Getting it into your agent
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add DaRL-GenAI/instructional_agents-skills/plugin install instructional-agentsWrote 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/skills/darl-genai/instructional_agents-skills/course-generate)<a href="https://agentmods.dev/skills/darl-genai/instructional_agents-skills/course-generate"><img src="https://agentmods.dev/badge/skills/darl-genai/instructional_agents-skills/course-generate/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/skills/darl-genai/instructional_agents-skills/course-generate"><img src="https://agentmods.dev/badge/skills/darl-genai/instructional_agents-skills/course-generate.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 | $0.00073 | $0.00813 |
| Opus 5 | $0.00036 | $0.00407 |
| Sonnet 5 | $0.00015 | $0.00163 |
| Haiku 4.5 | $0.00007 | $0.00081 |
Grade A, and why
course-generate 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 11d 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 — 99 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Course Generate (Full ADDIE Pipeline)
Runs the complete multi-agent course generation pipeline:
- Phase 1 — Foundation deliberations (learning objectives, resource assessment, target audience, syllabus, assessment planning, final project)
- Phase 2 — Per-chapter development (slides, scripts, assessments via SlidesDeliberation)
- Phase 3 — Evaluation (Program Chair + Test Student review)
This is a long-running task — typically 20–60 minutes depending on model and number of chapters. Confirm scope with the user before launching.
When to invoke this skill
- User asks to generate a course / teaching materials / curriculum
- User provides a topic/subject and wants end-to-end output
- User wants to reproduce the paper's pipeline on new content
Do not invoke for:
- Just converting existing LaTeX → PPTX → use
latex-to-pptx - Only evaluating existing slides → use
slide-evaluate - Optimizing an existing slide chapter → (future:
slide-optimizeskill)
Prerequisites
pip install instructional-agentsOPENAI_API_KEYset in the environment- Disk space ~10–50 MB per course
Usage
python3 "${CLAUDE_PLUGIN_ROOT}/skills/course-generate/scripts/generate.py" \
--course "<course name>" \
[--model <model_name>] \
[--exp-name <tag>] \
[--catalog <catalog_name>] \
[--copilot <copilot_name>]
Arguments
| Arg | Required | Description |
|---|---|---|
--course |
yes | Course name/topic, e.g. "Reinforcement Learning" |
--model |
no | LLM model (default: gpt-4o-mini) |
--exp-name |
no | Subdirectory under exp/ (default: test) |
--catalog |
no | Pre-loaded reference catalog name |
--copilot |
no | Copilot mode with interactive feedback |
Example
python3 "${CLAUDE_PLUGIN_ROOT}/skills/course-generate/scripts/generate.py" \
--course "Introduction to Reinforcement Learning" \
--model gpt-4o \
--exp-name rl_undergrad_2026
Output structure
exp/<exp-name>/
├── learning_objectives.md
├── resource_assessment.md
├── target_audience.md
├── syllabus.md
├── assessment_planning.md
├── final_project.md
├── chapter_1/
│ ├── slides.tex
│ ├── slides.pdf (if compiled)
│ ├── script.md
│ └── assessment.md
├── chapter_2/...
└── evaluation/
├── program_chair_review.md
└── test_student_review.md
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 99 lines · 73 tokens per session scan A 03704db73e1b
course-generate is a skill published in the GitHub repository DaRL-GenAI/instructional_agents-skills (1 stars, last pushed 4mo ago), licensed MIT. It adds 73 tokens to every session and 813 once invoked, about $0.0004 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 2026-08-31.
Other skills, from other repositories
eat
Extract knowledge, frameworks, and methodologies from a URL, file, video, article, or podcast. Use when user says '/eat', 'eat this', 'eat from', shares a URL/file for insights, or wants to learn from a video/article without reading the whole thing. NOT for summarization or news digests. Requires yt-dlp, whisper or…
writer-style
Write original educational and long-form technical content in a specific author's authentic voice — courses, lessons, explainers, deep-dives, threads — using a two-layer voice pack (an always-on PRIMARY voice that reproduces the author's idiolect + SECONDARY craft borrowed from master writers, applied not…
learn-from-fix
Capture Elixir/Ecto/LiveView lessons and Hex API rules. Use after corrections or when asked to document learning, record a lesson, prevent a fixed mistake, or remember package guidance with --library.
elixir-idioms
OTP/BEAM patterns and Elixir idioms — GenServer, Supervisor, Task, Registry, pattern matching, with chains, pipes. Use when designing processes or debugging BEAM issues.
examples
Provide Phoenix, LiveView, Ecto, OTP, or Oban examples. Use when asked for sample code, a walkthrough, a proper implementation, or expected workflow output. Pair with domain skills. NOT for debugging, direct changes, best-practice advice, or audits.
intro
Walk through the Elixir/Phoenix plugin commands, workflow, and features in 6 interactive sections. Use when a new user wants to learn what the plugin offers or needs a refresher on available commands.