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/break-it/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/break-it)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/break-it"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/break-it/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/break-it"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/break-it.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 | $0.00015 | $0.00811 |
| Opus 5 | $0.00008 | $0.00405 |
| Sonnet 5 | $0.00003 | $0.00162 |
| Haiku 4.5 | $0.00002 | $0.00081 |
Grade A, and why
break-it 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 9d 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 — 85 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Break It: Learn by Debugging
Introduce a realistic bug into a RAG pipeline and challenge the learner to find and fix it. Debugging teaches more than building because you have to understand why things work to figure out why they don't.
Language awareness: See
.claude/LANGUAGE_AWARENESS.md.
Step 1: Choose a Difficulty
If the learner specifies a difficulty (e.g., /break-it hard), use that. Otherwise, calibrate based on their progress:
- Easy — One obvious bug. The pipeline runs but gives clearly wrong results.
- Medium — One subtle bug. The pipeline runs and results look plausible but are wrong.
- Hard — Two bugs that interact. Fixing one makes the other more visible.
Step 2: Select a Bug Category
Pick from these common RAG failure modes. Rotate through categories so the learner sees variety:
Chunking Bugs
- Chunk size set way too small (10 tokens) — fragments lose all context
- Zero overlap — information at boundaries is lost
- Wrong separator for the document type (splitting code on paragraphs)
Embedding Bugs
- Mismatched embedding models between indexing and querying
- Missing query prefix for asymmetric models (e.g., no "query: " prefix for E5)
- Embeddings not normalized, breaking cosine similarity
Retrieval Bugs
- Top-k set to 1 — missing relevant context spread across chunks
- Wrong distance metric (L2 instead of cosine)
- Metadata filter too restrictive — filtering out relevant results
Generation Bugs
- Context placed after the question (lost-in-the-middle effect)
- No grounding instruction — model ignores context and hallucinates
- Context window exceeded — retrieved chunks silently truncated
Pipeline Bugs
- Stale index — new documents added but not re-embedded
- Document not chunked before embedding (entire doc as one vector)
- Query embedded with a different model than the corpus
Step 3: Present the Broken Pipeline
Show the learner a complete, runnable pipeline with the bug(s) already in place. Include:
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.
- 9d ago First seen · 85 lines · 15 tokens per session scan A 42ed5ddf9f42
break-it is a skill published in the GitHub repository TakaGoto/rag-learning-academy (18 stars, last pushed 5mo ago), licensed MIT. It adds 15 tokens to every session and 811 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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