Borrowing it
Nothing to install: this file belongs to timothywarner-org/ab100. 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/ab100/main/.github/skills/ab100-study-planner/SKILL.mdgit clone --depth 1 https://github.com/timothywarner-org/ab100Wrote 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/ab100/ab100-study-planner)<a href="https://agentmods.dev/skills/timothywarner-org/ab100/ab100-study-planner"><img src="https://agentmods.dev/badge/skills/timothywarner-org/ab100/ab100-study-planner.svg" alt="Measured on agentmods" 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 | $0.00066 | $0.01388 |
| Opus 5 | $0.00033 | $0.00694 |
| Sonnet 5 | $0.00013 | $0.00278 |
| Haiku 4.5 | $0.00007 | $0.00139 |
Grade A, and why
ab100-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 7d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: ab100.study_planner.personalized
Description: Generates a personalized AB-100 study plan based on the user's self-assessed confidence across exam domains, prioritizing weak areas with estimated hours and Microsoft Learn module links.
Grounding
Required sources:
docs/ab100-exam-objectives.md(canonical AB-100 skills measured, synced verbatim from the Microsoft Learn study guide)- Microsoft Learn (access via the Microsoft Learn MCP server using
microsoft_docs_searchfor current Learn module URLs) - Use
microsoft_docs_fetchto verify Learn module links are current and active
Workflow
-
Present domains with weights. Show the three AB-100 domains and their exam weight percentages:
Domain Exam Weight Plan AI-powered business solutions 25-30% Design AI-powered business solutions 25-30% Deploy AI-powered business solutions 40-45% -
Offer optional finer-grained confidence ratings. The Deploy domain carries the most weight and has four subdomains. If the user wants a more targeted plan, offer to rate each subdomain separately:
Plan subdomains:
- Analyze requirements for AI-powered business solutions
- Design overall AI strategy for business solutions
- Evaluate costs and benefits (ROI)
Design subdomains:
- Design AI and agents for business solutions
- Design extensibility of AI solutions
- Orchestrate configuration for prebuilt agents and apps
Deploy subdomains:
- Analyze, monitor, and tune AI-powered business solutions
- Manage the testing of AI-powered business solutions
- Design the ALM process for AI-powered business solutions
- Design responsible AI, security, governance, risk management, and compliance
-
Ask for confidence ratings. Ask the user to rate their confidence in each area using one of these levels:
- Strong -- comfortable with most objectives; needs only light review.
- Moderate -- familiar with the concepts but needs targeted practice.
- Weak -- limited experience; needs focused study.
- Unknown -- not sure; treat as weak.
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.
- 7d ago First seen · 115 lines · 66 tokens per session scan A 7004dc08c820
ab100-study-planner is a skill published in the GitHub repository timothywarner-org/ab100 (37 stars, last pushed 1mo ago), licensed MIT. It adds 66 tokens to every session and 1,388 once invoked, about $0.0003 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 2026-08-30.
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