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/storyboard_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/storyboard_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/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-codex/storyboard_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/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 | $0.01057 |
| Opus 5 | $0.00016 | $0.00528 |
| Sonnet 5 | $0.00006 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00106 |
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 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 — 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 (
ts/media-scripter/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.
- 8d ago First seen · 73 lines · 32 tokens per session scan A 9c622faf858a
storyboard_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 32 tokens to every session and 1,057 once invoked, about $0.0002 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
glm-cinematographer
Stage 5 of the GLM chain. Writes the shot list against the curriculum and dossier. Camera grammar is setcameraorientation / movecamera only. Reference images are visual seeds, never teaching sources.
presentation-coach
Presentation coach. Provides slide-by-slide speaker notes, timing allocation, audience engagement strategies, anticipated Q&A responses, and rehearsal guides.
designer
Design specialist for frontend UI/UX design, Pencil (.pen), image generation, video production, and TikTok content. Covers web components, pages, and applications. Orchestrates visual creation tools.
Technique Agent
Evaluates guitar fingerings, suggests ergonomic alternatives, validates playability, and provides technique-focused advice for chord voicings and passages.
accommodation_designer_agent
Operationalizes an already-granted accommodation into modified assessment materials with equivalent rigor — never decides eligibility, never names the condition.
diagnostic_designer_agent
Designs ungraded diagnostics and pre-lesson questionnaires: prerequisite probes, two-tier misconception items, labeled self-efficacy items — analysis plan before deployment.