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/sandbox/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/sandbox)<a href="https://agentmods.dev/skills/takagoto/rag-learning-academy/sandbox"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/sandbox/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/sandbox"><img src="https://agentmods.dev/badge/skills/takagoto/rag-learning-academy/sandbox.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 | $0.00013 | $0.00650 |
| Opus 5 | $0.00006 | $0.00325 |
| Sonnet 5 | $0.00003 | $0.00130 |
| Haiku 4.5 | $0.00001 | $0.00065 |
Grade A, and why
sandbox 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.
How it starts
The opening of the file, as written. The whole thing — 72 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sandbox: Get a Working RAG Pipeline in 5 Minutes
Scope: This skill scaffolds a minimal pipeline for quick experimentation. For guided, step-by-step building with explanations, use
/build.
Scaffold a complete, minimal RAG pipeline with sample data so the learner has something running immediately. This is for experimentation, not production.
Language awareness: See
.claude/LANGUAGE_AWARENESS.md.
Step 1: Check Environment
Read progress/learner-profile.md for the learner's chosen language. If no profile exists, default to Python.
Check if dependencies are installed. If not, guide the learner through setup:
Python:
pip install chromadb langchain sentence-transformers
TypeScript:
npm install chromadb langchain @langchain/community
For Go/Rust, note that setup is more manual and offer to walk through it.
Step 2: Create the Sandbox
Create a sandbox/ directory with three files:
File 1: Sample Data (sandbox/data.txt)
A short collection of 5-10 paragraphs about a topic (e.g., coffee brewing methods, or RAG itself). Keep it under 2000 words. The data should be interesting enough that queries feel meaningful.
File 2: The Pipeline (sandbox/pipeline.[ext])
A single-file RAG pipeline that:
- Loads the sample data
- Chunks it (fixed-size, 200 tokens, 50 overlap)
- Embeds chunks using a local model (all-MiniLM-L6-v2)
- Stores in ChromaDB (in-memory)
- Takes a query, retrieves top-3 chunks
- Prints the retrieved chunks with similarity scores
No LLM generation step yet. Keep it simple: retrieval only.
File 3: README (sandbox/README.md)
Quick instructions: how to run it, what to try, and 3 suggested experiments:
- Try different queries and see what comes back
- Change the chunk size and see how results change
- Add your own data file and query it
Step 3: Run It Together
Run the pipeline with a sample query and show the output. Walk through what happened at each step: "Here's where it chunked your data... here's the embedding step... here's what ChromaDB returned."
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 · 72 lines · 13 tokens per session scan A 1b425e3b417c
sandbox is a skill published in the GitHub repository TakaGoto/rag-learning-academy (19 stars, last pushed 5mo ago), licensed MIT. It adds 13 tokens to every session and 650 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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