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 Miaoge-Ge/coding-agent-skills --skill prompt-engineering-expertgit clone --depth 1 https://github.com/Miaoge-Ge/coding-agent-skillsWrote 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/miaoge-ge/coding-agent-skills/prompt-engineering-expert)<a href="https://agentmods.dev/skills/miaoge-ge/coding-agent-skills/prompt-engineering-expert"><img src="https://agentmods.dev/badge/skills/miaoge-ge/coding-agent-skills/prompt-engineering-expert/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/miaoge-ge/coding-agent-skills/prompt-engineering-expert"><img src="https://agentmods.dev/badge/skills/miaoge-ge/coding-agent-skills/prompt-engineering-expert.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 | $0.00077 | $0.00969 |
| Opus 5 | $0.00039 | $0.00485 |
| Sonnet 5 | $0.00015 | $0.00194 |
| Haiku 4.5 | $0.00008 | $0.00097 |
Grade A, and why
prompt-engineering-expert 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 10d 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 — 79 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering Expert
Examples beat adjectives; structure beats length. Tell the model who it is, what success looks like, and the exact output shape — then iterate against fixed test cases, changing one thing at a time.
When to Use
- Writing or improving a system/user prompt.
- Inconsistent, off-format, verbose, or low-quality model output.
- Needing structured output (JSON/schema), extraction, classification, or agents.
- Reducing hallucination or steering tone/length/reasoning.
When NOT to Use
- Retrieval/context assembly →
rag-expert. - Quantitative eval/regression harness →
llm-testing-expert. - Model training/fine-tuning →
deep-learning-expert. - Anthropic SDK wiring + caching →
claude-api(built-in).
Core Principles
1. Structure the prompt
- State role, task, and success criteria explicitly. Durable rules → system prompt; the variable task/data → user turn.
- Separate sections with clear delimiters (markdown headings or XML-like tags
<context>…</context>) so instructions, data, and examples don't blur. - Be specific about constraints: length, tone, audience, and what to do on ambiguity or missing info. Prefer positive instructions ("respond in valid JSON") over a pile of "don't".
2. Steer with examples & reasoning
- Few-shot: 2–5 representative, diverse examples that demonstrate the exact format and tricky edge cases. Examples teach format faster than descriptions.
- For reasoning-heavy tasks, let the model think before answering (chain-of-thought / a scratchpad), but keep the final output concise — or separate reasoning from the user-visible answer.
- Put the most important instruction near the start or end; long middles get "lost".
3. Reliable structured output
- For machine consumption, define a strict schema and use the provider's structured-output/JSON mode or tool-calling. Show one example of the exact shape.
- Provide a fallback so the model doesn't invent values: "if a field is unknown, use null; do not guess."
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.
- 10d ago First seen · 79 lines · 77 tokens per session scan A c7e32f1afd17
prompt-engineering-expert is a skill published in the GitHub repository Miaoge-Ge/coding-agent-skills (5 stars, last pushed 3mo ago), licensed MIT. It adds 77 tokens to every session and 969 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 2026-08-31.
Other skills, from other repositories
Prompt Refiner
Improves AI prompts to be clearer, more specific, and produce more consistent outputs.
compressed-system-prompts
How to refactor wordy, repetitive system prompts into high-density imperative token structures, cutting baseline system prompt costs by 70% while improving instruction adherence.
agent-system-prompt-architect
Use when designing, reviewing, revising, or templating deployable system prompts for agent projects, especially prompts needing clear roles, task boundaries, tool-use rules, evidence handling, output formats, safety behavior, or compact runtime-ready structure.
best-practices
Transforms vague prompts into optimized Claude Code prompts. Adds verification, specific context, constraints, and proper phasing. Invoke with /best-practices.
llm-application-dev
Building applications with Large Language Models - prompt engineering, RAG patterns, and LLM integration. Use for AI-powered features, chatbots, or LLM-based automation.
firebase-ai
Use when setting up firebaseai, generating text/chat with Gemini, streaming AI output, building multimodal prompts, or handling AI errors.