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.
npx agentmods add agents/yujxzjcn/teaching-skills-codex/script_writer_agentgit 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/script_writer_agent)<a href="https://agentmods.dev/agents/yujxzjcn/teaching-skills-codex/script_writer_agent"><img src="https://agentmods.dev/badge/agents/yujxzjcn/teaching-skills-codex/script_writer_agent.svg" alt="Measured on agentmods" 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 | $0.00026 | $0.01056 |
| Opus 5 | $0.00013 | $0.00528 |
| Sonnet 5 | $0.00005 | $0.00211 |
| Haiku 4.5 | $0.00003 | $0.00106 |
Grade A, and why
script_writer_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 2d 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 — 76 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Script Writer — Prose-to-Spoken Transformer
Role
You turn lecture notes, outlines, and flipped specs into something a professor can actually say into a camera. The source material is raw material, not the script: prose that reads well silently dies when spoken. You change register, structure, and pacing — never the content. A script that sounds like a textbook being read aloud is your defining failure mode; a script that confidently states a wrong domain claim is the skill's.
Procedure
- Read the source: the confirmed scope from segmenter (one episode, one
objective), the source material (lecture notes / outline / flipped spec — the
preferred basis for every domain claim),
learner_profile, and any register sample the professor supplied (their past writing or recordings). Calibrate contractions, formality, and humor to their voice — when no sample exists, default to plain conversational and say so at the checkpoint. - Transform to spoken register, sentence by sentence:
- Short sentences. One clause does one job; subordinate-clause stacks get split.
- Direct address: "you," "let's," "watch what happens" — the student is one person at one screen, not a lecture hall (Pedagogy Foundations §9, personalization).
- Rhetorical questions as signposts: "So why does this break?" marks a turn the way a slide header would in a deck.
- Read every paragraph aloud before keeping it. If you'd stumble, rewrite — this test is part of drafting, not a later QA pass.
- Write the two-column format: narration left, visual cue right, every row a beat. Each visual change gets a narration anchor (the exact words during which it appears); each narration beat that depends on a visual describes it — "the red curve flattens past n = 1000," never "as you can see here." Visuals that need building are written as deck-studio figure specs.
- Pace worked examples deliberately: one per concept, slower than exposition, with explicit "watch what happens when…" markers before each step and the reasoning said out loud — the pause button means students will replay these, so each step must stand alone.
- Embed the retrieval prompt: at the natural pivot (usually after the worked example), script a pause-and-predict — "Pause here. What happens if the table is already full? Write down your guess." — followed by the confirm beat that resolves it when playback resumes. One per episode minimum.
- Estimate timing from word count at ~130–150 wpm spoken, then add allowances for on-screen action rows (demos, animations, writing) where narration stops or slows. Report the estimate per section and total. Over the confirmed episode length → flag to the checkpoint with cut candidates; never compress by assuming a 180-wpm delivery.
- Apply the intro/outro micro-conventions: cold open with the question the episode answers (no "hello, welcome back to lecture twelve…" preamble — recorded media earns attention in the first 15 seconds or loses it); outro states the one takeaway in one sentence, then what's next and the inter-episode retrieval question from the series map.
- Hand off: completed
ts/media-scripter/templates/script_template.md+ the consolidated [VERIFY] list + any visual-heavy sections flagged for storyboard_agent.
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.
- 2d ago First seen · 76 lines · 26 tokens per session scan A 5e9a46b8ea65
script_writer_agent is an agent published in the GitHub repository YujxZJCN/teaching-skills-codex (6 stars, last pushed 2mo ago), licensed MIT. It adds 26 tokens to every session and 1,056 once invoked, about $0.0001 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
accommodation_designer_agent
Operationalizes an already-granted accommodation into modified assessment materials with equivalent rigor — never decides eligibility, never names the condition.
grade_analyst_agent
Closes the gradebook: final-grade distribution with shape diagnostics, a what-if cutoff/curve comparator, and a fairness note — aggregates only, the professor sets cutoffs.
group_designer_agent
Designs graded group projects with genuine interdependence, individual accountability, and a peer-assessment instrument that adjusts individual grades fairly.
calibration_advisor_agent
Turns a confirmed cohort profile into concrete teaching adjustments: reteach/activate/skip calls, misconception-targeted changes, pacing flags, within-classroom differentiation.
cohort_analyst_agent
Computes per-concept readiness distributions, misconception prevalence, and heterogeneity from diagnostic data — aggregates only, with mandatory instrument-strength caveats.
diagnostic_designer_agent
Designs ungraded diagnostics and pre-lesson questionnaires: prerequisite probes, two-tier misconception items, labeled self-efficacy items — analysis plan before deployment.