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 hardness1020/awesome-prompt-skill --skill prompt-engineeringgit clone --depth 1 https://github.com/hardness1020/awesome-prompt-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/hardness1020/awesome-prompt-skill/prompt-engineering)<a href="https://agentmods.dev/skills/hardness1020/awesome-prompt-skill/prompt-engineering"><img src="https://agentmods.dev/badge/skills/hardness1020/awesome-prompt-skill/prompt-engineering/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/hardness1020/awesome-prompt-skill/prompt-engineering"><img src="https://agentmods.dev/badge/skills/hardness1020/awesome-prompt-skill/prompt-engineering.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 | $0.00114 | $0.03347 |
| Opus 5 | $0.00057 | $0.01673 |
| Sonnet 5 | $0.00023 | $0.00669 |
| Haiku 4.5 | $0.00011 | $0.00335 |
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 12d 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 — 389 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
Expert guidance for designing, optimizing, evaluating, and securing prompts for LLMs. Patterns derived from production agentic systems (Claude Code) and the prompt engineering research landscape.
Core Capabilities
- Core Prompting Techniques - Reasoning, structured output, few-shot, constraint injection
- System Prompt Architecture - Modular section-builders, static/dynamic boundaries, caching
- Agent & Tool Integration - Agent specialization, tool-aware prompts, tiered permissions
- Prompt Optimization & Automation - APE, DSPy, EvoPrompt, compression, A/B testing
- Security & Robustness - Injection defense, instruction hierarchy, Constitutional AI
- Evaluation & Benchmarking - Assertion-based, model-graded, regression testing
- Production Best Practices - Prompt-as-code, versioning, monitoring, anti-patterns
For deep dives, see the references/ directory linked from each section below.
1. Core Prompting Techniques
Full catalog: See references/techniques-catalog.md for all 58+ techniques with examples.
Reasoning Amplification
- Chain of Thought (CoT): Add "Let's think step by step" or provide worked examples. Best for math, logic, multi-step reasoning.
- Tree of Thoughts (ToT): Explore multiple reasoning branches, evaluate and prune. Use for planning, creative tasks, or problems with dead ends.
- Self-Consistency: Sample multiple CoT paths, take majority vote. Improves reliability at cost of latency.
- ReAct (Reason + Act): Interleave reasoning traces with tool calls. Foundation of agentic prompting.
Structured Output
- XML tagging: Wrap sections in
<analysis>,<result>,<examples>tags for clear structure. Anthropic's recommended approach. - JSON mode: Constrain output to valid JSON schemas for API consumption.
- Markdown formatting: Use headers, lists, code blocks for human-readable structured output.
Few-Shot & Exemplars
What ships with it
7 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.
- 12d ago First seen · 389 lines · 114 tokens per session scan A 64392aa073d7
prompt-engineering is a skill published in the GitHub repository hardness1020/awesome-prompt-skill (4 stars, last pushed 5mo ago), licensed MIT. It adds 114 tokens to every session and 3,347 once invoked, about $0.0006 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
optimize-claude-opus-5-prompts
Clarify, audit, and rewrite rough or existing prompts for Claude Opus 5 using Anthropic's official prompting guidance. Use when a user asks to optimize, improve, migrate, debug, or design a prompt, system prompt, or agent harness for Claude Opus 5 (claude-opus-5), including requests phrased as Opus 5 prompt…
optimize-claude-fable-5-prompts
A prompt-editing method for Claude Fable 5, an AI model. It turns rough or existing instructions into clearer prompts while keeping the original goal and important limits.
optimize-gemini-prompts
A prompt-editing skill for clarifying and rewriting prompts intended for Google’s Gemini models.
optimize-gpt-5-6-prompts
A guide for improving prompts written for OpenAI’s GPT-5.6 models, including GPT-5.6 Sol.
optimize-gpt-6-astra-prompts
Clarify, audit, migrate, and rewrite prompts for GPT-6 Astra using OpenAI's official latest-model prompting guidance. Use when a user asks to optimize, improve, rewrite, debug, migrate, or design a GPT-6 Astra prompt, including requests phrased as GPT-6, Astra, or gpt-6-astra prompt optimization, or when an…
optimize-deepseek-v4-prompts
Clarify, audit, and rewrite rough or existing prompts specifically for DeepSeek-V4 Pro and Flash using the DeepSeek-AI V4 technical report as the model-specific evidence base. Use when a user asks to optimize, improve, rewrite, debug, migrate, or design a DeepSeek-V4 prompt, including requests phrased as DeepSeek…