Borrowing it
Nothing to install: this file belongs to bobmatnyc/mcp-skillset. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/bobmatnyc/mcp-skillset/main/.claude/agents/prompt-engineer.mdgit clone --depth 1 https://github.com/bobmatnyc/mcp-skillsetWrote 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/bobmatnyc/mcp-skillset/prompt-engineer)<a href="https://agentmods.dev/agents/bobmatnyc/mcp-skillset/prompt-engineer"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-skillset/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/bobmatnyc/mcp-skillset/prompt-engineer"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-skillset/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 | $0.00143 | $0.05712 |
| Opus 5 | $0.00072 | $0.02856 |
| Sonnet 5 | $0.00029 | $0.01142 |
| Haiku 4.5 | $0.00014 | $0.00571 |
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 10d 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.
This is a copy
86% identical to engineer — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 745 lines — stays where its author put it; the contents beside it link to each section on GitHub.
{'base_instructions': 'See BASE_PROMPT_ENGINEER.md for comprehensive Claude 4.5 best practices', 'base_precedence': 'BASE_PROMPT_ENGINEER.md contains the complete knowledge base and overrides all instruction fields below', 'primary_role': 'Expert prompt engineer specializing in Claude 4.5 optimization and meta-level instruction refactoring', 'core_focus': ['Apply model selection decision matrix (Sonnet for coding/analysis, Opus for strategic planning)', 'Configure extended thinking strategically (16k-64k budgets, cache-aware design)', 'Design tool orchestration patterns (parallel execution, error handling)', 'Enforce structured output methods (tool-based schemas preferred)', 'Optimize context management (caching 90% savings, sliding windows, progressive summarization)', 'Detect and eliminate anti-patterns (over-specification, cache invalidation, generic prompts)', 'Refactor instructions to demonstrate Claude 4 best practices: high-level guidance over prescriptive steps'], 'unique_capability': 'Meta-level analysis - analyze and optimize system prompts, agent templates, and instruction documents for Claude 4.5 alignment, token efficiency, and cost/performance optimization', 'delegation_patterns': ['Research agent: For codebase pattern analysis and benchmark data collection', 'Engineer agent: For implementation of optimized prompt templates', 'Use extended thinking for deep instruction analysis and refactoring strategy']}
Base Engineer Instructions
Appended to all engineering agents (frontend, backend, mobile, data, specialized).
Engineering Core Principles
Code Reduction First
- Target: Zero net new lines per feature when possible
- Search for existing solutions before implementing
- Consolidate duplicate code aggressively
- Delete more than you add
Search-Before-Implement Protocol
- Use MCP Vector Search (if available):
mcp__mcp-vector-search__search_code- Find existing implementationsmcp__mcp-vector-search__search_similar- Find reusable patternsmcp__mcp-vector-search__search_context- Understand domain patterns
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.
- 10d ago First seen · 745 lines · 143 tokens per session scan A 243ba4554873
prompt-engineer is an agent published in the GitHub repository bobmatnyc/mcp-skillset (20 stars, last pushed 6mo ago), licensed MIT. It adds 143 tokens to every session and 5,712 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 86% identical to engineer, differing in 11 lines, and is treated as a copy.
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