llm-application-dev

llm-application-dev is a skill for Claude Code, Codex from MoizIbnYousaf/Ai-Agent-Skills. It costs 40 tokens per session (1,267 once invoked), scanned A, original, MIT.

A guide to building applications that use large language models, including prompt design, retrieval of relevant information, and model integration.

In plain words
What is it for?
Designing prompts, adding examples and rules, building chatbots or AI features, retrieving supporting information, and calling language-model APIs.
Why use it?
It helps make model responses more consistent and connects the model to application code and outside context.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 1.1krepo 23d ago A scan Socket: passSnyk: passSkillSpector: pass 40 tokens original MIT

Good fit Designing prompts, adding examples and rules, building chatbots or AI features, retrieving supporting information, and calling language-model APIs.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/moizibnyousaf/ai-agent-skills/llm-application-dev
About the project

AI Agent Skills is a curated library and package manager for installing, organizing, and creating skills for compatible AI coding agents. It is for developers who want to manage reusable agent instructions through a command-line or terminal interface. The catalogue skills and agents are examples of the kind of add-ons it helps manage.

MoizIbnYousaf/Ai-Agent-Skills · 1,134 stars · on GitHub · npmjs.com

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 MoizIbnYousaf/Ai-Agent-Skills --skill llm-application-dev
Clone the repo
git clone --depth 1 https://github.com/MoizIbnYousaf/Ai-Agent-Skills

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 llm-application-dev

README.md
[![agentmods](https://agentmods.dev/badge/skills/moizibnyousaf/ai-agent-skills/llm-application-dev/github.svg)](https://agentmods.dev/skills/moizibnyousaf/ai-agent-skills/llm-application-dev)
Your own site
<a href="https://agentmods.dev/skills/moizibnyousaf/ai-agent-skills/llm-application-dev"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/ai-agent-skills/llm-application-dev/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 llm-application-dev

Your own site · 80×15
<a href="https://agentmods.dev/skills/moizibnyousaf/ai-agent-skills/llm-application-dev"><img src="https://agentmods.dev/badge/skills/moizibnyousaf/ai-agent-skills/llm-application-dev.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,267 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. Third-party audits
  • Socket pass 3 Apr 2026
  • Snyk pass 3 Apr 2026
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 $0.00040 $0.01267
Opus 5 $0.00020 $0.00633
Sonnet 5 $0.00008 $0.00253
Haiku 4.5 $0.00004 $0.00127

Measured 10d ago against content hash a00e729ae907, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

llm-application-dev 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 10d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

skills/llm-application-dev/SKILL.md · 218 lines

How it starts

The opening of the file, as written. The whole thing — 218 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LLM Application Development

Prompt Engineering

Structured Prompts

const systemPrompt = `You are a helpful assistant that answers questions about our product.

RULES:
- Only answer questions about our product
- If you don't know, say "I don't know"
- Keep responses concise (under 100 words)
- Never make up information

CONTEXT:
{context}`;

const userPrompt = `Question: {question}`;

Few-Shot Examples

const prompt = `Classify the sentiment of customer feedback.

Examples:
Input: "Love this product!"
Output: positive

Input: "Worst purchase ever"
Output: negative

Input: "It works fine"
Output: neutral

Input: "${customerFeedback}"
Output:`;

Chain of Thought

const prompt = `Solve this step by step:

Question: ${question}

Let's think through this:
1. First, identify the key information
2. Then, determine the approach
3. Finally, calculate the answer

Step-by-step solution:`;

API Integration

OpenAI Pattern

import OpenAI from 'openai';

const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });

async function chat(messages: Message[]): Promise<string> {
  const response = await openai.chat.completions.create({
    model: 'gpt-4',
    messages,
    temperature: 0.7,
    max_tokens: 500,
  });

  return response.choices[0].message.content ?? '';
}

Anthropic Pattern

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({ apiKey: process.env.ANTHROPIC_API_KEY });

async function chat(prompt: string): Promise<string> {
  const response = await anthropic.messages.create({
    model: 'claude-3-opus-20240229',
    max_tokens: 1024,
    messages: [{ role: 'user', content: prompt }],
  });

  return response.content[0].type === 'text'
    ? response.content[0].text
    : '';
}

Streaming Responses

async function* streamChat(prompt: string) {
  const stream = await openai.chat.completions.create({
    model: 'gpt-4',
    messages: [{ role: 'user', content: prompt }],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content;
    if (content) yield content;
  }
}

Read the full file on GitHub · 218 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. 10d ago First seen · 218 lines · 40 tokens per session scan A a00e729ae907

Subscribe to this mod's changes

llm-application-dev is a skill published in the GitHub repository MoizIbnYousaf/Ai-Agent-Skills (1,134 stars, last pushed 23d ago), licensed MIT. It adds 40 tokens to every session and 1,267 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

sqlite-vec-skilld

ALWAYS use when writing code importing "sqlite-vec". Consult for debugging, best practices, or modifying sqlite-vec, sqlite vec.

skilld-dev/skilld · 35 tokens

audit-langfuse-llm

Run a PDCA quality audit on LLM/AI features: traces, prompts, costs, evals, grounding, hallucination. Use for "audit LLM quality", "check Langfuse", "audit prompts", "check AI quality", "audit AI costs", "check traces". Jailbreak/OWASP LLM → audit-llm-security. Token caps → plan-llm-cost-guardrails.

kensaurus/cursor-kenji · 93 tokens

ai-automation

Workflow automation skills using AI. Build chatbots, automate repetitive tasks, integrate LLMs into pipelines, design intent-based assistants. Triggers on: chatbot, automation, workflow, AI agent, RAG, LLM integration, intent recognition, conversation design.

fatihkan/badi · 0 tokens

prompt-engineer

Transform rough prompts/ideas into production-ready LLM prompts. Use when crafting, refining, or optimizing prompts for any AI model (Codex, GPT, Llama, etc.) with advanced techniques like CoT, constitutional AI, RAG optimization.

opencue/cuecards · 54 tokens

ai-expertise-engine

Comprehensive AI/ML expertise covering prompt engineering, LLM architecture, AI agent design, RAG systems, fine-tuning, AI safety, and cutting-edge AI research for building and leveraging AI systems.

onfire7777/universal-ai-skills-library · 46 tokens

audit-llm-security

Read-only OWASP LLM Top 10 audit of app-facing AI: prompt injection, data leakage, unsafe output/agency, RAG risks, misinformation, and unbounded spend. Use when "audit LLM security", "prompt injection", "jailbreak my chatbot", or "is my AI safe?". General app security → audit-security.

kensaurus/cursor-kenji · 76 tokens