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 ZHOUCOOKIE/prompt-engineering-skill --skill engit clone --depth 1 https://github.com/ZHOUCOOKIE/prompt-engineering-skillWrote 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/zhoucookie/prompt-engineering-skill/en)<a href="https://agentmods.dev/skills/zhoucookie/prompt-engineering-skill/en"><img src="https://agentmods.dev/badge/skills/zhoucookie/prompt-engineering-skill/en/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/zhoucookie/prompt-engineering-skill/en"><img src="https://agentmods.dev/badge/skills/zhoucookie/prompt-engineering-skill/en.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 | $0.00212 | $0.11269 |
| Opus 5.5 | $0.00085 | $0.04508 |
| Sonnet 5.5 | $0.00042 | $0.02254 |
| Haiku 4.5 | $0.00021 | $0.01127 |
Grade A, and why
prompt-engineering 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.
The source is not reproduced here
A licence we could not identify
The repository carries a LICENSE file, but it is custom or dual enough that GitHub cannot name it and neither can this catalogue. Unknown terms are not permission, so the body is not copied here. Read the licence at the source and decide for yourself.
What ships with it
5 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.
- 10d ago Changed · +5 lines 9fe7821a5c14
- 1mo ago Changed · +37 lines 66792659acec
- 1mo ago Changed · +12 lines c3b61f38c36c
- 2mo ago First seen · 349 lines · 212 tokens per session scan A 08d30fc8f672
prompt-engineering is a skill published in the GitHub repository ZHOUCOOKIE/prompt-engineering-skill (2 stars, last pushed 11d ago), with no licence file. It adds 212 tokens to every session and 11,269 once invoked, about $0.0008 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 2026-08-31.
Other skills, from other repositories
prompt-engineering-outlines
Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library.
llm-engineering-expert
Build reliable applications on large language models: prompt design, structured output, evaluation, guardrails, and cost and latency control. Use when the user mentions LLMs, prompts, prompt engineering, few-shot examples, structured or JSON output, function calling, hallucination, model evaluation, token costs…
partner-anthropic
Anthropic intelligence — Claude model family, Claude Code, MCP protocol, Claude for Work programs, May 2026 state, Frank's relationship state, integration patterns. Use when generating Anthropic content, writing about Claude/MCP/Claude Code, partnership-conversation prep for Anthropic, or evaluating new Anthropic…
prompt-hub
Compose the 13-agent Prompt Hub team via @prompt-conductor. Use when designing prompts, optimizing prompts, evaluating prompts, importing patterns from Fabric / awesome-chatgpt-prompts / awesome-claude-prompts, running IFS introspection or psychometric profiling, or building knowledge-base prompt sets. Auto-trigger on…
cost-aware-llm-pipeline
Pick the smallest model that can finish the task, and record the class of work. Use when routing a search, a multi-file edit, or an architecture review, or when a weekly model cap is close.
instructor
Extract structured data from LLM responses with Pydantic validation, retry failed extractions automatically, parse complex JSON with type safety, and stream partial results with Instructor - battle-tested structured output library.