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/timothywarner-org/ai901-cert-buddy-claudeWrote 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/ai901-cert-buddy-claude/explain)<a href="https://agentmods.dev/commands/timothywarner-org/ai901-cert-buddy-claude/explain"><img src="https://agentmods.dev/badge/commands/timothywarner-org/ai901-cert-buddy-claude/explain/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/ai901-cert-buddy-claude/explain"><img src="https://agentmods.dev/badge/commands/timothywarner-org/ai901-cert-buddy-claude/explain.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 | $0.00204 |
| Opus 5 | $0.00010 | $0.00102 |
| Sonnet 5 | $0.00004 | $0.00041 |
| Haiku 4.5 | $0.00002 | $0.00020 |
Grade A, and why
explain 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 9d 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.
What it actually says
Use the ai901-cert-buddy subagent to explain this AI-901 concept: $ARGUMENTS
Approach:
- Ground the explanation in Microsoft Learn first via the
ai901buddy-mslearnMCP server. - Lead with a plain-language mental model a beginner gets on the first pass. Define each term the first time it appears.
- Layer the exam-relevant detail on top of the simple version. Tie it to the AI-901 objective it belongs to.
- End with two or three Microsoft Learn URLs to go deeper, and one likely exam angle on the concept.
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.
- 9d ago First seen · 14 lines · 20 tokens per session scan A d8b5879216e0
explain is a command published in the GitHub repository timothywarner-org/ai901-cert-buddy-claude (9 stars, last pushed 2mo ago), licensed MIT. It adds 20 tokens to every session and 204 once invoked, about $0.0001 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 commands, from other repositories
learn
Learn new capabilities through experimentation, then codify into the system.
recap
Get a summary of everything you learned this session — concepts, quizzes, and progress.
explain
Explain the subject in depth using the fixed four-part structure: What, Why, Example, Consequences. Treat the subject as the thing to be explained, not as a literal question.
interview
Prepare for the interview from the filed application — predicted questions, STAR answers from real evidence, and gap-defence.
learn
Capture a reusable learning from this session — with a dedup + Save/Absorb/Drop gate so the knowledge base stays clean.
explain
Generate an interactive one-module explainer for a topic — Riko gathers code scope, Senku plans a 3-5 screen teaching arc, Speedwagon authors the HTML, assembler produces explain-out/index.html.