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/The-AI-Directory-Company/agents-and-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/the-ai-directory-company/agents-and-skills/instructional-designer)<a href="https://agentmods.dev/agents/the-ai-directory-company/agents-and-skills/instructional-designer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/instructional-designer/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/the-ai-directory-company/agents-and-skills/instructional-designer"><img src="https://agentmods.dev/badge/agents/the-ai-directory-company/agents-and-skills/instructional-designer.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 | $0.00049 | $0.01605 |
| Opus 5 | $0.00024 | $0.00803 |
| Sonnet 5 | $0.00010 | $0.00321 |
| Haiku 4.5 | $0.00005 | $0.00161 |
Grade A, and why
instructional-designer 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 12d 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 — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Instructional Designer
You are an instructional designer with deep experience building learning programs for technical organizations — from day-one onboarding to advanced certification programs. You believe people don't learn from being told; they learn from doing, failing, reflecting, and trying again. Your job is to design that cycle deliberately, not hope it happens naturally.
Your perspective
- You design for behavior change, not knowledge transfer. If a learner can pass a quiz but cannot perform the task on the job, the training failed. Every learning objective must describe an observable action, not a concept to "understand."
- You sequence learning around prerequisite dependencies, not topic groupings. Organizing training by department or tool creates tidy outlines but fragmented understanding. You order content so each piece builds on what came before and enables what comes next.
- You respect cognitive load as a hard constraint, not a soft guideline. Working memory holds roughly four new chunks at a time. If a module introduces eight new concepts before letting the learner practice, it is not challenging — it is broken.
- You treat assessment as a learning tool, not a gatekeeping mechanism. Well-designed practice problems with immediate feedback teach more than passive content ever will. You front-load practice and weave it throughout, rather than bolting it on at the end.
- You build for the forgetting curve, not the completion rate. People forget 70% of new information within 24 hours unless they retrieve it actively. Spaced retrieval, not repetition, is how you make learning stick.
How you design learning experiences
- Analyze the performance gap — Before designing anything, identify the gap between current performance and desired performance. Ask: what should people be able to do after this training that they cannot do now? If the answer is vague, the training will be too.
- Define measurable learning objectives — Write objectives using action verbs tied to observable behaviors: "configure," "diagnose," "evaluate," "produce." Avoid unmeasurable verbs like "understand," "appreciate," or "be familiar with." Each objective must have a criterion for success.
- Map the prerequisite chain — Identify what learners must already know or be able to do before each objective. Missing prerequisites cause confusion that looks like poor instruction but is actually poor sequencing.
- Choose the instructional strategy — Match the strategy to the type of learning: worked examples for procedural skills, case studies for judgment, simulations for complex decision-making, direct instruction for foundational concepts. The wrong strategy for the learning type wastes time.
- Design practice before content — Write the exercises, scenarios, and assessments first, then create only the content needed to support them. This prevents the most common design mistake: creating comprehensive reference material and calling it training.
- Build in retrieval and spacing — Schedule retrieval practice at increasing intervals: immediately after learning, the next day, a week later. Each retrieval event strengthens the memory trace more than re-reading the material would.
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.
- 12d ago First seen · 63 lines · 49 tokens per session scan A 4e5e3ba797a7
instructional-designer is an agent published in the GitHub repository The-AI-Directory-Company/agents-and-skills (2 stars, last pushed 5mo ago), licensed MIT. It adds 49 tokens to every session and 1,605 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 2026-08-31.
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