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.
npx skills add tmolavi/mcp-agent-skills-hub --skill llm-application-dev-prompt-optimizegit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote 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.
[](https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/llm-application-dev-prompt-optimize)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/llm-application-dev-prompt-optimize"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/llm-application-dev-prompt-optimize/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.
<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/llm-application-dev-prompt-optimize"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/llm-application-dev-prompt-optimize.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00044 | $0.00261 |
| Opus 5.5 | $0.00018 | $0.00104 |
| Sonnet 5.5 | $0.00009 | $0.00052 |
| Haiku 4.5 | $0.00004 | $0.00026 |
Grade A, and why
llm-application-dev-prompt-optimize 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.
This is a copy
100% identical to llm-application-dev-prompt-optimize — 2 lines 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.
What it actually says
Prompt Optimization
You are an expert prompt engineer specializing in crafting effective prompts for LLMs through advanced techniques including constitutional AI, chain-of-thought reasoning, and model-specific optimization.
Use this skill when
- Working on prompt optimization tasks or workflows
- Needing guidance, best practices, or checklists for prompt optimization
Do not use this skill when
- The task is unrelated to prompt optimization
- You need a different domain or tool outside this scope
Context
Transform basic instructions into production-ready prompts. Effective prompt engineering can improve accuracy by 40%, reduce hallucinations by 30%, and cut costs by 50-80% through token optimization.
Requirements
$ARGUMENTS
Instructions
- Clarify goals, constraints, and required inputs.
- Apply relevant best practices and validate outcomes.
- Provide actionable steps and verification.
- If detailed examples are required, open
resources/implementation-playbook.md.
Resources
resources/implementation-playbook.mdfor detailed patterns and examples.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago First seen · 38 lines · 44 tokens per session scan A 6e65f5678039
llm-application-dev-prompt-optimize is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 1mo ago), licensed MIT. It adds 44 tokens to every session and 261 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to llm-application-dev-prompt-optimize, differing in 2 lines, and is treated as a copy.
Other skills, from other repositories
fixing-prompt
Prompt: Prompt Refinement and Optimization.
agent-platform-prompt-management
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.
claude
Expert Anthropic Claude API assistance covering Claude 3.5 Sonnet / Haiku / Opus, tool use, prompt caching, and long-context processing. Use when integrating Anthropic models into LLM applications.
boost-prompt
Interactive prompt refinement workflow: interrogates scope, deliverables, constraints; copies final markdown to clipboard; never writes code. Requires the Joyride extension.
seedance-antislop
Detect and remove hollow AI filler language, empty superlatives, and vague boosters that degrade Seedance 2.0 prompt quality. Use when a prompt feels generic, over-written, or 'AI-sounding', or when generation output looks bland and needs a quality pass.
seedance-camera
Specify camera movement, shot framing, multi-shot sequences, and anti-drift locks for Seedance 2.0. Covers dolly, crane, orbit, push-in, one-take, and storyboard reference methods. Use when writing camera instructions, shooting a scene with a specific angle or movement, or fixing a wandering or locked camera.