Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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/foryourhealth111-pixel/Vibe-Skillsnpx agentmods add skills/foryourhealth111-pixel/vibe-skills/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/foryourhealth111-pixel/vibe-skills/senior-prompt-engineer)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/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/foryourhealth111-pixel/vibe-skills/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/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 | $0.00089 | $0.01216 |
| Opus 5.5 | $0.00036 | $0.00486 |
| Sonnet 5.5 | $0.00018 | $0.00243 |
| Haiku 4.5 | $0.00009 | $0.00122 |
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 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
86% identical to senior-computer-vision — 32 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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Prompt Engineer
World-class senior prompt engineer skill for production-grade AI/ML/Data systems.
Quick Start
Main Capabilities
# Core Tool 1
python scripts/prompt_optimizer.py --input data/ --output results/
# Core Tool 2
python scripts/rag_evaluator.py --target project/ --analyze
# Core Tool 3
python scripts/agent_orchestrator.py --config config.yaml --deploy
Core Expertise
This skill covers world-class capabilities in:
- Advanced production patterns and architectures
- Scalable system design and implementation
- Performance optimization at scale
- MLOps and DataOps best practices
- Real-time processing and inference
- Distributed computing frameworks
- Model deployment and monitoring
- Security and compliance
- Cost optimization
- Team leadership and mentoring
Tech Stack
Languages: Python, SQL, R, Scala, Go ML Frameworks: PyTorch, TensorFlow, Scikit-learn, XGBoost Data Tools: Spark, Airflow, dbt, Kafka, Databricks LLM Frameworks: LangChain, LlamaIndex, DSPy Deployment: Docker, Kubernetes, AWS/GCP/Azure Monitoring: MLflow, Weights & Biases, Prometheus Databases: PostgreSQL, BigQuery, Snowflake, Pinecone
Reference Documentation
1. Prompt Engineering Patterns
Comprehensive guide available in references/prompt_engineering_patterns.md covering:
- Advanced patterns and best practices
- Production implementation strategies
- Performance optimization techniques
- Scalability considerations
- Security and compliance
- Real-world case studies
2. Llm Evaluation Frameworks
Complete workflow documentation in references/llm_evaluation_frameworks.md including:
- Step-by-step processes
- Architecture design patterns
- Tool integration guides
- Performance tuning strategies
- Troubleshooting procedures
3. Agentic System Design
Technical reference guide in references/agentic_system_design.md with:
- System design principles
- Implementation examples
- Configuration best practices
- Deployment strategies
- Monitoring and observability
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.
- 1mo ago First seen · 227 lines · 89 tokens per session scan A 504c1ee82323
senior-prompt-engineer is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,772 stars, last pushed yesterday), licensed Apache-2.0. It adds 89 tokens to every session and 1,216 once invoked, about $0.0004 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to senior-computer-vision, differing in 32 lines, and is treated as a copy.
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