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/versoxbt/claude-initial-setup/prompt-engineeringnpx skills add VersoXBT/claude-initial-setup --skill prompt-engineeringgit clone --depth 1 https://github.com/VersoXBT/claude-initial-setupWrote 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/versoxbt/claude-initial-setup/prompt-engineering)<a href="https://agentmods.dev/skills/versoxbt/claude-initial-setup/prompt-engineering"><img src="https://agentmods.dev/badge/skills/versoxbt/claude-initial-setup/prompt-engineering.svg" alt="Measured on agentmods" 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 | $0.00060 | $0.01704 |
| Opus 5 | $0.00030 | $0.00852 |
| Sonnet 5 | $0.00012 | $0.00341 |
| Haiku 4.5 | $0.00006 | $0.00170 |
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 5d 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 — 214 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
Patterns and techniques for crafting effective Claude prompts. Covers system prompts, few-shot learning, chain-of-thought reasoning, structured output, and prefilling.
When to Use
- User is writing or optimizing prompts for Claude
- User needs structured JSON output from Claude
- User is designing system prompts or instructions
- User wants chain-of-thought reasoning or few-shot examples
- User is building prompt templates for reuse
Core Patterns
System Prompts
System prompts set Claude's persona, constraints, and output format. Place stable instructions here; they are cached separately and can use prompt caching.
message = client.messages.create(
model="claude-sonnet-4-6-20250514",
max_tokens=1024,
system="""You are a senior code reviewer. Follow these rules:
1. Focus on bugs, security issues, and performance problems.
2. Rate severity as CRITICAL, HIGH, MEDIUM, or LOW.
3. Provide a fix for each issue found.
4. If the code is clean, say "No issues found." and nothing else.""",
messages=[{"role": "user", "content": f"Review this code:\n```\n{code}\n```"}]
)
Few-Shot Examples
Provide 2-4 input/output examples to demonstrate the exact format and reasoning you expect. Use the messages array with alternating user/assistant turns.
messages = [
{"role": "user", "content": "Classify: 'My order never arrived and nobody responds to emails'"},
{"role": "assistant", "content": '{"category": "shipping", "sentiment": "negative", "priority": "high"}'},
{"role": "user", "content": "Classify: 'Love the new feature update, works great!'"},
{"role": "assistant", "content": '{"category": "feedback", "sentiment": "positive", "priority": "low"}'},
{"role": "user", "content": f"Classify: '{user_input}'"}
]
message = client.messages.create(
model="claude-sonnet-4-6-20250514",
max_tokens=256,
messages=messages
)
Chain-of-Thought Reasoning
Ask Claude to reason step-by-step before giving a final answer. Use extended thinking for complex problems that benefit from deep reasoning.
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.
- 5d ago First seen · 214 lines · 60 tokens per session scan A 8d4553b0e801
prompt-engineering is a skill published in the GitHub repository VersoXBT/claude-initial-setup (4 stars, last pushed 3mo ago), licensed MIT. It adds 60 tokens to every session and 1,704 once invoked, about $0.0003 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.
Other skills, from other repositories
harness-optimizer
Use this skill when tuning AI agent configurations for performance, cost, and reliability. Covers token optimization, context window management, model selection, prompt engineering for agents, and ...
prompt_engineering
Expert guide on prompt engineering for 2024-2025 models (GPT-4o, Claude 3.5, o1, o3, Gemini 2.0). Covers reasoning models, delimiters, structured output, and context engineering.
prompt-writing
Create, refine, and optimize high-quality YAML prompts for AI assistants. Use when working with prompt templates, system prompts, agent prompts, or any prompt engineering tasks. Provides structure guidelines, template patterns, and quality standards for YAML-based prompts.
chain-of-thought-prompts
Chain-of-thought and step-by-step reasoning prompts for complex problem solving.
few-shot-example-gen
Few-shot example generation and optimization for improved LLM performance.
llm-classifier
LLM-based zero-shot and few-shot classification for flexible intent detection.