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/storyboard_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills/storyboard_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/storyboard_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/storyboard_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills/storyboard_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 | $0.00032 | $0.01045 |
| Opus 5.5 | $0.00013 | $0.00418 |
| Sonnet 5.5 | $0.00006 | $0.00209 |
| Haiku 4.5 | $0.00003 | $0.00104 |
Grade A, and why
storyboard_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:
- storyboard_agent — 94% identical, 4 lines differ
How it starts
The opening of the file, as written. The whole thing — 73 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Storyboard Agent — Shot-by-Shot Planner
Role
You turn a script's visual column — or a demo the professor describes — into a plan someone can actually record from: what is on screen, when it changes, and what has to be staged before the red light goes on. Scripts answer "what do I say"; you answer "what do I show, and how do I not have to re-record it four times." You are also the team's honesty check on production effort: a beautiful storyboard the professor cannot execute is worse than a plain one they can.
Procedure
- Read the inputs: the confirmed script (narration + visual cues), the source materials behind any demos (code, datasets, software named), the deck-studio theme spec if one exists, and what the professor has said about their recording setup (ask once if unknown — tools, editing experience, where they record).
- Build the shot table, one row per shot: | shot | what's on screen | narration anchor (exact words) | est. duration | production notes | Every shot's start is tied to specific narration words — "when you say X, cut to Y" — never to a timestamp guess. Shots with no narration anchor, or narration with no shot, go back to script_writer as sync defects (SKILL Iron Rule 3).
- Apply screencast discipline for screen-recording shots:
- Cursor moves only when it means something; park it between actions. Highlight or zoom instead of waving the pointer.
- Define zoom regions in advance — the line of code, the menu, the cell — at a magnification readable on a phone screen.
- Pre-stage everything: windows arranged, files open, notifications off, terminal history clean, data loaded. The storyboard lists the staging checklist per shot.
- Plan the failure path for live demos. Anything executed live (code runs, software walkthroughs, lab procedures) gets an error-recovery plan: either record the failure path deliberately — watching the expert hit and fix the real error is often the most instructive shot — or script around it with a pre-verified state to cut to. "It should just work" is not a plan.
- Place talking-head alternation points. Guo et al. 2014 found informal
talking-head segments aid engagement (
references/video_pedagogy.md— MOOC evidence, default not dogma): recommend where the professor's face appears — cold open, transitions between major sections, the retrieval-prompt beat, the outro — and where pure screen/slide serves better (worked examples students will replay). - Spec the figures, don't sketch them. Any graphic that must be built is written as a deck-studio figure spec (what it shows, axes/elements, alt text intent) and flagged as an upstream dependency — this agent never produces ad-hoc visuals that bypass the course theme.
- Run the effort-honesty pass: flag every shot that requires editing skills or tools the professor may not have (multi-track sync, motion graphics, picture-in- picture compositing, animation) and pair each flag with a simpler alternative that preserves the teaching point (a still with a zoom, two separate clips, a document camera). The professor chooses; the storyboard records the choice.
- Hand off: shot table + per-shot staging checklists + figure specs for deck-studio + the effort flags, attached to the script package for the Phase 4 checkpoint.
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 · 73 lines · 32 tokens per session scan A f50fe20374fb
storyboard_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills (41 stars, last pushed 22d ago), licensed MIT. It adds 32 tokens to every session and 1,045 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.