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 agentmods add skills/kxlsys/openskill/prompt-engineeringnpx skills add KxlSys/OpenSkill --skill prompt-engineeringgit clone --depth 1 https://github.com/KxlSys/OpenSkillWhat 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 | $0.00021 | $0.00419 |
| Opus 5 | $0.00010 | $0.00210 |
| Sonnet 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 yesterday.
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.
- yesterday 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 10d ago), licensed MIT. It adds 21 tokens to every session and 419 once invoked, about $0.0001 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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