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/plan)<a href="https://agentmods.dev/commands/timothywarner-org/ai901-cert-buddy-claude/plan"><img src="https://agentmods.dev/badge/commands/timothywarner-org/ai901-cert-buddy-claude/plan/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/plan"><img src="https://agentmods.dev/badge/commands/timothywarner-org/ai901-cert-buddy-claude/plan.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 | $0.00018 | $0.00309 |
| Opus 5 | $0.00009 | $0.00154 |
| Sonnet 5 | $0.00004 | $0.00062 |
| Haiku 4.5 | $0.00002 | $0.00031 |
Grade A, and why
plan 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 10d 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 and the ai901-study-planner skill to build a personalized AI-901 study plan.
Starting input (optional): $ARGUMENTS
If no ratings are given, show the pick-list
Present the two AI-901 domains with their exam weights, and ask the learner to rate each on a simple scale, explained in plain language:
- Domain 1 -- Identify AI concepts and capabilities (40-45%)
- Domain 2 -- Implement AI solutions by using Microsoft Foundry (55-60%)
Confidence levels:
strong-- comfortable, needs light review onlymoderate-- familiar, needs targeted practiceweak-- limited experience, needs focused studyunknown-- not sure, treat as weak
Then plan
- Order study from weakest to strongest; within equal confidence, prioritize the higher-weight domain.
- For each area give estimated hours and current Microsoft Learn module URLs (grounded through the MCP server, never invented).
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.
- 10d ago First seen · 29 lines · 18 tokens per session scan A 22932d9037b6
plan is a command published in the GitHub repository timothywarner-org/ai901-cert-buddy-claude (9 stars, last pushed 2mo ago), licensed MIT. It adds 18 tokens to every session and 309 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
audit-quiz-coverage
Find quiz coverage gaps from recent guide/CHANGELOG/CC-releases changes and propose new questions.
learn
Learn new capabilities through experimentation, then codify into the system.
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.
recap
Get a summary of everything you learned this session — concepts, quizzes, and progress.
learn
Capture a reusable learning from this session — with a dedup + Save/Absorb/Drop gate so the knowledge base stays clean.
interview
Prepare for the interview from the filed application — predicted questions, STAR answers from real evidence, and gap-defence.