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 duthaho/skillhub --skill learngit clone --depth 1 https://github.com/duthaho/skillhubWrote 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/duthaho/skillhub/learn)<a href="https://agentmods.dev/skills/duthaho/skillhub/learn"><img src="https://agentmods.dev/badge/skills/duthaho/skillhub/learn/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/duthaho/skillhub/learn"><img src="https://agentmods.dev/badge/skills/duthaho/skillhub/learn.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00164 | $0.02630 |
| Opus 5.5 | $0.00066 | $0.01052 |
| Sonnet 5.5 | $0.00033 | $0.00526 |
| Haiku 4.5 | $0.00016 | $0.00263 |
Grade A, and why
learn 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 — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.
learn — personalized tutor with memory
/learn <topic> — start or continue learning a topic
/learn — list topics in progress and what's due
/learn quiz <topic> — quiz-only session (no new material)
Answer one question per session: what should this learner do for the next N minutes to durably advance toward their goal? One unit per session, active recall before and after, everything scored and remembered. The point is durable learning, not an impressive wall of text.
This skill is deliberately keyless (native WebSearch/WebFetch only,
when verification is needed) and human-paced: the learner answers real
questions in chat; you grade what they actually wrote.
Modes (auto-detect)
- NEW — no
out/learn/<slug>.mdexists for the topic → interview + syllabus. - CONTINUE — a log exists → run the session loop from where it left off.
- STATUS —
/learnwith no topic → summarize all logs: per topic, progress (x/yunits), last session date, review items due, and a suggested next step. - QUIZ — "quiz me" → recall-only session from the review queue + covered units; grade, update the log, teach nothing new.
The learning log — memory across sessions
One file per topic: out/learn/<slug>.md (<slug> = topic lowercased,
non-alphanumerics → hyphens). It is the skill's entire memory
and it is the learner's file too — plain markdown they can read and edit.
Respect manual edits (a unit hand-marked done stays done; ask nothing).
# learn log: <Topic>
## Learner
Goal: <what they want to be able to DO> · Level: <self-described start point>
Background: <relevant experience to hook examples onto> · Session length: ~<N> min
Freshness: <stable — teach from knowledge · fast-moving — verify each unit before teaching>
## Syllabus <!-- status: ☐ not started · ◐ taught, not passed · ✓ passed -->
1. ✓ <unit — one teachable idea>
pass: <the check question/exercise that proves it> · rubric: <2–3 criteria>
2. ◐ <unit>
pass: <…> · rubric: <…>
## Review queue <!-- weak spots; due dates, not session counts -->
- <item missed> — missed 2026-07-05 (conf: sure) · due 2026-07-06 · passes 0/3
## Sources <!-- verified references; check here before searching again -->
- <title> — <url> · verified 2026-07-05 · covers: <units/claims>
## Sessions
| # | Date | Unit | Warm-up | Check | Notes |
|---|------|------|---------|-------|-------|
| 1 | 2026-07-05 | 1. <unit> | — | 3/4 | confused X with Y |
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 · 191 lines · 164 tokens per session scan A e4f65b54c66e
learn is a skill published in the GitHub repository duthaho/skillhub (9 stars, last pushed 1mo ago), licensed MIT. It adds 164 tokens to every session and 2,630 once invoked, about $0.0007 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 2026-08-31.
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examples
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intro
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land
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toy
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