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 skills add yezannnnn/agentGroup --skill senior-prompt-engineergit clone --depth 1 https://github.com/yezannnnn/agentGroupWrote 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/yezannnnn/agentgroup/senior-prompt-engineer)<a href="https://agentmods.dev/skills/yezannnnn/agentgroup/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/yezannnnn/agentgroup/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/yezannnnn/agentgroup/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/yezannnnn/agentgroup/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 | $0.02706 |
| Opus 5 | $0.00041 | $0.01353 |
| Sonnet 5 | $0.00016 | $0.00541 |
| Haiku 4.5 | $0.00008 | $0.00271 |
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 11d 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:
- senior-prompt-engineer — 100% identical, 8 lines differ
How it starts
The opening of the file, as written. The whole thing — 356 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 ships with it
6 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.
- 11d ago First seen · 356 lines · 81 tokens per session scan A ce3f1ef8aa55
senior-prompt-engineer is a skill published in the GitHub repository yezannnnn/agentGroup (149 stars, last pushed 3mo ago), licensed MIT. It adds 81 tokens to every session and 2,706 once invoked, about $0.0004 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 2026-08-30.
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