building-with-llms

building-with-llms is a skill for Claude Code, Codex from liqiongyu/lenny_skills_plus. It costs 48 tokens per session (2,268 once invoked), scanned A, original, Apache-2.0.

A guide for building software features that use large language models (LLMs), the AI systems that generate and interpret text. It covers prompts, tools, data, testing, safety, and preparing the feature for production.

In plain words
What is it for?
Use it to design prompts and tool definitions, create output formats and guardrails, build evaluation test sets, plan logging and fallbacks, and prepare an assistant, copilot, or tool-using workflow for launch.
Why use it?
LLM features can give inconsistent answers, expose risks, or become expensive and slow without clear tests and safeguards. This helps define expected behavior and a repeatable way to measure and improve it.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: positional $N argument; mentions Claude Code; mentions Codex.

Good fit Use it to design prompts and tool definitions, create output formats and guardrails, build evaluation test sets, plan logging and fallbacks, and prepare an assistant, copilot, or tool-using workflow for launch.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/liqiongyu/lenny_skills_plus/building-with-llms
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add liqiongyu/lenny_skills_plus --skill building-with-llms
Clone the repo
git clone --depth 1 https://github.com/liqiongyu/lenny_skills_plus

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for building-with-llms

README.md
[![agentmods](https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/building-with-llms/github.svg)](https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/building-with-llms)
Your own site
<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/building-with-llms"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/building-with-llms/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.

agentmods 80×15 button for building-with-llms

Your own site · 80×15
<a href="https://agentmods.dev/skills/liqiongyu/lenny_skills_plus/building-with-llms"><img src="https://agentmods.dev/badge/skills/liqiongyu/lenny_skills_plus/building-with-llms.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 48 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,268 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00048 $0.02268
Opus 5 $0.00024 $0.01134
Sonnet 5 $0.00010 $0.00454
Haiku 4.5 $0.00005 $0.00227

Measured 12d ago against content hash dc668bb6e85e, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

Grade A, and why

building-with-llms 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 12d 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.

skills/building-with-llms/SKILL.md · 137 lines

How it starts

The opening of the file, as written. The whole thing — 137 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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 are doing model training/research, infra architecture, or bespoke model tuning (delegate to ML/eng; this skill assumes API models).
  • You only want “which model/provider should we pick?” (treat as an input; if it dominates, do a separate evaluation doc).
  • You want to design an eval/benchmark framework without building a specific feature (use ai-evals).
  • You need to evaluate a vendor/tool for adoption rather than build an LLM feature (use evaluating-new-technology).
  • You want to quickly prototype or vibe-code an idea without production planning (use vibe-coding).

Inputs

Minimum required

  • Use case + target user + what “good” looks like (success metrics + failure modes)
  • The LLM’s job: generate text, transform data, classify, extract, plan, or take actions via tools
  • Constraints: privacy/compliance, data sensitivity, latency, cost, reliability, supported regions
  • Integration surface: UI/workflow, downstream systems/APIs/tools, and any required output schema

Read the full file on GitHub · 137 lines

Changes

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.

  1. 12d ago First seen · 137 lines · 48 tokens per session scan A dc668bb6e85e

Subscribe to this mod's changes

building-with-llms is a skill published in the GitHub repository liqiongyu/lenny_skills_plus (52 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 48 tokens to every session and 2,268 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

prompt-optimizer

You are the Prompt Optimizer, tasked with algorithmically improving the prompts of other Forgewright skills using the DSPy framework. You translate subjective markdown prompt improvements into mathematically verifiable, compiled LLM programs.

buiphucminhtam/forgewright · 53 tokens

agent-prompt-builder

Stop writing system prompts by hand — let structured identity generate them automatically from beliefs and responsibilities.

live-neon/persona-mcp · 22 tokens

prompt-engineer

Transforms user prompts into optimized prompts using frameworks (RTF, RISEN, Chain of Thought, RODES, Chain of Density, RACE, RISE, STAR, SOAP, CLEAR, GROW).

sagebynature/team-nexus · 45 tokens

ai-engineering-toolkit

6 production-ready AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.

sickn33/agentic-awesome-skills · 47 tokens

guidance

Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework.

davila7/claude-code-templates · 38 tokens

plan

Use when a request needs shaping before any code is written — a rough or vague prompt to sharpen, an ambiguous idea to design, or a clear-enough task to decompose. One chain-starter that amplifies the prompt, designs the approach, and decomposes it into a batched task file, skipping whichever phases the request…

jeremylongshore/tons-of-skills-marketplace · 144 tokens