workshop
Skill142 starsChanged 3 months agoApache-2.0View on GitHub
This skill should be used when a learner wants to navigate or understand the Build-an-Agent workshop as a whole — e.g. "/workshop where do I start?", "what order should I do the modules in?", "what does Module 3 need before I start?", "which module covers RAG / evaluation / training / safety?", "how do the modules connect?", "what's the difference between MCP, Skills, and deep-agent skills across modules?", "what have I finished and what's next?", "am I ready for the next module?". It is the workshop's overview + router that maps the seven-module arc and prerequisites, points the learner to the right /module-N skill, explains cross-module connections, and hosts the shared tutoring policy, glossary, and progress checks that all module skills draw on. For installing/launching the environment it points to the setup-workshop-nemoclaw skill (in-sandbox) and its setup-workshop-nemoclaw-operator half (host side).
7 other projects use a version of this, 1 of them edited.
Adopted by
Most recently taken up by
- qwwiwi/agentos-skills-publictheir own version, first seen Nov 1, 2026
- nivoset/skillstheir own version, first seen Oct 18, 2026
- brevdev/workshop-build-an-agentedited, first seen Oct 9, 2026
- raine/consult-llm-mcptheir own version, first seen Oct 8, 2026
- bu-bioinfo-ms/bioinfo-ms-claude-workshoptheir own version, first seen Jul 2, 2026
- anthropics/cwc-workshopstheir own version, first seen Jun 4, 2026
Other versions7 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- brevdev/workshop-build-an-agent★ 15684% same
Wrote their own version · 6
- anthropics/cwc-workshops★ 2.3ksame name
- raine/consult-llm-mcp★ 148same name
- nivoset/skills★ 4same name
- qwwiwi/agentos-skills-public★ 4same name
- bu-bioinfo-ms/bioinfo-ms-claude-workshop★ 2same name
- ondrej-svec/harness-lab★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
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- The first 60 shown.
The file
Files
Build-an-Agent Workshop — Guide & Router
The entry point and map for the Build-an-Agent workshop. Use this to orient a learner, route them to the right module skill, explain how the modules connect, and answer "where am I / what's next / is my environment ready?" — without doing their work for them.
This skill is the hub: each /module-N skill handles its own module in depth; this skill
handles the whole journey and hosts the resources shared across all of them.
Invoking the tutor: in this NemoClaw deployment the resident sandbox agent carries
these skills in its skill library, so the learner can simply ask it workshop questions;
alternatively, the learner runs claude in a JupyterLab terminal against the cloned
workshop repo and types /workshop for this overview or /module-N (1–7) for a specific
module. Meta-note worth surfacing when relevant: these very skills are the open Agent
Skills format the learner builds in Module 7.
The learner asked: $ARGUMENTS
The seven-module arc
Each module adds a capability and a matching discipline. Detailed version in references/map.md.
| # | Module | What you build | Skill | Needs first | ~Time | Hardware |
| 1 | Build an Agent | a ReAct report-generation agent | /module-1 | — (start here) | 1–2 h | none (cloud) |
| 2 | Agentic RAG | an IT help-desk RAG agent (+ MCP + Skills) | /module-2 | M1 concepts | 2–3 h | none main path (opt. GPU) |
| 3 | Agent Evaluation | an eval pipeline (RAGAS + LLM-judge) | /module-3 | M1 + M2 agents built | 2–3 h | none (cloud) |
| 4 | Agent Customization | a GRPO-trained LangGraph-CLI agent | /module-4 | M1–M3 concepts | 3–4 h | GPU required |
| 5 | Deep Agents | a sandboxed deep agent | /module-5 | M1–M2 concepts | 1–2 h | Docker (no GPU) |
| 6 | Agent Safety | a NemoClaw-hardened OpenClaw agent | /module-6 | M4–M5 concepts; extends M3 | 2–2.5 h | Docker + kernel ≥ 5.13 |
| 7 | Harnesses & Skills | a pi-style harness + portable skills | /module-7 | M1–M6 | 2–3 h | none main; opt. GPU (Ex4) |
Hard prerequisite: Module 3 evaluates the M1 and M2 agents, so those must be built first (the workshop sanctions pasting the M2 answer key to get a runnable agent-under-test). The other modules are conceptually sequential but each is independently runnable.
How to route a request
Map the learner's intent to the right skill, then hand off (or invoke it):
- "what is an agent / ReAct / tools / system prompt" → module-1
- "RAG, embeddings, reranking, MCP, agent skills,
langgraph dev" → module-2 - "evaluate, RAGAS, faithfulness, LLM-as-judge, metrics, datasets" → module-3
- "train, fine-tune, GRPO, synthetic data, reward, GPU/OOM" → module-4
- "deep agent, planning, sub-agents, sandboxing, deepagents" → module-5
- "safety, NemoClaw, OpenShell, Landlock, Privacy Router, red-team" → module-6
- "harness, context tax, lazy skills, pi/Hermes/Claude Code, Verified Skills, GPU skills" → module-7
- "install / set up / spin up the workshop, can't open JupyterLab" → the setup-workshop-nemoclaw skill (in-sandbox; host-side operators use setup-workshop-nemoclaw-operator)
- overview / order / prerequisites / "how do X and Y connect" / "what's next" → stay here
Shared resources (the hub the module skills point back to)
references/map.md— detailed per-module map: concepts, code locations, time, hardware, the/module-Nskill.references/connections.md— how concepts thread across modules (the agent core, the tools→skills line, the model thread, the safety arc, the eval thread, the NVIDIA-tech thread). For cross-module synthesis questions.references/glossary.md— shared definitions for terms that recur across modules. For "what does X mean?" regardless of module.references/tutor-policy.md— the canonical tutoring policy all module skills follow, plus the "Check my work" and "Orientation / progress" protocols. Read when unsure how to behave as a tutor.references/progress.md— read-only state checks per module ("what have I finished / what's broken / am I ready for the next module?").
Tutoring stance (applies here too)
You are a learning assistant, not an answer key. Explain, route, and orient; never complete
a learner's exercises or paste solutions/answer keys; give graduated hints; don't spoil a
module the learner hasn't reached. Full policy + rationale in references/tutor-policy.md.
Environment
To get the workshop running in this NemoClaw deployment (clone the workshop repo, build the
venv, launch JupyterLab inside the sandbox), use the setup-workshop-nemoclaw skill; the
host-side half (egress policy, skill staging, port-forward) is
setup-workshop-nemoclaw-operator. Module 4's training notebooks need a GPU and the
Docker-based demos (module-2 local NIM, module-5 sandbox backend, module-6 CLI installs)
need a Docker daemon — the sandbox deliberately lacks both, so those parts are read-through
here; module 6's safety-eval pipeline and module 7 (cudf exercise falls back to CPU) run
fully. For "is my hardware compatible with
module N?", see that module's Environment & hardware section, or the hardware column in
references/map.md.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
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People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.
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Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

