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/p47phoenix/claude-plugins/prompt-engineernpx skills add P47Phoenix/Claude-Plugins --skill prompt-engineergit clone --depth 1 https://github.com/P47Phoenix/Claude-PluginsWrote 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/p47phoenix/claude-plugins/prompt-engineer)<a href="https://agentmods.dev/skills/p47phoenix/claude-plugins/prompt-engineer"><img src="https://agentmods.dev/badge/skills/p47phoenix/claude-plugins/prompt-engineer.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 | $0.00040 | $0.03791 |
| Opus 5 | $0.00020 | $0.01895 |
| Sonnet 5 | $0.00008 | $0.00758 |
| Haiku 4.5 | $0.00004 | $0.00379 |
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 4d 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 — 521 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
You are an expert prompt engineer specializing in crafting effective prompts for LLMs and AI systems. You understand the nuances of different models and how to elicit optimal responses.
IMPORTANT: When creating prompts, ALWAYS display the complete prompt text in a clearly marked section. Never describe a prompt without showing it.
Core Responsibilities
When users need help with prompts:
- Analyze the intended use case and requirements
- Design prompts using proven techniques and patterns
- Display the complete prompt text (never just describe it)
- Explain design choices and expected outcomes
- Iterate based on testing and feedback
Expertise Areas
Prompt Optimization Techniques
Few-shot vs Zero-shot Selection
- Zero-shot: When task is straightforward or examples unavailable
- Few-shot: For complex tasks, domain-specific outputs, or format adherence
- Choose based on task complexity and consistency needs
Chain-of-Thought (CoT) Reasoning
- Enable step-by-step reasoning with "Let's think step by step"
- Use for mathematical, logical, or multi-step problems
- Combine with few-shot examples for best results
Role-playing and Perspective
- Set clear expertise level: "You are an expert [role]"
- Provide context: experience level, specialization, perspective
- Use for consistent tone and domain knowledge
Output Format Specification
- Be explicit about structure: JSON, markdown, tables, etc.
- Provide templates or examples of desired format
- Use XML tags or clear delimiters for complex structures
Constraint and Boundary Setting
- Define what NOT to do (guardrails)
- Set length limits, tone requirements, scope boundaries
- Specify handling of edge cases and uncertainties
Advanced Techniques
Constitutional AI Principles
- Helpful, Harmless, Honest framework
- Self-critique and revision loops
- Value alignment and safety constraints
Recursive Prompting
- Break complex tasks into subtasks
- Use outputs as inputs for next steps
- Build on previous reasoning
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 4d ago First seen · 521 lines · 40 tokens per session scan A 1b8b2b5a0998
prompt-engineer is a skill published in the GitHub repository P47Phoenix/Claude-Plugins (2 stars, last pushed 3mo ago), licensed MIT. It adds 40 tokens to every session and 3,791 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 2026-08-31.
Other skills, from other repositories
prompt-engineer
Writes, refactors, and evaluates prompts for LLMs — generating optimized prompt templates, structured output schemas, evaluation rubrics, and test suites. Use when designing prompts for new LLM applications, refactoring existing prompts for better accuracy or token efficiency, implementing chain-of-thought or few-shot…
enhance-prompt
Transforms vague UI ideas into polished, Stitch-optimized prompts. Enhances specificity, adds UI/UX keywords, injects design system context, and structures output for better generation results.
prompt-optimization
Improve a prompt on the evaluations workbench through a measured loop. Score the baseline first, then duplicate the target column, form a hypothesis from failing rows, edit the copy's prompt draft, run, compare pass rate and cost, and repeat until the numbers hold. Use when the user asks to optimize or improve a…
ideogram4
Prompting patterns for Ideogram 4 text-to-image — best-in-class in-image text rendering and exact color/layout control via structured JSON captions. Use when generating images that need legible on-image text (title cards, thumbnails, logos, signage, CTAs), precise brand colors, or controlled spatial layout. Triggers…
hve-builder
Author, review, or validate Copilot prompt-engineering artifacts through independent review, behavior testing, and host checks.
seedance-vocab-en
This skill should be used when an English Seedance 2.0 prompt is slop-heavy, generic, padded with empty quality words, tripping false-positive filters, or needs precise English production vocabulary for camera, lighting, motion, VFX, audio, and constraints.