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 Jignesh-Ponamwar/skills-mcp --skill llm-prompt-engineeringgit clone --depth 1 https://github.com/Jignesh-Ponamwar/skills-mcpWrote 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/jignesh-ponamwar/skills-mcp/llm-prompt-engineering)<a href="https://agentmods.dev/skills/jignesh-ponamwar/skills-mcp/llm-prompt-engineering"><img src="https://agentmods.dev/badge/skills/jignesh-ponamwar/skills-mcp/llm-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/jignesh-ponamwar/skills-mcp/llm-prompt-engineering"><img src="https://agentmods.dev/badge/skills/jignesh-ponamwar/skills-mcp/llm-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 | $0.00097 | $0.02067 |
| Opus 5 | $0.00048 | $0.01033 |
| Sonnet 5 | $0.00019 | $0.00413 |
| Haiku 4.5 | $0.00010 | $0.00207 |
Grade A, and why
llm-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 11d 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 — 294 lines — stays where its author put it; the contents beside it link to each section on GitHub.
LLM Prompt Engineering Skill
Principle 1: Be Specific and Unambiguous
Vague prompts produce vague outputs. Every ambiguity in a prompt becomes variance in the output.
❌ "Summarize this article."
✅ "Summarize this article in 3 bullet points, each 1-2 sentences. Focus on:
- The main problem being solved
- The proposed solution or finding
- The practical implication for developers
Do not include background context that isn't mentioned in the article."
Principle 2: Assign a Role / Persona
Role assignment primes the model's knowledge domain and response style:
"You are a senior security engineer conducting a code audit. Your role is to:
- Identify vulnerabilities with severity ratings (Critical/High/Medium/Low)
- Explain the attack vector for each finding
- Provide concrete remediation code
- Be precise - only flag real issues, not style preferences"
Principle 3: Structured Prompts with XML Tags (Claude / GPT-4)
XML tags make prompt sections unambiguous and allow easy programmatic parsing:
<system_prompt>
You are a technical writer specializing in API documentation.
<rules>
- Write in second person ("you can", "use the", not "the developer should")
- Show complete code examples with working imports
- Include error handling in every example
- Format: Overview → Parameters table → Example → Common errors
</rules>
</system_prompt>
<user_request>
Document the following Python function:
<function>
{code_here}
</function>
</user_request>
Principle 4: Chain-of-Thought Reasoning
For complex tasks, instruct the model to reason step by step before answering:
"Before giving your final answer, think through this step by step:
1. What information is given?
2. What is being asked?
3. What approach should I take?
4. Work through the solution
5. Verify the answer
Then provide your final answer."
Or simply: "Think step by step." - this alone improves accuracy on reasoning tasks.
Zero-shot CoT:
Q: A bat and a ball cost $1.10 total. The bat costs $1.00 more than the ball. How much does the ball cost?
Let's think step by step.
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.
- 11d ago First seen · 294 lines · 97 tokens per session scan A 7f6340fde0cb
llm-prompt-engineering is a skill published in the GitHub repository Jignesh-Ponamwar/skills-mcp (7 stars, last pushed 3mo ago), licensed Apache-2.0. It adds 97 tokens to every session and 2,067 once invoked, about $0.0005 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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