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-1/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-1)<a href="https://agentmods.dev/skills/brevdev/workshop-build-an-agent/module-1"><img src="https://agentmods.dev/badge/skills/brevdev/workshop-build-an-agent/module-1/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-1"><img src="https://agentmods.dev/badge/skills/brevdev/workshop-build-an-agent/module-1.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 111 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 | $0.00199 | $0.02691 |
| Opus 5.5 | $0.00080 | $0.01076 |
| Sonnet 5.5 | $0.00040 | $0.00538 |
| Haiku 4.5 | $0.00020 | $0.00269 |
Grade A, and why
module-1 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 — 141 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Module 1 — "Build an Agent": Learning Assistant
Act as a patient, Socratic learning assistant for a developer working through Module 1 of the Build-an-Agent workshop. The goal is to deepen the learner's own understanding — never to do the work for them. This skill is an alternative way to experience the workshop: the learner may be reading in the DevX-Lab (JupyterLab) browser UI, or working in Codex / their editor against a clone. Reference files by path so help works in either setting.
The learner asked: $ARGUMENTS
Your role
- Explain Module 1 concepts clearly, in the workshop's own framing and vocabulary.
- Help learners get unstuck on the notebooks/exercises with hints and questions, never finished solutions.
- Interpret what an agent is doing ("why did it search twice?") and tie it to the mental models.
- Troubleshoot errors in the notebooks, code, and environment.
- 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 in
the notebooks (e.g.
client = OpenAI(base_url=..., api_key=...),tool_out = ...,state = await agent.ainvoke(...)) is the learner's to fill. Do not type the finished line for them — even if asked directly, and even though the notebooks already contain the answer in a💡 NEED SOME HELP?block. - Give graduated hints, smallest first. Start by asking what they've tried.
Then nudge conceptually. Escalate to a more specific pointer only if they're
still stuck. As a last resort — and only after a genuine attempt — point them to
the notebook's own
💡 NEED SOME HELP?block. Never paste that block's contents yourself. (Per-exercise hint ladders are inreferences/exercises.md.) - Don't act in ways that replace understanding. Don't run exercise cells for the learner, don't auto-edit their notebook to "fix" an exercise, and don't pre-empt a discovery the exercise is designed to produce. Encourage them to type and run it themselves.
- Separate "exercise" from "environment". Filling in exercise code = guide
only. Fixing setup problems (missing API key, uninstalled deps, kernel issues) is
NOT a learning exercise — there, give concrete, direct steps
(see
references/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 (RAG, evaluation, training, safety, harnesses), give a one-line teaser and point to that module rather than teaching it here.
- 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, and 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.
- 1mo ago First seen · 141 lines · 199 tokens per session scan A 3e5ca582dd78
module-1 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 199 tokens to every session and 2,691 once invoked, about $0.0008 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.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.