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 thedesignproject/agent-skills --skill prompt-engineergit clone --depth 1 https://github.com/thedesignproject/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/thedesignproject/agent-skills/prompt-engineer)<a href="https://agentmods.dev/skills/thedesignproject/agent-skills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/thedesignproject/agent-skills/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/thedesignproject/agent-skills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/thedesignproject/agent-skills/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 | $0.00093 | $0.01112 |
| Opus 5 | $0.00046 | $0.00556 |
| Sonnet 5 | $0.00019 | $0.00222 |
| Haiku 4.5 | $0.00009 | $0.00111 |
Grade A, and why
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 9d 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
91% identical to prompt-engineer — 13 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 — 134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
Expert prompt engineer specializing in designing, optimizing, and evaluating prompts that maximize LLM performance across diverse use cases.
When to Use This Skill
- Designing prompts for new LLM applications
- Optimizing existing prompts for better accuracy or efficiency
- Implementing chain-of-thought or few-shot learning
- Creating system prompts with personas and guardrails
- Building structured output schemas (JSON mode, function calling)
- Developing prompt evaluation and testing frameworks
- Debugging inconsistent or poor-quality LLM outputs
- Migrating prompts between different models or providers
Core Workflow
- Understand requirements — Define task, success criteria, constraints, and edge cases
- Design initial prompt — Choose pattern (zero-shot, few-shot, CoT), write clear instructions
- Test and evaluate — Run diverse test cases, measure quality metrics
- Validation checkpoint: If accuracy < 80% on the test set, identify failure patterns before iterating (e.g., ambiguous instructions, missing examples, edge case gaps)
- Iterate and optimize — Make one change at a time; refine based on failures, reduce tokens, improve reliability
- Document and deploy — Version prompts, document behavior, monitor production
Reference Guide
Load detailed guidance based on context:
| Topic | Reference | Load When |
|---|---|---|
| Prompt Patterns | references/prompt-patterns.md |
Zero-shot, few-shot, chain-of-thought, ReAct |
| Optimization | references/prompt-optimization.md |
Iterative refinement, A/B testing, token reduction |
| Evaluation | references/evaluation-frameworks.md |
Metrics, test suites, automated evaluation |
| Structured Outputs | references/structured-outputs.md |
JSON mode, function calling, schema design |
| System Prompts | references/system-prompts.md |
Persona design, guardrails, context management |
Prompt Examples
Zero-shot vs. Few-shot
Zero-shot (baseline):
Classify the sentiment of the following review as Positive, Negative, or Neutral.
Review: {{review}}
Sentiment:
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.
- 9d ago First seen · 134 lines · 93 tokens per session scan A b22597ff808f
prompt-engineer is a skill published in the GitHub repository thedesignproject/agent-skills (86 stars, last pushed 14d ago), licensed MIT. It adds 93 tokens to every session and 1,112 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to prompt-engineer, differing in 13 lines, and is treated as a copy.
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