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/ab900Wrote 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/ab900/ab900-study-planner)<a href="https://agentmods.dev/commands/timothywarner-org/ab900/ab900-study-planner"><img src="https://agentmods.dev/badge/commands/timothywarner-org/ab900/ab900-study-planner/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/ab900/ab900-study-planner"><img src="https://agentmods.dev/badge/commands/timothywarner-org/ab900/ab900-study-planner.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 | $0.00025 | $0.00688 |
| Opus 5.5 | $0.00010 | $0.00275 |
| Sonnet 5.5 | $0.00005 | $0.00138 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
ab900-study-planner 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.
How it starts
The opening of the file, as written. The whole thing — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AB-900 Study Planner
Generate a personalized AB-900 study plan based on your confidence across the three exam domains.
Use this skill
You must follow the workspace skill ab900-study-planner for workflow, domain-specific guidance, output format, and delivery rules (full plan in a single message).
Inputs (from chat)
- Domain 1 confidence: ${input:domain1:Strong | Moderate | Weak | Unknown}
- Domain 2 confidence: ${input:domain2:Strong | Moderate | Weak | Unknown}
- Domain 3 confidence: ${input:domain3:Strong | Moderate | Weak | Unknown}
Grounding and validation rules
- Ground all Microsoft Learn module links in Microsoft Learn using the Microsoft Learn MCP server and Copilot web search. Do not invent Learn URLs.
- Use the AB-900 exam skills outline for objective mapping. The current version is Skills measured as of July 22, 2026; use its domain titles and objective wording verbatim.
- Prioritize weak domains first. Within equal confidence levels, prioritize by exam weight (Domain 2 first at 35-40%).
Terminology
Always use current Microsoft product names. Apply all mappings from the agent terminology table silently. Never use:
- "Azure AD", "AAD", or "Azure Active Directory" (use "Microsoft Entra ID")
- "compliance.microsoft.com" (use "purview.microsoft.com")
- "AI hub" (use "DSPM for AI")
- "Azure AI Studio" (use "Microsoft Foundry")
- "Microsoft 365 Defender" (use "Microsoft Defender XDR")
- "Exchange Online admin center" (use "Exchange admin center")
- "Content explorer" where the objective means "Data explorer"
- "Requested agents tab" (use "Requests")
- "Billing > Billing policies" for Copilot (use "Copilot > Billing & usage")
- "restricted site access" in the SharePoint Advanced Management objective (use "restricted access control")
The unit of consumption is the Copilot Credit at $0.01 per credit.
Output format
Use the Markdown output format defined in the ab900-study-planner skill exactly. The output must include: domain priority order, confidence rating, estimated study hours, focus objectives, high-priority topics, Microsoft Learn module links, total hours, and an offer to start practicing.
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 · 58 lines · 25 tokens per session scan A 9af486ecef10
ab900-study-planner is a command published in the GitHub repository timothywarner-org/ab900 (29 stars, last pushed yesterday), licensed MIT. It adds 25 tokens to every session and 688 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 2026-10-20.
Other commands, from other repositories
graph
Extract prerequisite concept graph from converted course materials and write course-index/concept-graph.md.
alt
Import an Exam Radar (OPTIMETA Alt plugin) export and fold its lecture-emphasis exam signal into the course index — radar.md, a lecture-emphasis column on coverage.md, and a gold-zone weakmap.
quiz
Generate N practice problems on a topic. Saves problem MD + hidden answer MD. User solves on paper, uploads answer PDF, then runs /grade. First arg weakmap → auto-load latest weakmap report and target its top weaknesses.
weakmap
Priority-ranked weakness report. No arg → fresh report from latest errors per pattern. With concept arg → patch latest report by adding the user-declared weakness, save as new timestamped file.
derive
Save a clean reference derivation of a target equation or theorem to derivations/. Draws from course materials (textbook, lecture notes) rather than testing the user.
mock
Generate a mock exam matched to the course's structure (inferred from course-index). Saves problem MD + hidden solution MD. User solves on paper, uploads PDF, runs /grade.