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 roedyrustam/vibes-plug --skill ai-prompt-engineering-expertgit clone --depth 1 https://github.com/roedyrustam/vibes-plugWrote 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/roedyrustam/vibes-plug/ai-prompt-engineering-expert)<a href="https://agentmods.dev/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert/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/roedyrustam/vibes-plug/ai-prompt-engineering-expert"><img src="https://agentmods.dev/badge/skills/roedyrustam/vibes-plug/ai-prompt-engineering-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00058 | $0.02449 |
| Opus 5.5 | $0.00023 | $0.00980 |
| Sonnet 5.5 | $0.00012 | $0.00490 |
| Haiku 4.5 | $0.00006 | $0.00245 |
Grade A, and why
ai-prompt-engineering-expert 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.
How it starts
The opening of the file, as written. The whole thing — 213 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AI Prompt Engineering & Automated Evals Expert (2026 Edition)
English
Orchestration & Integration
- Foundation Models & Reasoning:
ai-llm-integration-expert,frontier-ai-models-expert,gemini-agent-booster - Output Contracts & Schemas:
structured-output-contracts,spec-driven-development-expert - Security & Red Teaming:
prompt-injection-firewall,autonomous-red-teamer,ai-safety-governance-expert - CI/CD & Regression Evals:
ci-cd-devops-architect,autonomous-tdd-debugger,github-actions-expert - Lifecycle & Governance:
zero-to-prod-orchestrator,session-memory-manager,anti-slop
Description
Production-grade guide covering prompt engineering and automated evaluation (Evals). Teaches how to write, version, defend, benchmark, and regression-test LLM prompts and agent workflows using Promptfoo, DeepEval, and structured JSON schemas.
Trigger Conditions
- Writing or refactoring system prompts for autonomous AI agents.
- Enforcing strict structured output (JSON Schema / Zod).
- Defending against Prompt Injection or jailbreak attacks.
- Setting up automated regression testing and CI/CD quality gates for LLMs.
- Benchmarking RAG output quality (Faithfulness, Relevance, Hallucinations).
Part 1: Prompt Construction & Defense
1. Structured Output (Schema-First)
Never rely on prompt instructions alone to get JSON. Always use native Tool Calling / Structured Outputs with JSON Schema or Zod:
import { z } from 'zod';
export const UserAnalysisSchema = z.object({
sentiment: z.enum(['positive', 'neutral', 'negative']),
confidence: z.number().min(0).max(1),
tags: z.array(z.string()),
});
2. Advanced Prompting Techniques
- Chain-of-Thought (CoT): Direct the model to deliberate before producing final answers. Instruct output inside
<thinking>tags. - Few-Shot Prompting: Provide 2-3 diverse input-output examples illustrating edge cases and desired formatting.
- XML Delimiters: Isolate instructions from untrusted data using explicit boundaries (e.g.
<user_input>,<system_rules>).
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 · +7 lines · +58 tokens per session 0dd8ae27654c
- 5d ago Changed · +2 lines · -58 tokens per session 35847a47bb19
- 8d ago Changed ca1ddba4da4b
- 17d ago Changed 3a9be980a9dd
- 22d ago Changed f527feb75a11
- 23d ago Changed d9b6d94dc23d
- 25d ago Changed · -6 lines · +43 tokens per session d493d83937cf
- 27d ago Changed · +6 lines · -43 tokens per session 418b664a661d
- 1mo ago Changed · +69 lines 9aadf9ac1f03
- 1mo ago First seen · 135 lines · 58 tokens per session scan A a95e51167989
ai-prompt-engineering-expert is a skill published in the GitHub repository roedyrustam/vibes-plug (113 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 2,449 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 2026-08-30.
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