"
starting_state:
- "<What must already exist>"
tasks:
- name: "<Task 1 name>"
steps: |
<Numbered steps only when sequencing matters.>
validation:
- "<Validation action + expected result>"
- name: "<Task 2 name>"
steps: |
<...>
validation:
- "<...>"
troubleshooting:
- symptom: "<common failure>"
fix: "<precise fix>"
cleanup:
steps: |
<Exact rollback steps>
validation:
- "<Check that rollback succeeded>"
references:
- "<Microsoft Learn URL(s)>"
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 90 lines · 63 tokens per session scan A e3ed7a69cd61
gh300-lab-creator is a skill published in the GitHub repository timothywarner-org/copilot-cert-prep (97 stars, last pushed 1mo ago), licensed MIT. It adds 63 tokens to every session and 663 once invoked, about $0.0003 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.
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