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 agentmods add skills/phalves23/prompt-engineering-skill/prompt-engineeringnpx skills add PhAlves23/prompt-engineering-skill --skill prompt-engineeringgit clone --depth 1 https://github.com/PhAlves23/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/phalves23/prompt-engineering-skill/prompt-engineering)<a href="https://agentmods.dev/skills/phalves23/prompt-engineering-skill/prompt-engineering"><img src="https://agentmods.dev/badge/skills/phalves23/prompt-engineering-skill/prompt-engineering.svg" alt="Measured on agentmods" 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 | $0.00132 | $0.02819 |
| Opus 5 | $0.00066 | $0.01409 |
| Sonnet 5 | $0.00026 | $0.00564 |
| Haiku 4.5 | $0.00013 | $0.00282 |
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 5d 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 — 128 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering
Turns a raw draft into a production-grade prompt by applying proven prompt engineering techniques. Distilled from primary sources (Anthropic prompting best practices + prompt improver, OpenAI GPT-5/reasoning guides, Google Gemini PTCF, The Prompt Report) — see references/ for the full material.
Purpose
The user sends a prompt draft (or describes what they want). The skill returns a rewritten, optimized version, ready to paste, plus a short changelog of what changed and why. It is not meant to answer the prompt — it is meant to improve the prompt.
Operating principle
- Improve the prompt, don't run it. This skill rewrites the prompt; it does not respond to the task the prompt describes. If the request is ambiguous (the user pasted a prompt without saying what they want), the default is to optimize the prompt and, at the end, offer to run it. Only execute the task if the user explicitly asks ("run this prompt", "answer this").
- Produce directly. Don't ask for permission. Only ask clarifying questions (1–3, max) when missing information would materially change the rewrite — expected output type, target model, or audience. If unanswered, assume the most sensible default, state the assumption in the changelog, and proceed.
- Calibrate effort to complexity. Simple prompt (lookup, formatting) → lean structural rewrite, no inflating with CoT/examples. Complex prompt (reasoning, classification, generation with criteria, agentic) → full structure. Don't decorate beyond what's needed — prompt over-engineering hurts latency and cost with no gain.
- If the draft is already good, say so and do the minimum. Don't invent changes to justify the rewrite. Apply the anti-overengineering rule to yourself: when the prompt already has solid role, structure, scope, and output contract, deliver only the marginal tweaks that genuinely add value and state that the rest was already good. Rewriting a good prompt into something "different but not better" is a failure.
- Optimize for the right model. The default is Claude 4.x. If the user indicates another target (GPT/o-series, Gemini), adjust per
references/model-profiles.md— the rules diverge (e.g. reasoning models do NOT want "think step by step").
What ships with it
11 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.
- assets/canonical-template.md 1.6 KB
- references/auto-optimization.md 2.1 KB
- references/evaluation.md 3.6 KB
- references/model-profiles.md 4.1 KB
- references/prompt-security.md 2.3 KB
- references/quality-checklist.md 2.0 KB
- references/sources.md 3.8 KB
- references/task-patterns.md 4.6 KB
- references/technique-index.md 6.0 KB
- references/techniques.md 16 KB
- references/worked-examples.md 14 KB
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.
- 5d ago First seen · 128 lines · 132 tokens per session scan A 3e0ee50ac431
prompt-engineering is a skill published in the GitHub repository PhAlves23/prompt-engineering-skill (9 stars, last pushed 3mo ago), licensed MIT. It adds 132 tokens to every session and 2,819 once invoked, about $0.0007 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.
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