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 KxlSys/OpenSkill --skill prompt-engineeringgit clone --depth 1 https://github.com/KxlSys/OpenSkillWrote 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/kxlsys/openskill/prompt-engineering)<a href="https://agentmods.dev/skills/kxlsys/openskill/prompt-engineering"><img src="https://agentmods.dev/badge/skills/kxlsys/openskill/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/kxlsys/openskill/prompt-engineering"><img src="https://agentmods.dev/badge/skills/kxlsys/openskill/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 | $0.00021 | $0.00419 |
| Opus 5.5 | $0.00008 | $0.00168 |
| Sonnet 5.5 | $0.00004 | $0.00084 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
prompt-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 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.
What it actually says
Prompt Engineering Methodology
Cette compétence définit un cadre pour concevoir, tester et optimiser les prompts destinés aux modèles de langage de grande taille (LLMs).
Étapes de Conception d'un Prompt
-
Définition de l'Objectif & du Rôle :
- Assigner un rôle ou un persona clair et précis au modèle (ex: "Vous êtes un expert en cybersécurité...").
- Définir précisément la tâche attendue.
-
Structuration du Prompt :
- Fournir des instructions claires et distinctes (en utilisant du Markdown, des balises XML comme
<instructions>,<context>, etc.). - Spécifier le format de sortie souhaité (JSON, Markdown, liste à puces, ton formel/informel).
- Fournir des instructions claires et distinctes (en utilisant du Markdown, des balises XML comme
-
Fourniture de Contexte et d'Exemples (Few-Shot Prompting) :
- Si possible, fournir un ou plusieurs exemples de couples entrée/sortie pour guider le modèle vers le comportement souhaité.
-
Décomposition de la Réflexion (Chain of Thought) :
- Inciter le modèle à détailler ses étapes de réflexion avant de donner la réponse finale (ex: "Expliquez votre raisonnement étape par étape...").
-
Gestion des Cas Limites (Fallback & Guardrails) :
- Ajouter des garde-fous pour éviter les hallucinations ou les comportements indésirables (ex: "Si vous ne connaissez pas la réponse, répondez simplement 'Je ne sais pas'").
Rapport de Conception de Prompt
Prompt Initial
Analyse des Résultats & Limitations
Prompt Optimisé (Version Finale)
Résultats Comparatifs
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 · 41 lines · 21 tokens per session scan A 7a959ffe702b
prompt-engineering is a skill published in the GitHub repository KxlSys/OpenSkill (2 stars, last pushed 28d ago), licensed MIT. It adds 21 tokens to every session and 419 once invoked, about $0.0001 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
ai-engineering-toolkit
Overview of six AI engineering workflows: prompt evaluation (8-dimension scoring), context budget planning, RAG pipeline design, agent security audit (65-point checklist), eval harness building, and product sense coaching.
guidance
Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework.
outlines
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library.
prompt-master
Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other…
seedance-prompt-zh
Chinese-language guidance for writing prompts for Seedance 2.0, an AI video generator that can use text, images, video, and audio.
seedance-prompt-en
Write effective prompts for Jimeng Seedance 2.0 multimodal AI video generation. Use when users want to create video prompts using text, images, videos, and audio inputs with the @ reference system. Covers camera movements, effects replication, video extension, editing, music beat-matching, e-commerce ads, short…