llm-application-dev

Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.

Skill for Claude CodeCodex

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.

agentmods
npx agentmods add skills/plurigrid/asi/llm-application-dev
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.

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. Scan, not verified.
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 $0.00040 $0.01258
Opus 5 $0.00020 $0.00629
Sonnet 5 $0.00008 $0.00252
Haiku 4.5 $0.00004 $0.00126

Measured today against content hash 48d6c4c65aa2, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, 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 today.

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. today 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 (61 stars, last pushed 1mo ago), licensed MIT. It adds 40 tokens to every session and 1,258 once invoked, about $0.0002 per session on Opus 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.

Related

Other skills, from other repositories

general-video

Author or edit a custom HyperFrames composition when no specialized workflow fits, or when BRIEF.md sets flow: companion. Use for longer or multi-scene pieces, brand and sizzle reels, montages, static loops, static title cards, footage remixes, and freeform builds. Use motion-graphics instead for a short unnarrated…

heygen-com/hyperframes · 92 tokens

html-ppt-hermes-cyber-terminal

OpenDesign + BYOK: choosing and wiring your own model, hands-on — cost, quality, and the routing decision. Built as a decision-grade AI literacy deck for engineers, IT, applied-AI teams.

nexu-io/open-design · 53 tokens

html-ppt-taste-brutalist

16:9 HTML deck in tactical-telemetry / CRT-terminal taste. Deactivated-CRT charcoal slides, white-phosphor monospace, hazard-red accent, scanline overlay, ASCII syntax, density over decoration. Distilled from Leonxlnx/taste-skill brutalist-skill (Tactical Telemetry mode).

nexu-io/open-design · 78 tokens

diagnostic-stem-delivery

Audio production with diagnostic analysis, timecode parsing from documents, and verified export workflow.

HKUDS/OpenSpace · 23 tokens

baoyu-youtube-transcript

Downloads YouTube video transcripts/subtitles and cover images by URL or video ID. Supports multiple languages, translation, chapters, and speaker identification. Caches raw data for fast re-formatting. Use when user asks to "get YouTube transcript", "download subtitles", "get captions", "YouTube字幕", "YouTube封面"…

JimLiu/baoyu-skills · 107 tokens

resolve-rough-cut

Assembling a short-form social rough cut from raw behind-the-scenes or vlog footage in the DaVinci Resolve MCP. Apply when asked for a rough cut, first cut, assembly, or "make me a timeline" from a folder of footage — day-in-the-life, BTS, process, or product-shoot material destined for Reels, TikTok, or Shorts.…

samuelgursky/davinci-resolve-mcp · 97 tokens