القائمة
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/davila7/claude-code-templates --skill prompt-engineering-patternsيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| name | prompt-engineering-patterns |
| description | Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability. |
| risk | unknown |
| source | community |
| date_added | 2026-02-27 |
Master advanced prompt engineering techniques to maximize LLM performance, reliability, and controllability.
resources/implementation-playbook.md.from prompt_optimizer import PromptTemplate, FewShotSelector
# Define a structured prompt template
template = PromptTemplate(
system="You are an expert SQL developer. Generate efficient, secure SQL queries.",
instruction="Convert the following natural language query to SQL:\n{query}",
few_shot_examples=True,
output_format="SQL code block with explanatory comments"
)
# Configure few-shot learning
selector = FewShotSelector(
examples_db="sql_examples.jsonl",
selection_strategy="semantic_similarity",
max_examples=3
)
# Generate optimized prompt
prompt = template.render(
query="Find all users who registered in the last 30 days",
examples=selector.select(query="user registration date filter")
)
Start with simple prompts, add complexity only when needed:
Level 1: Direct instruction
Level 2: Add constraints
Level 3: Add reasoning
Level 4: Add examples
[System Context] → [Task Instruction] → [Examples] → [Input Data] → [Output Format]
Build prompts that gracefully handle failures:
# Combine retrieved context with prompt engineering
prompt = f"""Given the following context:
{retrieved_context}
{few_shot_examples}
Question: {user_question}
Provide a detailed answer based solely on the context above. If the context doesn't contain enough information, explicitly state what's missing."""
# Add self-verification step
prompt = f"""{main_task_prompt}
After generating your response, verify it meets these criteria:
1. Answers the question directly
2. Uses only information from provided context
3. Cites specific sources
4. Acknowledges any uncertainty
If verification fails, revise your response."""
Track these KPIs for your prompts:
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