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 KhaledSaeed18/dotclaude --skill learning-plangit clone --depth 1 https://github.com/KhaledSaeed18/dotclaudeWrote 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/khaledsaeed18/dotclaude/learning-plan)<a href="https://agentmods.dev/skills/khaledsaeed18/dotclaude/learning-plan"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/learning-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/khaledsaeed18/dotclaude/learning-plan"><img src="https://agentmods.dev/badge/skills/khaledsaeed18/dotclaude/learning-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 | $0.00108 | $0.01000 |
| Opus 5.5 | $0.00043 | $0.00400 |
| Sonnet 5.5 | $0.00022 | $0.00200 |
| Haiku 4.5 | $0.00011 | $0.00100 |
Grade A, and why
learning-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 1mo 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning fails from three causes: no clear end state, resources consumed in the wrong order, and no retrieval practice. A plan fixes all three before the first hour is spent. The plan is measured by what the learner can do, not by what they have read.
Step 1: define the goal as a test
Write the goal as something the learner will do at the end, observable and dated: "By 30 Nov, implement and evaluate a retrieval-augmented pipeline on my thesis dataset and explain each design choice to my supervisor." If the stated goal is "understand X", ask what they would do with the understanding until it becomes a task.
Derive the sub-skills the test requires (usually four to eight). These are the units.
Step 2: locate the learner
Current level per sub-skill (none, some, working, solid), from what they say and, when possible, a two-minute probe question per unit. Prerequisites missing become the first units. Skip what is already solid; a plan that re-teaches the known is abandoned by week two.
Step 3: choose resources
One primary resource per unit (a chapter, a course module, a paper, official docs), chosen for being the canonical or best-explained source, not the most popular. One practice source per unit (exercises, a small build, a dataset). Optional reference links. No unit has more than three resources; the learner should never be choosing what to read.
For a research field: the survey paper first, then the two or three foundational papers, then the recent ones from literature-search; paper-reader for the notes.
Step 4: sequence and schedule
Order units by dependency, then by usefulness to the goal. Assign each a time box (hours) from the learner's weekly budget, with 20% slack. Interleave: a week never contains only reading. Each week has:
- Learn: the primary resource for the current unit.
- Practice: the exercise or build for the unit, done from memory before consulting notes.
- Retrieve: 15 minutes recalling the previous units without notes (self-explanation, flashcards, or teaching it aloud); spaced at 1 day, 1 week, 1 month after each unit.
- Build: progress on the capstone project, which starts by week two, not at the end.
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.
- 1mo ago First seen · 69 lines · 108 tokens per session scan A 6b04a1b23385
learning-plan is a skill published in the GitHub repository KhaledSaeed18/dotclaude (5 stars, last pushed yesterday), licensed MIT. It adds 108 tokens to every session and 1,000 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-09-22.
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