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 Pixel-Process-UG/superkit-agents --skill senior-prompt-engineergit clone --depth 1 https://github.com/Pixel-Process-UG/superkit-agentsWrote 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/pixel-process-ug/superkit-agents/senior-prompt-engineer)<a href="https://agentmods.dev/skills/pixel-process-ug/superkit-agents/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/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/pixel-process-ug/superkit-agents/senior-prompt-engineer"><img src="https://agentmods.dev/badge/skills/pixel-process-ug/superkit-agents/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 | $0.00066 | $0.02582 |
| Opus 5.5 | $0.00026 | $0.01033 |
| Sonnet 5.5 | $0.00013 | $0.00516 |
| Haiku 4.5 | $0.00007 | $0.00258 |
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.
How it starts
The opening of the file, as written. The whole thing — 346 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Senior Prompt Engineer
Overview
Design, test, and optimize prompts for large language models. This skill covers systematic prompt engineering including few-shot example design, chain-of-thought reasoning, system prompt architecture, structured output specification, parameter tuning, evaluation methodology, A/B testing, and prompt version management.
Announce at start: "I'm using the senior-prompt-engineer skill for prompt design and optimization."
Phase 1: Requirements
Goal: Define the task objective, quality criteria, and constraints before writing any prompt.
Actions
- Define the task objective clearly
- Identify input/output format requirements
- Determine quality criteria (accuracy, tone, format)
- Assess edge cases and failure modes
- Choose model and parameter constraints
STOP — Do NOT proceed to Phase 2 until:
- Task objective is stated in one sentence
- Input format and output format are defined
- Quality criteria are measurable
- Edge cases are listed
- Model selection is justified
Phase 2: Prompt Design
Goal: Draft the prompt with proper architecture, examples, and constraints.
Actions
- Draft system prompt with role, constraints, and format
- Design few-shot examples (3-5 representative cases)
- Add chain-of-thought scaffolding if reasoning is needed
- Specify output structure (JSON, markdown, etc.)
- Add error handling instructions
Prompt Architecture Layers
| Layer | Purpose | Example |
|---|---|---|
| 1. Identity | Who the model is | "You are a sentiment classifier..." |
| 2. Context | What it knows/has access to | "You have access to product reviews..." |
| 3. Task | What to do | "Classify each review as positive/negative/neutral" |
| 4. Constraints | What NOT to do | "Never include PII in output" |
| 5. Format | How to structure output | "Respond in JSON: {classification, confidence}" |
| 6. Examples | Demonstrations | 3-5 representative input/output pairs |
| 7. Metacognition | Handling uncertainty | "If uncertain, classify as neutral and explain" |
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 · 346 lines · 66 tokens per session scan A 074917d2bae7
senior-prompt-engineer is a skill published in the GitHub repository Pixel-Process-UG/superkit-agents (3 stars, last pushed 7mo ago), licensed MIT. It adds 66 tokens to every session and 2,582 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-03.
Other skills, from other repositories
claude
Expert Anthropic Claude API assistance covering Claude 3.5 Sonnet / Haiku / Opus, tool use, prompt caching, and long-context processing. Use when integrating Anthropic models into LLM applications.
prompt-engineering
Applies prompt engineering patterns — few-shot, chain-of-thought, templates, optimization. Use when improving prompts, debugging agent behavior, or designing generation prompts.
ai-loading-ux
Designs AI waiting-state UX: thinking indicators, reasoning display, progress steps, and streaming states. Use when asked to improve loading or perceived wait in AI interfaces.
llm-structured-output
Designs prompts, schemas, validation, and retry logic for reliable machine-readable model output. Use for JSON, typed objects, extractions, or tool arguments a system must parse.
developing-genkit-js
Develop AI-powered applications using Genkit in Node.js/TypeScript. Use when the user asks about Genkit, AI agents, flows, or tools in JavaScript/TypeScript, or when encountering Genkit errors, validation issues, type errors, or API problems.
agent-platform-prompt-management
Manages and orchestrates prompts in Agent Platform. Use when you need to create, list, retrieve, version, or delete managed prompts in Agent Platform. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform prompts.