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.
npx agentmods add skills/scibly-dev/skills/assessment-designnpx skills add scibly-dev/skills --skill assessment-designgit clone --depth 1 https://github.com/scibly-dev/skillsWrote 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/scibly-dev/skills/assessment-design)<a href="https://agentmods.dev/skills/scibly-dev/skills/assessment-design"><img src="https://agentmods.dev/badge/skills/scibly-dev/skills/assessment-design.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 | $0.00112 | $0.01089 |
| Opus 5 | $0.00056 | $0.00544 |
| Sonnet 5 | $0.00022 | $0.00218 |
| Haiku 4.5 | $0.00011 | $0.00109 |
Grade A, and why
assessment-design 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 6d 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 — 91 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Assessment Design with AI
Most AI-generated questions are too easy — they test recognition, not learning. Worse, a common AI failure mode is testing the wrong thing entirely: quizzing an exact percentage, date, or phrase pulled from the source text instead of whether the learner can actually use the underlying skill. A learner can memorize "73%" and still fail to apply the concept it came from; that's not transfer, it's trivia. This skill shows you how to use Bloom's taxonomy as a prompt lever to get application-level questions with quality distractors and feedback that actually teaches.
Step 1 — Understand the assessment context
Ask the user:
- What's the topic? Be specific — not "leadership", but "giving constructive feedback to a peer who missed a deadline".
- What Bloom's level? If unsure:
- Remember / Understand: learners recall or explain concepts → compliance or foundational knowledge
- Apply: learners use knowledge in a new situation → skills and procedures
- Analyze / Evaluate: learners break down situations or make judgments → complex decision-making
- Question format? MCQ, scenario-based (situation + question), true/false with explanation, short answer, matching, drag-to-sequence.
- How many questions? And for what purpose — formative check, end-of-course test, certification?
- What does the learner already know? Helps calibrate how tricky the distractors should be.
Step 2 — Build the assessment generation prompt
Construct this prompt:
ASSESSMENT GENERATION PROMPT
Role: You are an expert assessment designer and instructional design specialist.
Topic: [specific topic]
Target Bloom's level: [Remember / Understand / Apply / Analyze / Evaluate]
Learner: [role + experience level]
Question format: [MCQ / scenario-based / true-false / matching]
Number of questions: [N]
For each question:
- Write a stem that presents a realistic situation or judgment call — avoid "which of the following" where possible.
- [If MCQ] Write 4 answer options: 1 correct, 2 plausible distractors reflecting common mistakes, 1 tempting shortcut with a hidden flaw.
- [If scenario-based] Start with a 2-sentence situation before the question.
- Write feedback for each option: why it's correct or why it's a common mistake (2 sentences max per option).
- Tag each question with its Bloom's level.
Avoid: trivially obvious wrong answers, questions answerable by scanning the course text without thinking, trick questions, double negatives.
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.
- 6d ago First seen · 91 lines · 112 tokens per session scan A 63cdaa73f765
assessment-design is a skill published in the GitHub repository scibly-dev/skills (5 stars, last pushed 1mo ago), licensed MIT. It adds 112 tokens to every session and 1,089 once invoked, about $0.0006 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.
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