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 skills add yugash007/edu-agent-skills --skill lesson-plangit clone --depth 1 https://github.com/yugash007/edu-agent-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/skills/yugash007/edu-agent-skills/lesson-plan)<a href="https://agentmods.dev/skills/yugash007/edu-agent-skills/lesson-plan"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/lesson-plan/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/yugash007/edu-agent-skills/lesson-plan"><img src="https://agentmods.dev/badge/skills/yugash007/edu-agent-skills/lesson-plan.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 | $0.00026 | $0.00806 |
| Opus 5 | $0.00013 | $0.00403 |
| Sonnet 5 | $0.00005 | $0.00161 |
| Haiku 4.5 | $0.00003 | $0.00081 |
Grade A, and why
lesson-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 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 — 60 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
Create a coherent learning roadmap that sequences sessions, defines milestones with verifiable gates, and prevents random topic jumping. Gives the learner a clear path from current level to stated goal.
Activation
- Learner has a defined goal + time constraint ("interview in 2 weeks", "learn this repo in a month"). Multiple sessions planned with non-trivial sequencing.
repo-understandproduced a curriculum map needing personalization. Learner asks "where do I start and how do I get to X?" - Skip if: one-off session with no continuity. Learner needs immediate help on a specific concept/bug. Plan already exists and is current.
- Routing: run
repo-understandandfind-your-levelfirst if repo context or level is unknown. Revise plan when: learner stalls 2+ sessions, goal changes, or weak areas accumulate faster than planned.
Inputs
- Learner goal (specific, time-bounded), level (from
find-your-level), available sessions/time budget, known constraints (weak areas, learning style), repo/domain scope.
Plan Structure
Three layers: Phases (thematic blocks) → Sessions (individual units with focus + gate) → Gates (acceptance criteria before advancing).
Workflow
- Decompose Goal — Restate as a specific, observable outcome (not "learn ML" but "train and evaluate a classifier on custom dataset"). Identify 3–5 sub-goals.
- Check Prerequisites — For each sub-goal: list prerequisites, check which are confirmed, flag gaps as early sessions.
- Define Phases — Group sub-goals into 2–4 phases. Each phase has a coherent outcome and takes 2–5 sessions. Order so each phase's output feeds the next.
- Sequence Sessions — Break each phase into sessions. Each has: primary topic, skill(s) to use, gate criterion. Max 2 heavy concepts per session.
- Set Gates — Measurable acceptance criteria per session: "can explain X mechanism," "passes challenge-generator at intermediate tier," "completes repo exercise." Gates prevent surface-level advancement.
- Add Buffers — 1 revision session per 4–5 sessions. Define re-plan triggers: 2 consecutive gate failures → revise plan.
- Get Buy-In — Present as a proposal. Ask: "Does this pacing feel right?" Adjust before finalizing.
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.
- 12d ago First seen · 60 lines · 26 tokens per session scan A db4ed8b7746d
lesson-plan is a skill published in the GitHub repository yugash007/edu-agent-skills (7 stars, last pushed 3mo ago), licensed MIT. It adds 26 tokens to every session and 806 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 2026-08-31.
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