Borrowing it
Nothing to install: this file belongs to TakaGoto/rag-learning-academy. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/TakaGoto/rag-learning-academy/main/.claude/skills/recap/SKILL.mdgit clone --depth 1 https://github.com/TakaGoto/rag-learning-academyWrote 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/takagoto/rag-learning-academy/recap)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/recap"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/recap/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/takagoto/rag-learning-academy/recap"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/recap.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 | $0.00011 | $0.00394 |
| Opus 5 | $0.00005 | $0.00197 |
| Sonnet 5 | $0.00002 | $0.00079 |
| Haiku 4.5 | $0.00001 | $0.00039 |
Grade A, and why
recap 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 10d 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.
What it actually says
Recap: What You Learned Last Time
Give the learner a fast, useful summary of their most recent session so they can pick up where they left off.
Step 1: Load Session History
Read the following files to reconstruct the last session:
progress/module-tracker.md— find the most recently completed lessons (by date)progress/quiz-results.md— any quizzes taken recentlyprogress/journal.md— any notes the learner wroteprogress/learner-profile.md— current module and track
If no progress data exists, say: "No previous session found. Run /start to begin your journey."
Step 2: Summarize Last Session
Keep it to 5-8 lines max. Cover:
- What you worked on — which lesson(s), quiz, or challenge
- Key concepts — the 2-3 main ideas from that session, in plain language
- Where you stopped — what's next in the track
Format:
Last Session Recap
==================
You worked on: [lesson/quiz/challenge name]
Key concepts:
- [concept 1 in one sentence]
- [concept 2 in one sentence]
- [concept 3 in one sentence]
Next up: [next incomplete lesson or suggested action]
Step 3: Suggest Next Action
Based on where they left off, suggest one clear next step:
- If mid-module:
/lessonto continue - If they just finished a module:
/quizto test understanding - If quiz scores were low:
/explain [weak topic]to review - If they wrote journal notes with questions: address the question briefly
Keep it short. The whole recap should fit in one screen.
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.
- 10d ago First seen · 52 lines · 11 tokens per session scan A 98cf89ff4b7d
recap is a skill published in the GitHub repository TakaGoto/rag-learning-academy (19 stars, last pushed 5mo ago), licensed MIT. It adds 11 tokens to every session and 394 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 2026-08-30.
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