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 fabiandistler/agents --skill llm-application-engineeringgit clone --depth 1 https://github.com/fabiandistler/agentsWrote 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/fabiandistler/agents/llm-application-engineering)<a href="https://agentmods.dev/skills/fabiandistler/agents/llm-application-engineering"><img src="https://agentmods.dev/badge/skills/fabiandistler/agents/llm-application-engineering/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/fabiandistler/agents/llm-application-engineering"><img src="https://agentmods.dev/badge/skills/fabiandistler/agents/llm-application-engineering.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 | $0.00059 | $0.03366 |
| Opus 5.5 | $0.00024 | $0.01346 |
| Sonnet 5.5 | $0.00012 | $0.00673 |
| Haiku 4.5 | $0.00006 | $0.00337 |
Grade A, and why
llm-application-engineering 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 4d 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
What ships with it
4 files 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.
- 4d ago Changed · +14 lines f1d6b95d1568
- 6d ago Changed · -25 lines 660161d98124
- 8d ago Changed · +14 tokens per session 1735571d7452
- 10d ago Changed e342eb2e0125
- 1mo ago Changed · +2 lines · -1 tokens per session 58f28efff59c
- 1mo ago Changed · +1 lines 8eb9c5075aab
- 2mo ago First seen · 253 lines · 46 tokens per session scan A 858026992ee5
llm-application-engineering is a skill published in the GitHub repository fabiandistler/agents (2 stars, last pushed yesterday), with no licence file. It adds 59 tokens to every session and 3,366 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
scale-to-millions
Use this skill whenever the user is designing, reviewing, debugging, or answering interview questions about system/software architecture and scalability — e.g. "scale my app", "how do I handle more traffic", "design a URL shortener / news feed / chat system", "my API is slow under load", "should I shard my database"…
docker-model
Use this skill when running local AI models with Docker Model Runner — the docker model CLI — e.g. "run an LLM locally with Docker", "pull a model from the ai/ namespace", "connect my app to a local model", "use a local model as backend for the Drupal AI module", or when wiring the models: top-level element into a…
data-integrity-post-normalize
Apply a composable pipeline of normalization operations to a string. Trim whitespace, fix encoding, normalize unicode, strip HTML, and more — in a single call.
math-research-activator
A routing guide for mathematical work in AI and cryptography. It helps choose the right concepts and checks for architecture design, theory, mathematical transfers, definitions, constructions, proofs of security, and protocol reviews.
speckit.ai-engineer
AI & LLM Systems Architect - Design RAG pipelines, vector search, semantic caching, evals, and tool-calling guardrails.
ai-ethics-review
Conduct a structured ethical review of an AI or ML feature, model, or product. Use when preparing to deploy an AI system, assessing algorithmic risk, auditing a model for bias, or producing a responsible AI impact assessment. Produces a structured ethics review covering fairness, transparency, privacy, safety…