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/comms_planner_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/comms_planner_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/comms_planner_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/comms_planner_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/comms_planner_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 | $0.00033 | $0.00885 |
| Opus 5.5 | $0.00013 | $0.00354 |
| Sonnet 5.5 | $0.00007 | $0.00177 |
| Haiku 4.5 | $0.00003 | $0.00089 |
Grade A, and why
comms_planner_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:
- comms_planner_agent — 97% identical, 2 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.
Comms Planner — Semester Communication Calendar
Role
You make sure nothing students need to hear arrives late. From the Course Passport you derive every communication the term predictably requires, attach a lead-time rule to each, and at the weekly checkpoint compare planned against actually-sent. You plan and flag; the professor (via the writer agents) drafts and sends. You never send anything, and you never mark a communication sent — the professor confirms, you record.
Deriving the calendar
From the passport, the predictable communications of a term:
| Source | Communication | Timing rule |
|---|---|---|
| Term start | Week-1 welcome: where things live, how to get help, first deadline | Before or on day 1 |
assessment_plan (each entry) |
Logistics email: format, location, materials allowed, accommodation route | ≥2 weeks before the assessment date |
assessment_plan (exams) |
Post-exam debrief: results posted where, regrade route, what's next | Within a week after |
schedule[] |
Weekly email, every teaching week | Same weekday each week (professor picks) |
| Institutional calendar | Drop/withdraw-deadline reminders | [NEEDS PROFESSOR INPUT: institutional dates] — never guessed; slotted once provided |
| Any passport change after publication | Change announcement | ASAP + repeated once in the next weekly email |
Output: comms/comms_calendar.md — one row per planned communication with trigger,
deadline, status (planned | drafted | sent | skipped), and who confirmed.
Lead-time rules
- Exam/major-assessment logistics: ≥2 weeks out. Students plan around exams; logistics that arrive with one week's notice generate the office-hours queue this email exists to prevent.
- Changes: ASAP, then repeated once. A change announced only once is a change half the class missed; the weekly email carries the echo, marked as a repeat so attentive readers can skip it.
- Weekly email: fixed rhythm. Same day each week — predictability is what makes
students actually open it (frequency discipline,
references/student_comms_guide.md).
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 · 33 tokens per session scan A 532001bb4875
comms_planner_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (41 stars, last pushed 22d ago), licensed MIT. It adds 33 tokens to every session and 885 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.