Borrowing it
Nothing to install: this file belongs to bobmatnyc/mcp-browser. 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-browser/main/.claude/agents/prompt-engineer.mdgit clone --depth 1 https://github.com/bobmatnyc/mcp-browserWrote 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-browser/prompt-engineer)<a href="https://agentmods.dev/agents/bobmatnyc/mcp-browser/prompt-engineer"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-browser/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-browser/prompt-engineer"><img src="https://agentmods.dev/badge/agents/bobmatnyc/mcp-browser/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 | $0.04798 |
| Opus 5.5 | $0.00057 | $0.01919 |
| Sonnet 5.5 | $0.00029 | $0.00960 |
| Haiku 4.5 | $0.00014 | $0.00480 |
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.
This is a copy
80% identical to engineer — 91 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 — 665 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.
- 1mo ago First seen · 665 lines · 143 tokens per session scan A da284ae8130b
prompt-engineer is an agent published in the GitHub repository bobmatnyc/mcp-browser (2 stars, last pushed 10mo ago), licensed MIT. It adds 143 tokens to every session and 4,798 once invoked, about $0.0006 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 80% identical to engineer, differing in 91 lines, and is treated as a copy.
Other agents, from other repositories
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ai-engineer
Senior AI/LLM engineer — designs and hardens LLM features: prompt design with structured/JSON-schema output, RAG (chunking, embeddings, retrieval eval, reranking), eval harnesses (golden sets, regression, LLM-as-judge, hallucination detection), model/provider selection by cost/latency/quality…
prompt-engineer
Petra - The Wordsmith ✍️. Prompt engineering specialist who crafts instructions that work with LLM mechanics. Invoke when creating agent definitions, system prompts, or LLM instructions. Leverages token prediction, attention mechanisms, and pattern reinforcement for maximum effectiveness.