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 curiositech/some_claude_skills --skill prompt-engineergit clone --depth 1 https://github.com/curiositech/some_claude_skillsWrote 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/curiositech/some_claude_skills/prompt-engineer)<a href="https://agentmods.dev/skills/curiositech/some_claude_skills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/prompt-engineer/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/curiositech/some_claude_skills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/curiositech/some_claude_skills/prompt-engineer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00040 | $0.01353 |
| Opus 5 | $0.00020 | $0.00677 |
| Sonnet 5 | $0.00008 | $0.00271 |
| Haiku 4.5 | $0.00004 | $0.00135 |
Grade A, and why
prompt-engineer 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 8d 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 — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
Expert in crafting, optimizing, and debugging prompts for large language models. Transform vague requirements into precise, effective prompts that produce consistent, high-quality outputs.
Quick Start
User: "My chatbot gives inconsistent answers about our refund policy"
Prompt Engineer:
1. Analyze current prompt structure
2. Identify ambiguity and edge cases
3. Apply constraint engineering
4. Add few-shot examples
5. Test with adversarial inputs
6. Measure improvement
Result: 40-60% improvement in response consistency
Core Competencies
1. Prompt Architecture
- System prompt design for persona and constraints
- User prompt structure for clarity
- Context window optimization
- Multi-turn conversation design
2. Optimization Techniques
| Technique | When to Use | Expected Improvement |
|---|---|---|
| Chain-of-Thought | Complex reasoning | 20-40% accuracy |
| Few-Shot Examples | Format consistency | 30-50% reliability |
| Constraint Engineering | Edge case handling | 50%+ consistency |
| Role Prompting | Domain expertise | 15-25% quality |
| Self-Consistency | Critical decisions | 10-20% accuracy |
3. Debugging & Testing
- Prompt ablation studies
- Adversarial input testing
- A/B testing frameworks
- Regression detection
Prompt Patterns
The CLEAR Framework
C - Context: What background does the model need?
L - Limits: What constraints apply?
E - Examples: What does good output look like?
A - Action: What specific task to perform?
R - Review: How to verify correctness?
System Prompt Template
You are [ROLE] with expertise in [DOMAIN].
## Your Task
[CLEAR, SPECIFIC INSTRUCTION]
## Constraints
- [CONSTRAINT 1]
- [CONSTRAINT 2]
## Output Format
[EXACT FORMAT SPECIFICATION]
## Examples
Input: [EXAMPLE INPUT]
Output: [EXAMPLE OUTPUT]
Chain-of-Thought Pattern
Think through this step-by-step:
1. First, identify [ASPECT 1]
2. Then, analyze [ASPECT 2]
3. Consider [EDGE CASES]
4. Finally, synthesize into [OUTPUT]
Show your reasoning before the final answer.
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.
- 8d ago First seen · 197 lines · 40 tokens per session scan A 3fcc77acca15
prompt-engineer is a skill published in the GitHub repository curiositech/some_claude_skills (221 stars, last pushed 5d ago), licensed MIT. It adds 40 tokens to every session and 1,353 once invoked, about $0.0002 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 2026-09-03.
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