llm-application-dev

llm-application-dev is a skill for Claude Code, Codex from plurigrid/asi. It costs 40 tokens per session (1,258 once invoked), scanned A, a copy of llm-application-dev, MIT.

A guide to building applications with large language models, the AI systems that generate and interpret text. It covers prompt design, retrieval-augmented generation, and model API integration.

In plain words
What is it for?
It helps build AI-powered features, chatbots, and automated workflows using structured prompts, examples, retrieved context, and APIs.
Why use it?
It helps developers design reliable AI features instead of treating prompts and model calls as ad hoc code.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

Good fit It helps build AI-powered features, chatbots, and automated workflows using structured prompts, examples, retrieved context, and APIs.

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Install with agentmods
npx agentmods add skills/plurigrid/asi/llm-application-dev
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 plurigrid/asi --skill llm-application-dev
Clone the repo
git clone --depth 1 https://github.com/plurigrid/asi

Made for: Claude Code, Codex.

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README.md
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Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,258 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 92% copy Near-identical to another mod 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 $0.01258
Opus 5.5 $0.00016 $0.00503
Sonnet 5.5 $0.00008 $0.00252
Haiku 4.5 $0.00004 $0.00126

Measured 1mo ago against content hash 48d6c4c65aa2, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, 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 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.

Origin

This is a copy

92% identical to llm-application-dev — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.

ies/music-topos/.agents/skills/llm-application-dev/SKILL.md · 217 lines

How it starts

The opening of the file, as written. The whole thing — 217 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 · 217 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. 1mo ago First seen · 217 lines · 40 tokens per session scan A 48d6c4c65aa2

Subscribe to this mod's changes

llm-application-dev is a skill published in the GitHub repository plurigrid/asi (70 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 1,258 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 92% identical to llm-application-dev, differing in 1 line, and is treated as a copy.