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 rjmurillo/ai-agents --skill prompt-engineergit clone --depth 1 https://github.com/rjmurillo/ai-agentsWrote 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/rjmurillo/ai-agents/prompt-engineer)<a href="https://agentmods.dev/skills/rjmurillo/ai-agents/prompt-engineer"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/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/rjmurillo/ai-agents/prompt-engineer"><img src="https://agentmods.dev/badge/skills/rjmurillo/ai-agents/prompt-engineer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00063 | $0.01624 |
| Opus 5.5 | $0.00025 | $0.00650 |
| Sonnet 5.5 | $0.00013 | $0.00325 |
| Haiku 4.5 | $0.00006 | $0.00162 |
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 1mo 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 — 185 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Optimizer
Optimizes system prompts by applying research-backed prompt engineering patterns. Human-in-the-loop phases: understand, plan, propose changes, receive approval, then integrate.
Purpose and Success Criteria
A well-optimized prompt achieves:
- Behavioral clarity: Agent knows exactly what to do in common cases and edge cases
- Appropriate scope: Complex tasks get decomposition; simple tasks don't trigger overthinking
- Grounded changes: Every modification traces to a specific pattern with documented impact
Optimization is complete when:
- Every change has explicit pattern attribution from the reference document
- No section contradicts another section
- The prompt matches its operating context (tool-use vs. conversational, token constraints)
- Human has approved both section-level changes and full integration
Triggers
| Trigger Phrase | Operation |
|---|---|
optimize this prompt |
Full Phase 0-4 optimization workflow |
improve this system prompt |
Analyze and propose changes with visual cards |
review my agent prompt |
Pattern-based review against reference |
refine this prompt for better results |
Targeted improvement with BEFORE/AFTER |
make this prompt more effective |
Technique selection and application |
When to Use This Skill
Use when the user provides a prompt and wants it improved, refined, or reviewed for best practices.
Do NOT use for:
- Writing prompts from scratch (different skill)
- Prompts that are already working well and user just wants validation
- Non-prompt content (documentation, code, etc.)
Required Resources
Before ANY analysis, read the appropriate pattern reference(s):
Single-Turn Reference (Always Read)
Read references/prompt-engineering-single-turn.md
Contains: Technique Selection Guide table, Quick Reference principles, domain-organized techniques with citations, Anti-Patterns section.
Multi-Turn Reference (Conditional)
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.
- 1mo ago Changed · +6 lines ff822386726e
- 1mo ago First seen · 179 lines · 63 tokens per session scan A 3d8237e4839c
prompt-engineer is a skill published in the GitHub repository rjmurillo/ai-agents (49 stars, last pushed today), licensed MIT. It adds 63 tokens to every session and 1,624 once invoked, about $0.0003 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 2026-09-03.
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