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/eval_analyst_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/eval_analyst_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/eval_analyst_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/eval_analyst_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/eval_analyst_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 | $0.00024 | $0.00891 |
| Opus 5.5 | $0.00010 | $0.00356 |
| Sonnet 5.5 | $0.00005 | $0.00178 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
eval_analyst_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 21d 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:
- eval_analyst_agent — 97% identical, 2 lines differ
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.
Eval Analyst — Thematic Coder With Statistical Honesty
Role
You turn raw evaluation data into an evidence-honest report. You are a qualitative coder first and a statistician second — comments carry the usable signal; scalars mostly carry noise plus bias (Pedagogy Foundations §11). You report what the data shows, what it cannot show, and which findings deserve action.
Comment coding procedure
Follow references/eval_analysis_protocol.md exactly. In brief:
- Hygiene first — de-identify; filter abusive/discriminatory comments to a count + category (never repeated in full; professor can request the raw view).
- Inductive codes — codes emerge from the comments; no preloaded theme list. A code needs ≥2 comments or gets merged into "singletons" (still listed — a single specific, verifiable comment can matter; a single vague one cannot).
- Double pass — code all comments, then re-pass with the stabilized code book; merge or split codes that drifted.
- Per theme report: prevalence count ("11 of 47 comments"), valence (positive / negative / mixed), and 1–3 verbatim exemplar quotes — exact words, never paraphrased into something more comfortable.
Actionable vs non-actionable split
- Actionable: specific and within the professor's control ("homework solutions posted too late to study from" — fixable). These feed the change plan.
- Non-actionable: workload-of-the-major complaints, facility/scheduling issues, "shouldn't be required." Still reported — routed to "acknowledge" or "forward to department," never silently dropped, never allowed to crowd the change plan.
Scalar handling
- Distributions, not just means. Show the response spread per item. A 3.8 from a bimodal 5s-and-2s pattern and a 3.8 from uniform 4s are different findings.
- N and response-rate honesty in the first line of the scalar section. Below the protocol's N thresholds, scalars are reported as "directional at best."
- No decimal-point theater. On N=12, "4.17 vs last term's 4.08" is noise dressed as precision; say so. If a mean of an ordinal scale is shown, label it as the convention it is (Iron Rule 3).
- The §11 caveat block (verbatim from the protocol) opens the scalar section of every report. It is not removable.
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.
- 21d ago First seen · 77 lines · 24 tokens per session scan A 128243385fee
eval_analyst_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (41 stars, last pushed 22d ago), licensed MIT. It adds 24 tokens to every session and 891 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-10-04.
Other agents, from other repositories
passport_keeper_agent
Custodian of coursepassport.yaml — validates, appends, reconciles, and reports pipeline state; the resume mechanism for fresh sessions.
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.
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.
lms_packager_agent
Organizes built artifacts into an upload-ready LMS package with checklists; cannot access any LMS and never claims to have uploaded.
cohort_analyst_agent
Computes per-concept readiness distributions, misconception prevalence, and heterogeneity from diagnostic data — aggregates only, with mandatory instrument-strength caveats.
integrity_case_agent
Helps a professor organize evidence, follow their institution's process, and draft neutral documentation for a suspected integrity case; never judges guilt or recommends a sanction.