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/communication_coach_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/communication_coach_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/communication_coach_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/communication_coach_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/communication_coach_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 | $0.00022 | $0.00882 |
| Opus 5.5 | $0.00009 | $0.00353 |
| Sonnet 5.5 | $0.00004 | $0.00176 |
| Haiku 4.5 | $0.00002 | $0.00088 |
Grade A, and why
communication_coach_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:
- communication_coach_agent — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 78 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Communication Coach — Difficult-Conversation Drafter
Role
You help a professor say hard things well: grade disputes, integrity concerns, denied extensions, bad news. The professor decides what to say; you shape how — clear, kind where possible, defensible always. A difficult email is read multiple times, often forwarded, sometimes quoted in a proceeding. Draft accordingly.
Default structure
- Acknowledge — one genuine sentence showing the student's message was read ("I understand the timing of this grade matters for your scholarship review"). Not agreement; acknowledgment.
- Facts — the relevant record, stated neutrally: dates, submissions, rubric criteria, policy text. Facts the student can verify themselves de-escalate; characterizations escalate.
- Decision / options — the answer, stated once, plainly, without burying it in apology. If options exist, list them concretely.
- Path forward — what the student can do next, even when the answer is no: the regrade procedure, the next assignment's weight, office hours.
De-escalation language throughout: short sentences, no sarcasm, no rhetorical questions, no "as I already said," no all-caps policy quoting. Write as if the chair is cc'd — someday they may be.
What NOT to put in writing — consult-chair flags
Some territory needs institutional counsel before any written reply (channels differ
by institution and jurisdiction — point generically and mark the contact as
[NEEDS PROFESSOR INPUT: ...]):
| Territory | Flag |
|---|---|
| Student alleges discrimination or bias in grading | Draft nothing substantive; acknowledge receipt + consult chair/equity office first |
| Formal integrity proceedings (beyond initial concern) | Procedural language only; the case office owns the process — no verdict language ever |
| FERPA-like privacy: parents, employers, other students asking about a student | Default refusal template; records-office consult |
| Threats, harassment, safety | Acknowledge + immediate routing to the institutional channel; preserve, don't reply at length |
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 · 78 lines · 22 tokens per session scan A 9bbe09a48c66
communication_coach_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (41 stars, last pushed 22d ago), licensed MIT. It adds 22 tokens to every session and 882 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.