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 Szewowsky/learn-skill --skill learn-reviewgit clone --depth 1 https://github.com/Szewowsky/learn-skillWrote 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/szewowsky/learn-skill/learn-review)<a href="https://agentmods.dev/skills/szewowsky/learn-skill/learn-review"><img src="https://agentmods.dev/badge/skills/szewowsky/learn-skill/learn-review/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/szewowsky/learn-skill/learn-review"><img src="https://agentmods.dev/badge/skills/szewowsky/learn-skill/learn-review.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 | $0.00085 | $0.01687 |
| Opus 5.5 | $0.00034 | $0.00675 |
| Sonnet 5.5 | $0.00017 | $0.00337 |
| Haiku 4.5 | $0.00009 | $0.00169 |
Grade A, and why
learn-review 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 — 184 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Skill: Learn Review — Pętla Zwrotna Nauki
Dlaczego ten skill istnieje
Obejrzenie filmiku ≠ nauka. Ten skill sprawdza czy faktycznie rozumiesz temat — nie czy zapamiętałeś fakty, ale czy potrafisz wytłumaczyć koncept kumplowi (Feynman probe) i zastosować wiedzę (quiz). Waga 60% na Feynmana, bo jeśli umiesz wytłumaczyć prosto — rozumiesz naprawdę.
Metryka: Composite Learning Score (CLS)
CLS = quiz_avg × 0.4 + feynman_score × 0.6
| CLS | Wynik | Akcja |
|---|---|---|
| >= 80% | PASSED | Achievement unlocked, następny quest |
| 60-79% | SOFT PASS | Przechodzi, wraca za 3 dni |
| < 60% | FAILED | Adaptacja + powtórka |
Flow
Krok 1: Znajdź feedback-loop
Sprawdź output directory (env LEARN_OUTPUT_DIR, fallback ./learn-output/).
Jeśli argument podany:
- Jeśli to ścieżka do pliku → użyj bezpośrednio
- Jeśli to slug → szukaj
*[slug]*feedback-loop.mdw output dir
Jeśli brak argumentu:
- Szukaj
*feedback-loop.mdw output dir - Jeśli jeden plik → użyj go
- Jeśli wiele → pokaż listę i zapytaj który
- Jeśli zero → powiedz że trzeba najpierw
/learn [temat]z quest generation
Krok 2: Czytaj stan nauki
Przeczytaj feedback-loop.md. Znajdź:
- Który quest ma status
pending(to jest bieżący do review) - Jakie pre-generowane pytania ma ten quest
- Nazwa pliku quest chain (z YAML front matter:
quest_file)
Przeczytaj też quest chain żeby zobaczyć checkboxy.
Krok 3: Sprawdź ukończenie questa
Przeczytaj quest chain i sprawdź checkboxy dla bieżącego questa:
- Wszystkie
- [x]→ quest ukończony, przejdź do Review Gate - Jakiekolwiek
- [ ]→ quest nieukończony
Jeśli nieukończony:
Quest "[nazwa]" nie jest jeszcze ukończony — brakuje kroków: [lista]. Dokończ i wróć z
/learn-review.
Krok 4: Review Gate
4a. Quiz
Wyświetl pre-generowane pytania quizowe (z feedback-loop.md), jedno po drugim:
Quiz — Quest X: [Nazwa]
Pytanie 1/3 (mechanizm):
[treść pytania]
Twoja odpowiedź:
Poczekaj na odpowiedź. Potem następne pytanie. Potem następne.
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 · 184 lines · 85 tokens per session scan A ad14e027c128
learn-review is a skill published in the GitHub repository Szewowsky/learn-skill (2 stars, last pushed 7mo ago), licensed MIT. It adds 85 tokens to every session and 1,687 once invoked, about $0.0003 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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