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 skills/jayrha/agentskills/study-plan-buildernpx skills add JayRHa/AgentSkills --skill study-plan-buildergit clone --depth 1 https://github.com/JayRHa/AgentSkillsWrote 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/jayrha/agentskills/study-plan-builder)<a href="https://agentmods.dev/skills/jayrha/agentskills/study-plan-builder"><img src="https://agentmods.dev/badge/skills/jayrha/agentskills/study-plan-builder.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 | $0.00115 | $0.01068 |
| Opus 5 | $0.00057 | $0.00534 |
| Sonnet 5 | $0.00023 | $0.00214 |
| Haiku 4.5 | $0.00012 | $0.00107 |
Grade A, and why
study-plan-builder 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 yesterday.
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 — 65 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Study Plan Builder
Overview
Produce a week-by-week learning plan that someone can actually follow. Work backward from the goal and deadline, sequence topics by dependency, and bake in active practice and spaced review rather than passive reading. Apply the evidence-based methods in references/learning-science.md.
Keywords: study plan, learning roadmap, curriculum, syllabus, exam prep, skill acquisition, spaced repetition, interleaving, milestones, schedule, time budget, deliberate practice.
Workflow
- Define the goal concretely. Turn "learn Python" into an observable outcome ("build and deploy a small Flask API", "pass the PCEP exam"). A measurable goal sets the scope.
- Establish constraints. Capture: deadline, hours/week available, current level, and preferred resource types. If unknown, assume ~5 hrs/week and beginner, and say so.
- Decompose into topics and order them by dependency (fundamentals before applications). Group into milestones — each a meaningful, demonstrable capability.
- Allocate time across the calendar working backward from the deadline; leave ~15% buffer for slippage and review. Don't overschedule.
- Design each week with the mix in
references/learning-science.md: input (read/watch) → active practice (exercises/projects) → retrieval (recall, flashcards, self-quiz). Practice should dominate over passive input. - Schedule spaced review of earlier material at expanding intervals, and interleave related topics rather than blocking one at a time.
- Add checkpoints. Every milestone ends with a concrete deliverable or self-test that proves mastery before moving on.
- Render the plan using
templates/study-plan.md. Include resources (by type, not a rigid single source), weekly goals, practice tasks, and review items. - Add an adjustment rule — what to do if behind (cut scope, not review) or ahead (deepen/project).
Decision Framework
| Situation | Plan choice |
|---|---|
| Hard deadline (exam) | Work backward from date; front-load content, back-load practice tests |
| Open-ended skill | Project-driven milestones; learn just-in-time for each project |
| Very limited time | Ruthless scope cut to the 20% that delivers 80%; daily micro-sessions |
| Building a habit | Short, consistent daily blocks beat rare long ones |
| Prerequisite gaps | Insert a "foundations" milestone before the main track |
What ships with it
2 files 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.
- yesterday First seen · 65 lines · 115 tokens per session scan A c95dabf6fd3f
study-plan-builder is a skill published in the GitHub repository JayRHa/AgentSkills (4 stars, last pushed 1mo ago), licensed MIT. It adds 115 tokens to every session and 1,068 once invoked, about $0.0006 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.
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