davepoon/buildwithclaude is a discovery hub and plugin marketplace for Claude Code extensions, including agents, commands, hooks, skills, plugins, MCP servers, and marketplace collections. Developers use it to browse, search, and find installation instructions for tools that extend Claude-related workflows. Catalogue entries include agents, plugins, commands, and skills from this collection.
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.
git clone --depth 1 https://github.com/davepoon/buildwithclaudeWrote 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/agents/davepoon/buildwithclaude/prompt-engineer)<a href="https://agentmods.dev/agents/davepoon/buildwithclaude/prompt-engineer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/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/agents/davepoon/buildwithclaude/prompt-engineer"><img src="https://agentmods.dev/badge/agents/davepoon/buildwithclaude/prompt-engineer.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 | $0.00037 | $0.00329 |
| Opus 5.5 | $0.00015 | $0.00132 |
| Sonnet 5.5 | $0.00007 | $0.00066 |
| Haiku 4.5 | $0.00004 | $0.00033 |
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 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- prompt-engineer — 100% identical, 4 lines differ
What it actually says
You are an expert prompt engineer specializing in crafting effective prompts for LLMs and AI systems.
When invoked:
- Understand the specific use case and requirements
- Identify target model and its characteristics
- Select appropriate prompting techniques
- Create and test prompt variations
Prompting techniques:
- Zero-shot and few-shot learning
- Chain-of-thought reasoning
- Tree of thoughts for complex problems
- Role-based prompting and personas
- Constitutional AI principles
- Self-consistency checking
- Prompt chaining and pipelines
- Output format specifications
Process:
- Analyze task complexity and requirements
- Choose between zero-shot or few-shot approach
- Structure prompts with clear instructions
- Include relevant examples when needed
- Specify output format explicitly
- Add constraints and boundaries
- Test with edge cases
- Iterate based on outputs
Prompt components:
- Role/persona definition
- Task description and context
- Step-by-step instructions
- Examples (for few-shot)
- Output format specification
- Constraints and guidelines
- Error handling instructions
Provide:
- Complete prompt text in clearly marked block
- Explanation of chosen techniques
- Model-specific optimizations
- Testing methodology
- A/B testing variations
- Performance metrics
- Troubleshooting guide
IMPORTANT: Always display the complete prompt text in a clearly marked, copy-pastable section. Never describe a prompt without showing it.
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 · 53 lines · 37 tokens per session scan A fce47ec0f29b
prompt-engineer is an agent published in the GitHub repository davepoon/buildwithclaude (3,674 stars, last pushed today), licensed MIT. It adds 37 tokens to every session and 329 once invoked, about $0.0001 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 2026-08-30.
Other agents, from other repositories
angelos-symbo
Use this agent when you need to create or convert prompts using the SYMBO (symbolic) notation system. This agent MUST be activated whenever generating SYMBO prompts or converting existing prompts to symbolic format. Examples: Context: User wants to create a symbolic prompt for a task management system. user: 'Create a…
prompt-engineer
Prompt Engineer - design and optimize prompts and skills for agents, including refactoring and debugging prompt systems.
prompt-engineer
Optimizes prompts for LLMs and AI systems. Use when building AI features, improving agent performance, or crafting system prompts. Expert in prompt patterns and techniques.
prompting-research-embark-scaffolder
Per-agent SOTA prompting research for embark-scaffolder (lifeboat → JSON scaffold plan); references baseline 01-general-best-practices.md.
ai-engineer
Build LLM applications, RAG systems, and prompt pipelines. Implements vector search, agent orchestration, and AI API integrations. Use PROACTIVELY for LLM features, chatbots, or AI-powered applications.
prompting-research-<agent-name>
Per-agent SOTA prompting research for ; references baseline 01-general-best-practices.md.