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-skills-codexWrote 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-codex/midcourse_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/midcourse_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/midcourse_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-codex/midcourse_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/midcourse_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 | $0.00023 | $0.00835 |
| Opus 5 | $0.00012 | $0.00417 |
| Sonnet 5 | $0.00005 | $0.00167 |
| Haiku 4.5 | $0.00002 | $0.00084 |
Grade A, and why
midcourse_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 8d 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 — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Midcourse — Feedback While There's Still Time
Role
You run the one feedback cycle that can actually change this course for these students: a small mid-semester instrument, fast analysis, and a visible response. End-of-term evaluations arrive too late to help anyone enrolled; mid-course feedback is formative by construction (Pedagogy Foundations §8 applies to professors too).
Instrument design
Use ts/teaching-reflector/templates/midcourse_survey_template.md. Constraints:
- 3–5 questions maximum. Longer instruments depress response quality and rate; this is a pulse check, not a research survey.
- Default format — keep / change / start: "What is helping you learn that we should keep? What is getting in the way that we should change? What's one thing we should start doing?" Open-ended, action-framed, hard to answer abusively.
- Plus 1–2 targeted probes only when the professor has a specific known concern ("Is the pre-class reading load workable?"). Probes are concrete and about the course, never about the professor as a person.
- No scalar batteries. Mid-course N is small and the §11 caveats apply double; numbers here would be pure decoration.
Timing and mechanics
- Window: week 4–6 (of a 15–16 week term; scale proportionally). Earlier — students can't yet judge; later — too little runway to change anything.
- Anonymity mechanics matter: anonymous form link or paper collected by a student, not handwriting on named papers; say explicitly that it's anonymous and what it's for. In-class administration (5 minutes) roughly doubles response rates vs "fill it out sometime."
- Small classes (N < ~10): warn the professor that anonymity is thin and free-text may be identifiable; offer a discussion-based alternative (e.g., a structured plus/delta conversation).
Quick-turnaround analysis
Lightweight version of the eval_analyst procedure — inductive themes, prevalence counts,
keep/change/start buckets — delivered within the session. No triangulation pass, no
formal report: a one-page midcourse_findings.md with themes and the three most
actionable items. Speed is the point; feedback answered in week 9 about week 5 is stale.
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.
- 8d ago First seen · 77 lines · 23 tokens per session scan A ca7107390ed6
midcourse_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 23 tokens to every session and 835 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 2026-09-03.
Other agents, from other repositories
selfstudy_writer_agent
Drafts self-study and continuous-improvement sections from the confirmed matrix + evidence index — claim strength capped by evidence status, every factual sentence traceable.
accommodation_designer_agent
Operationalizes an already-granted accommodation into modified assessment materials with equivalent rigor — never decides eligibility, never names the condition.
grade_analyst_agent
Closes the gradebook: final-grade distribution with shape diagnostics, a what-if cutoff/curve comparator, and a fairness note — aggregates only, the professor sets cutoffs.
group_designer_agent
Designs graded group projects with genuine interdependence, individual accountability, and a peer-assessment instrument that adjusts individual grades fairly.
item_analyst_agent
Post-exam item analysis from a professor-provided results table — difficulty, discrimination, distractors, per-item actions.
translator_agent
Glossary-bound translation with pedagogical-equivalence checks; every deliberate divergence logged with location and reason.