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/surfmind-space/awesome-surfmind/study-plannpx skills add surfmind-space/awesome-surfmind --skill study-plangit clone --depth 1 https://github.com/surfmind-space/awesome-surfmindWrote 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/surfmind-space/awesome-surfmind/study-plan)<a href="https://agentmods.dev/skills/surfmind-space/awesome-surfmind/study-plan"><img src="https://agentmods.dev/badge/skills/surfmind-space/awesome-surfmind/study-plan.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 | $0.00073 | $0.00702 |
| Opus 5 | $0.00036 | $0.00351 |
| Sonnet 5 | $0.00015 | $0.00140 |
| Haiku 4.5 | $0.00007 | $0.00070 |
Grade A, and why
study-plan 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 5d 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 — 53 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Study Plan
Build a staged learning plan from a topic, course page, or documentation set so the learner moves from where they are now to a concrete target outcome. Work from what is visible and what the user states; do not assume prior knowledge they haven't shown, and do not invent course names, deadlines, or resources.
- Pin down the learner's current level, target outcome, deadline, and available materials when visible; if a key constraint is missing, ask once or state the assumption you're making.
- Scale the plan to the scope: a one-hour topic gets a single session with a few steps, a multi-week goal gets weekly blocks, and a multi-month curriculum gets phases with milestones.
- Break each stage into a goal plus a practice loop — mix reading, active recall, exercises, and a small project, ending on a checkpoint the learner can self-assess.
- Fit the cadence to the user's available time, and don't overload any stage with more resources than they can finish.
Return concise headings and include these sections when they fit: Goal, Prerequisites, Learning path (staged, with time allocations), Practice tasks, Review checkpoints, Next session. If the context is insufficient, say what is missing and give the best supported next step.
Example
Request:
I want to learn React in 4 weeks, I already know JavaScript, about 5 hours a week.
Plan:
Goal: Build and ship a small React app in 4 weeks (~5 hrs/week).
Prerequisites: Comfortable with JS (you have this); a code editor and Node installed.
Learning path
- Week 1 — Fundamentals (5 hrs): components, JSX, props, state. Read the official "Learn React" intro; build a counter and a to-do list.
- Week 2 — Hooks & effects (5 hrs):
useState,useEffect, lists and keys. Fetch and render data from a public API.- Week 3 — Composition & routing (5 hrs): lifting state, forms, React Router. Add multiple pages to your week-2 app.
- Week 4 — Project (5 hrs): ship one app end to end (e.g. a movie search), then deploy it.
Practice tasks
- Rebuild the to-do list from memory without looking (active recall).
- Refactor one component to lift shared state up a level.
Review checkpoints
- End of week 2: explain when
useEffectruns and why, in your own words.- End of week 4: your deployed app loads, fetches data, and routes between pages.
Next session: Start week 1 by reading "Describing the UI" and building the counter — budget ~1 hour.
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.
- 5d ago First seen · 53 lines · 73 tokens per session scan A 794adb2db7a1
study-plan is a skill published in the GitHub repository surfmind-space/awesome-surfmind (5 stars, last pushed 1mo ago), licensed MIT. It adds 73 tokens to every session and 702 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.
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