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 vuralserhat86/antigravity-agentic-skills --skill prompt_engineeringgit clone --depth 1 https://github.com/vuralserhat86/antigravity-agentic-skillsWrote 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/vuralserhat86/antigravity-agentic-skills/prompt_engineering)<a href="https://agentmods.dev/skills/vuralserhat86/antigravity-agentic-skills/prompt_engineering"><img src="https://agentmods.dev/badge/skills/vuralserhat86/antigravity-agentic-skills/prompt_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/vuralserhat86/antigravity-agentic-skills/prompt_engineering"><img src="https://agentmods.dev/badge/skills/vuralserhat86/antigravity-agentic-skills/prompt_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 | $0.00000 | $0.10488 |
| Opus 5 | $0.00000 | $0.05244 |
| Sonnet 5 | $0.00000 | $0.02098 |
| Haiku 4.5 | $0.00000 | $0.01049 |
Grade B, and why
prompt_engineering scanned grade B with 1 finding 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 9d 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.
Instruction-override phrasingmediumPrompt injection
Text telling the model to disregard its earlier instructions or safety rules is the shape of a prompt injection, whoever wrote it.
"ignore previous instructions", Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
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 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.
- 9d ago First seen · 1,418 lines · 0 tokens per session scan B e93f2d62630a
prompt_engineering is a skill published in the GitHub repository vuralserhat86/antigravity-agentic-skills (42 stars, last pushed 8mo ago), with no licence file. It costs nothing until one of its globs matches a file; then it loads 10,488 tokens. A static security scan graded it B with 1 finding (instruction-override phrasing). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
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Production-ready patterns for building LLM applications. Covers RAG pipelines, agent architectures, prompt IDEs, and LLMOps monitoring. Use when designing AI applications, implementing RAG, building agents, or setting up LLM observability.
llm-security
Use for authorized security assessment of LLM applications and AI agents, including prompt injection, tool abuse, RAG exposure, memory poisoning, and model supply-chain risks.
llm-ops
LLM Operations -- RAG, embeddings, vector databases, fine-tuning, prompt engineering avancado, custos de LLM, evals de qualidade e arquiteturas de IA para producao.
ai-product
Every product will be AI-powered. The question is whether you'll build it right or ship a demo that falls apart in production.