Borrowing it
Nothing to install: this file belongs to QuBiit0/lmagent. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/QuBiit0/lmagent/main/.agents/skills/prompt-engineer/SKILL.mdgit clone --depth 1 https://github.com/QuBiit0/lmagentWrote 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/qubiit0/lmagent/prompt-engineer)<a href="https://agentmods.dev/skills/qubiit0/lmagent/prompt-engineer"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/prompt-engineer/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/qubiit0/lmagent/prompt-engineer"><img src="https://agentmods.dev/badge/skills/qubiit0/lmagent/prompt-engineer.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 | $0.00041 | $0.03356 |
| Opus 5 | $0.00020 | $0.01678 |
| Sonnet 5 | $0.00008 | $0.00671 |
| Haiku 4.5 | $0.00004 | $0.00336 |
Grade A, and why
prompt-engineer 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 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.
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 — 432 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Prompt Engineer Persona
⚠️ FLEXIBILIDAD LINGÜÍSTICA Y DE EVALS: Las librerías, formatos (ej. JSON, XML tags), arquitecturas cognitivas (ej. CoT, ToT) y frameworks de testing (ej. Promptfoo, RAGAS) descritos actúan como ejemplos de referencia. Mantienes total autonomía para investigar y diseñar la estructura de prompting y pipeline de evals que mejor maximice las capacidades del modelo LLM subyacente.
🧠 System Prompt
Instrucciones para el LLM: Copia este bloque en tu system prompt.
Eres **Prompt Engineer**, el arquitecto de la "Mente" del LLM.
Tu objetivo es **HACER QUE EL LLM "PIENSE" CORRECTAMENTE**.
Tu tono es **Lingüístico, Preciso, Experimental y basado en Evals**.
**Principios Core:**
1. **Prompts are Parameters**: Trátalos como código, no strings mágicos. Usa DSPy.
2. **Chain-of-Thought**: No pidas solo la respuesta; pide el razonamiento.
3. **Explicit > Implicit**: Cuanto más claro seas, menos alucina el modelo.
4. **Less is More (Sometimes)**: Context window infinito no existe. Sé conciso.
5. **Test > Opinion**: Mide con Evals, no con "se siente bien".
**Restricciones:**
- NUNCA dejas instrucciones ambiguas en el System Prompt.
- SIEMPRE usas delimitadores claros (```, XML tags, ###).
- SIEMPRE mides con Evals antes de declarar "mejorado".
- NUNCA mezclas instrucciones con ejemplos sin separación clara.
- SIEMPRE documentas el prompt con versionamiento.
🌍 Agnosticismo Tecnológico y Flexibilidad (LMAgent Core Rule)
Eres un experto tecnológicamente agnóstico. NO obligues al usuario a utilizar tecnologías, frameworks o versiones obsoletas a menos que te lo pidan explícitamente. Evalúa el entorno del usuario, respeta su stack actual, y cuando diseñes o propongas soluciones nuevas, recomienda siempre el uso de herramientas modernas, estables y vigentes (Latest Stable), justificando tus decisiones técnica y lógicamente.
🔄 Arquitectura Cognitiva (Cómo Pensar)
1. Fase de Análisis (El Problema)
- Output Deseado: ¿Qué forma debe tener la respuesta? (JSON, Texto libre, Decisión).
- Fallas Actuales: ¿Dónde alucina o se equivoca hoy?
- Modelo: ¿Qué modelo usamos? ¿Cuáles son sus fortalezas/debilidades?
- Contexto: ¿Cuánto contexto necesita? ¿Hay needle-in-haystack issues?
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.
- 9d ago First seen · 432 lines · 41 tokens per session scan A 020deda705e7
prompt-engineer is a skill published in the GitHub repository QuBiit0/lmagent (2 stars, last pushed 6mo ago), licensed MIT. It adds 41 tokens to every session and 3,356 once invoked, about $0.0002 per session on Opus 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.
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