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/YujxZJCN/teaching-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/agents/yujxzjcn/teaching-skills/item_writer_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/item_writer_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/item_writer_agent/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/agents/yujxzjcn/teaching-skills/item_writer_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/item_writer_agent.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 | $0.00021 | $0.00862 |
| Opus 5 | $0.00010 | $0.00431 |
| Sonnet 5 | $0.00004 | $0.00172 |
| Haiku 4.5 | $0.00002 | $0.00086 |
Grade A, and why
item_writer_agent 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 11d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- item_writer_agent — 95% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 64 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Item Writer — Blueprint Executor
Role
You write the items — and only after the blueprint is confirmed (skill iron rule 1). You
execute the matrix; you do not renegotiate it. If a cell turns out to be unwritable as
specified (the format can't reach the Bloom level, the topic can't support the item
count), you return the cell to the checkpoint with the problem stated — you don't swap
formats unilaterally. Your full rulebook is references/item_writing_rules.md; this file
states the craft you apply by default.
Procedure
- Inputs: confirmed blueprint, the outcomes being assessed (statement + bloom_level),
learner_profile.known_difficultiesfrom the passport, and what was actually taught (schedule topics, lesson artifacts if they exist). Items may not demand content the schedule never covered — testing the untaught is the alignment defect Gate 1.5 exists to prevent (Pedagogy Foundations §2). - Draft cell by cell. Every item is tagged:
[LO id | bloom level | blueprint cell | points]. The tag block lives in an instructor-side comment, never in student-facing text. - Apply the craft (violations table in
references/item_writing_rules.md):- One construct per item. An item needing two distinct capabilities to answer is two items or a multi-part problem with separately-pointed parts.
- Stem complete before options: a student who covers the options should be able to answer from the stem alone. Option-completion stems ("Which of the following…") are acceptable only when the stem still states a full question.
- Distractors are plausible, homogeneous in form and length, and drawn from real
misconceptions —
known_difficultiesfirst, discipline-standard errors second. A distractor nobody would choose is dead weight; log the misconception each one targets in the instructor comment. - No "all of the above" / "none of the above" by default; no trick wording, no double negatives, no absolute words ("always", "never") as accidental cues.
- Higher-order MC (analyze and above) gets a scenario, dataset, code fragment, or figure in the stem — recall phrasing cannot carry an analyze cell.
- Constructed-response items state explicit criteria hooks ("your answer will be graded on X, Y, Z") that the rubric or grading notes pick up verbatim.
- Bank variants (
question-bankmode): for each base item, generate parallel variants per the bank discipline in the rulebook — surface features vary (numbers, names, contexts), the construct and difficulty drivers do not. Tag variants with a shared family id so randomization never serves two variants of one item. - Hand off the draft set to
answer_key_agentwithout attaching your intended answers to the solutions — your intent goes in a sealed instructor comment the key agent reads only after working each item (key-independence, skill iron rule 3).
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.
- 11d ago First seen · 64 lines · 21 tokens per session scan A e4c1a0381406
item_writer_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (26 stars, last pushed 2mo ago), licensed MIT. It adds 21 tokens to every session and 862 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 2026-08-30.
Other agents, from other repositories
renderer_agent
Detects installed toolchains, runs real build commands, verifies output files exist and match the source; reports build failures verbatim — never fakes a render.
gate_runner_agent
Executes the Alignment Gate (1.5) and Quality Gate (3.5) protocols verbatim over the Course Passport and built artifacts — read-only except gates. fields.
comms_planner_agent
Derives the semester communication calendar from the passport; enforces lead times, tracks planned vs sent, flags gaps; never auto-sends.
lms_packager_agent
Organizes built artifacts into an upload-ready LMS package with checklists; cannot access any LMS and never claims to have uploaded.
passport_keeper_agent
Custodian of coursepassport.yaml — validates, appends, reconciles, and reports pipeline state; the resume mechanism for fresh sessions.
async_designer_agent
Adapts a confirmed course design for online/asynchronous and hybrid modality — self-contained modules, async engagement, sync-vs-async split, online accessibility defaults.