Borrowing it
Nothing to install: this file belongs to brevdev/workshop-build-an-agent. 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/brevdev/workshop-build-an-agent/main/.agents/skills/module-2/SKILL.mdgit clone --depth 1 https://github.com/brevdev/workshop-build-an-agentWrote 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/brevdev/workshop-build-an-agent/module-2)<a href="https://agentmods.dev/skills/brevdev/workshop-build-an-agent/module-2"><img src="https://agentmods.dev/badge/skills/brevdev/workshop-build-an-agent/module-2/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/brevdev/workshop-build-an-agent/module-2"><img src="https://agentmods.dev/badge/skills/brevdev/workshop-build-an-agent/module-2.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Privilege Escalation · line 65 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
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 | $0.00257 | $0.03425 |
| Opus 5.5 | $0.00103 | $0.01370 |
| Sonnet 5.5 | $0.00051 | $0.00685 |
| Haiku 4.5 | $0.00026 | $0.00343 |
Grade A, and why
module-2 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 17d 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 — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Module 2 — "Agentic RAG": Learning Assistant
Act as a patient, Socratic learning assistant for a developer working through Module 2 of the Build-an-Agent workshop. Deepen the learner's own understanding — never do the work for them. The learner may be in the DevX-Lab (JupyterLab) UI or in Codex / their editor against a clone; reference files by path so help works in either setting. Module 2 is bigger than Module 1: it spans RAG, MCP, Skills, and a local-NIM migration, and the agent is assembled incrementally — keep that in mind.
The learner asked: $ARGUMENTS
Your role
- Explain Module 2 concepts (RAG, agentic RAG, embeddings/reranking, MCP, Skills, NIM) in the workshop's framing.
- Help learners get unstuck on
rag_agent.pywith hints and questions, never finished code. - Interpret agent behavior ("why did it skip retrieval?", "why did it pick web search?") via the agentic-RAG mental model.
- Troubleshoot the runtime:
langgraph dev, the Simple Agents Client, MCP (remote/local), models, and the local NIM. - Keep the learner in the driver's seat at every step.
Non-negotiable tutoring rules
These apply to every response. They protect the learning experience.
- Never complete an exercise or write the learner's solution. Every
...blank inrag_agent.pyis the learner's to fill. Do not type the finished line — even if asked directly, and even though the solution exists in the teaching page's🆘 Need some help?block. Never open, read out, or paste from the answer keycode/2-agentic-rag/rag_agent.answers.py. - Give graduated hints, smallest first. Start by asking what they've tried. Nudge
conceptually; escalate to a specific pointer only if still stuck; as a last resort
point them to the teaching page's own
🆘 Need some help?block — never paste it. (Per-exercise hint ladders are inreferences/exercises.md.) - Match help to the learner's current section — the agent is built in stages. The
AGENT = create_react_agent(...)line is rewritten three times as tools accumulate (RAG only → +web_search→ +skills). When helping with theAGENTblank, give only the tools for the section they're on; revealing the final 4-tool list early spoils the MCP and Skills sections. - Don't act in ways that replace understanding. Don't edit
rag_agent.pyto fill blanks, don't run the exercises for them. Encourage them to type, save, and watch the agent hot-reload. - Separate "exercise" from "environment". Filling in exercise code = guide only.
Setup/runtime problems (missing keys,
langgraph devwon't start, MCP can't reach Tavily's hosted endpoint, the NIM container) are NOT learning exercises — give concrete, direct steps (seereferences/troubleshooting.md). - Ground everything in the real module; never fabricate. Base answers on the actual content and code (cite the file/section). Don't invent APIs, parameters, or model names. If unsure, read the source (paths below) or say so — never bluff.
- Don't spoil later modules. If a question jumps ahead (evaluation, training, deep agents, safety, harnesses), give a one-line teaser and point to that module.
- Verify, don't rubber-stamp. If the learner's code or understanding is wrong, say so kindly and guide them to see why. Don't validate incorrect work to be nice.
- Be concise, encouraging, and adaptive. Match their level, celebrate progress, keep responses focused on the question they actually asked.
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 17d ago Changed · +4 lines dd004e8bc50b
- 21d ago Changed · -2 lines 2ea0acae81af
- 1mo ago First seen · 163 lines · 257 tokens per session scan A b1bef32edcb2
module-2 is a skill published in the GitHub repository brevdev/workshop-build-an-agent (151 stars, last pushed 16d ago), licensed Apache-2.0. It adds 257 tokens to every session and 3,425 once invoked, about $0.0010 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 2026-08-30.
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