building-with-llms
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.
npx skills add majiayu000/claude-skill-registry --skill building-with-llms --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Building with LLMs ## Scope **Covers** - Building and shipping LLM-powered features/apps (assistant, copilot, light agent workflows) - Prompt + tool contract design (instructions, schemas, examples, guardrails) - Data quality + evaluation (test sets, rubrics, red teaming, iteration loop) - Production readiness (latency/cost budgets, logging, fallbacks, safety/security checks) - Using coding agents (Codex/Claude Code) to accelerate engineering safely **When to use** - “Turn this LLM feature idea into a build plan with prompts, evals, and launch checks.” - “We need a system prompt + tool definitions + output schema for our LLM workflow.” - “Our LLM is flaky—design an eval plan and iteration loop to stabilize quality.” - “Design a RAG/tool-using agent approach with safety and monitoring.” - “We want to use an AI coding agent to implement this—set constraints and review gates.” **When NOT to use** - You need product/portfolio strategy and positioning (use `ai-product-strategy`). - You need a full PRD/spec set for cross-functional alignment (use `writing-prds` / `writing-specs-designs`). - You need primary user research (use `conducting-user-interviews` / `usability-testing`). - You a
- Scope
- Inputs
- Outputs (deliverables)
- Workflow (8 steps)
- 1) Frame the job, boundary, and “good”
- 2) Choose the minimum viable autonomy pattern
- 3) Design the context strategy (prompting → RAG → tools)
- 4) Draft the prompt + tool contract (make the system legible)
- 5) Build the eval set + rubric (debug like software)
- 6) Prototype a thin slice, using coding agents safely
- 7) Production readiness: budgets, monitoring, and failure handling
- 8) Quality gate + finalize
- Quality gate (required)
- Examples
What does the building-with-llms skill do?
Produce an LLM Build Pack (prompt+tool contract, data/eval plan, architecture+safety, launch checklist). Use for building with LLMs, GPT/Claude apps, prompt engineering, RAG, and tool-using agents.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill building-with-llms --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.