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 alecs5am/ralphy --skill prompt-engineering-expertgit clone --depth 1 https://github.com/alecs5am/ralphyWrote 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/alecs5am/ralphy/prompt-engineering-expert)<a href="https://agentmods.dev/skills/alecs5am/ralphy/prompt-engineering-expert"><img src="https://agentmods.dev/badge/skills/alecs5am/ralphy/prompt-engineering-expert.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00076 | $0.01104 |
| Opus 5 | $0.00038 | $0.00552 |
| Sonnet 5 | $0.00015 | $0.00221 |
| Haiku 4.5 | $0.00008 | $0.00110 |
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 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 — 103 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering Expert
Use this skill to make prompts clearer, more reliable, easier to evaluate, and better matched to the model, task, tools, and operating context.
Workflow
Identify the prompt's job: task, audience, model or agent context, available tools, inputs, output consumers, and failure cost. Diagnose weaknesses before rewriting: ambiguity, missing context, conflicting instructions, brittle examples, unsafe scope, untestable success criteria, output format gaps, or token bloat. Choose the lightest effective technique: direct instructions first; add roles, examples, structured tags, staged reasoning, tool-use guidance, or prompt chaining only when they solve a concrete issue. Produce an improved prompt or instruction set with enough surrounding explanation for the user to evaluate the tradeoffs. Define validation: expected behaviors, edge cases, regression cases, and success criteria.
Response Pattern
For prompt reviews, prefer this structure:
- Diagnosis: the highest-impact issues, ordered by severity.
- Revision: a ready-to-use improved prompt.
- Why It Works: concise rationale for major changes.
- Tests: representative cases the user should run.
For prompt generation, prefer:
- Ask for missing high-risk constraints only if they cannot be reasonably assumed.
- Otherwise state assumptions and draft the prompt.
- Include variables/placeholders when the prompt should be reusable.
- Include a short evaluation checklist.
Core Principles
- Make the task objective explicit.
- Give only the context needed to perform the task.
- State non-negotiable constraints separately from preferences.
- Specify the expected output format when downstream use matters.
- Use examples to teach patterns, not to smuggle one-off answers.
- Avoid hidden contradictions between role, task, constraints, and format.
- Prefer observable success criteria over subjective goals such as "high quality" or "good."
- Preserve model flexibility where multiple valid answers exist.
- Add safeguards for uncertainty: cite provided evidence, mark assumptions, and say what is unknown.
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.
- 5d ago First seen · 103 lines · 76 tokens per session scan A ec81a7767660
prompt-engineering-expert is a skill published in the GitHub repository alecs5am/ralphy (132 stars, last pushed 13d ago), licensed Apache-2.0. It adds 76 tokens to every session and 1,104 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 2026-09-03.
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