Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/eimis1990/inzonenpx agentmods add skills/eimis1990/inzone/senior-prompt-engineerWrote 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/eimis1990/inzone/senior-prompt-engineer)<a href="https://agentmods.dev/skills/eimis1990/inzone/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/eimis1990/inzone/senior-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/eimis1990/inzone/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/eimis1990/inzone/senior-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 | $0.00081 | $0.02716 |
| Opus 5 | $0.00041 | $0.01358 |
| Sonnet 5 | $0.00016 | $0.00543 |
| Haiku 4.5 | $0.00008 | $0.00272 |
Grade A, and why
senior-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 9d 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
100% identical to senior-prompt-engineer — 8 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 — 360 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Prompt Engineer
Prompt engineering patterns, LLM evaluation frameworks, and agentic system design.
Table of Contents
- Quick Start
- Tools Overview
- Prompt Engineering Workflows
- Reference Documentation
- Common Patterns Quick Reference
Quick Start
# Analyze and optimize a prompt file
python scripts/prompt_optimizer.py prompts/my_prompt.txt --analyze
# Evaluate RAG retrieval quality
python scripts/rag_evaluator.py --contexts contexts.json --questions questions.json
# Visualize agent workflow from definition
python scripts/agent_orchestrator.py agent_config.yaml --visualize
Tools Overview
1. Prompt Optimizer
Analyzes prompts for token efficiency, clarity, and structure. Generates optimized versions.
Input: Prompt text file or string Output: Analysis report with optimization suggestions
Usage:
# Analyze a prompt file
python scripts/prompt_optimizer.py prompt.txt --analyze
# Output:
# Token count: 847
# Estimated cost: $0.0025 (GPT-4)
# Clarity score: 72/100
# Issues found:
# - Ambiguous instruction at line 3
# - Missing output format specification
# - Redundant context (lines 12-15 repeat lines 5-8)
# Suggestions:
# 1. Add explicit output format: "Respond in JSON with keys: ..."
# 2. Remove redundant context to save 89 tokens
# 3. Clarify "analyze" -> "list the top 3 issues with severity ratings"
# Generate optimized version
python scripts/prompt_optimizer.py prompt.txt --optimize --output optimized.txt
# Count tokens for cost estimation
python scripts/prompt_optimizer.py prompt.txt --tokens --model gpt-4
# Extract and manage few-shot examples
python scripts/prompt_optimizer.py prompt.txt --extract-examples --output examples.json
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.
- 9d ago First seen · 360 lines · 81 tokens per session scan A 1f1349ac97f4
senior-prompt-engineer is a skill published in the GitHub repository eimis1990/inzone (5 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 2,716 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to senior-prompt-engineer, differing in 8 lines, and is treated as a copy.
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