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 skills add AlterLab-IEU/AlterLab-Academic-Skills --skill alterlab-syllabus-ai-policygit clone --depth 1 https://github.com/AlterLab-IEU/AlterLab-Academic-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/alterlab-ieu/alterlab-academic-skills/alterlab-syllabus-ai-policy)<a href="https://agentmods.dev/skills/alterlab-ieu/alterlab-academic-skills/alterlab-syllabus-ai-policy"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-syllabus-ai-policy/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/skills/alterlab-ieu/alterlab-academic-skills/alterlab-syllabus-ai-policy"><img src="https://agentmods.dev/badge/skills/alterlab-ieu/alterlab-academic-skills/alterlab-syllabus-ai-policy.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00222 | $0.02789 |
| Opus 5 | $0.00111 | $0.01394 |
| Sonnet 5 | $0.00044 | $0.00558 |
| Haiku 4.5 | $0.00022 | $0.00279 |
Grade A, and why
alterlab-syllabus-ai-policy 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Syllabus AI-Use Policy Drafter — Per-Course, Per-Assignment GenAI Statements
The focused add-on that turns "what's my AI policy?" into a concrete, paste-ready
syllabus statement. It does one thing well: given a course and its graded
tasks, it assigns each task an explicit permitted / restricted / prohibited
tier, writes the matching disclosure and attribution clause, and binds the whole
statement to the institution's own academic-integrity code — so the policy is
enforceable, not aspirational. It deliberately does not author the rest of
the syllabus, learning outcomes, or rubrics; that is alterlab-teaching-design.
When to Use This Skill
Use it when the request is about the AI-use rules of a course or assignment:
Draft an AI-use policy for my syllabus.
Write a course statement on ChatGPT / generative AI for students.
I need an academic-integrity clause that covers AI tools.
Give me per-assignment rules: where can students use AI, where not?
How should students disclose and cite AI they used in an essay?
Make my AI policy consistent with our university's integrity code.
→ Gather the course context (level, discipline, the list of graded tasks, the
institution's integrity-code reference), assign each task a tier, then run
scripts/policy_builder.py to emit the statement and scripts/policy_lint.py
to catch contradictions before you hand it back.
Does NOT Trigger
This skill is a narrow add-on. Route adjacent asks to the right sibling:
| The ask is really about… | Route to | Why not here |
|---|---|---|
| Designing the whole course / syllabus, learning outcomes, rubrics, lesson plans, backward design | alterlab-teaching-design |
Owns full course/backward design; this skill only writes the AI-policy section |
| Ethics of using an AI tool on human-subjects data (IRB, consent, de-identification) | alterlab-research-ethics |
Research-ethics / IRB territory, not a teaching policy |
| Whether a student submission was AI-generated; running a detector | alterlab-teaching-design (assessment) |
This skill writes policy; it does not adjudicate or detect individual cases |
| Verifying that citations a student or author produced actually exist | alterlab-citation-verifier |
Citation existence-checking, not policy drafting |
| Turkish-system integrity/ethics process (ÜAK, YÖK etik kurul) | alterlab-tr-research-ethics |
Turkey-specific ethics workflow, parameterized differently |
| Institutional accreditation / assurance-of-learning reporting | alterlab-accreditation-aol |
Program-level AoL, not a course AI clause |
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 214 lines · 222 tokens per session scan A 1e65f52bc35c
alterlab-syllabus-ai-policy is a skill published in the GitHub repository AlterLab-IEU/AlterLab-Academic-Skills (66 stars, last pushed 7d ago), licensed MIT. It adds 222 tokens to every session and 2,789 once invoked, about $0.0011 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 2026-09-05.
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