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 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/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/versoxbt/claude-initial-setup/prompt-engineering"><img src="https://agentmods.dev/badge/skills/versoxbt/claude-initial-setup/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 | $0.00060 | $0.01704 |
| Opus 5.5 | $0.00024 | $0.00682 |
| Sonnet 5.5 | $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 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.
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.
- 1mo 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 5mo ago), licensed MIT. It adds 60 tokens to every session and 1,704 once invoked, about $0.0002 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.
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