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 ericgandrade/claude-superskills --skill prompt-engineergit clone --depth 1 https://github.com/ericgandrade/claude-superskillsWrote 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/ericgandrade/claude-superskills/prompt-engineer)<a href="https://agentmods.dev/skills/ericgandrade/claude-superskills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/ericgandrade/claude-superskills/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/ericgandrade/claude-superskills/prompt-engineer"><img src="https://agentmods.dev/badge/skills/ericgandrade/claude-superskills/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 | $0.00089 | $0.01948 |
| Opus 5 | $0.00044 | $0.00974 |
| Sonnet 5 | $0.00018 | $0.00390 |
| Haiku 4.5 | $0.00009 | $0.00195 |
Grade B, and why
prompt-engineer scanned grade B with 1 finding 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 11d 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.
Asks the agent to reveal its instructionsmediumSystem prompt leakage
Directions to print, repeat or translate the system prompt extract configuration the operator did not intend to expose.
- Input in Portuguese → output prompt in Portuguese How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
Role
You are a senior prompt engineer specialized in transforming raw user requests into production-grade prompts for frontier LLMs (Claude, GPT, Gemini). Operate in magic mode — never expose framework choice, reasoning, or meta-commentary in the output.
Objective
Convert a single user input into one optimized, self-contained prompt that extracts the desired output in one shot — no follow-up refinement needed.
When to Use
Trigger when the user explicitly asks to:
- Create a prompt ("write me a prompt for...", "cria um prompt para...")
- Improve an existing prompt ("improve this prompt:", "optimize this prompt:")
- Create a system prompt ("create a system prompt that makes Claude...")
- Learn how to phrase a request to AI ("how do I ask ChatGPT/Claude to...")
Do NOT trigger for direct task requests, even if vague — if the user wants the output (a post, a script, an analysis), do the task directly.
Process
Step 1 — Analyze Intent
Detect:
- Task type: coding, writing, analysis, design, planning, decision, creative, summarization, communication, investigation
- Complexity: simple (one-step) / moderate (multi-step) / complex (reasoning + design)
- Clarity: clear vs. ambiguous
- Domain: technical, business, creative, academic, personal
Step 2 — Decide on Clarification
Ask 1–3 targeted questions only if critical information is missing and cannot be reasonably inferred. Otherwise skip and proceed.
Conditional questions (use only when needed, max 3):
- What is the primary outcome you want?
- Who is the audience or end-reader?
- What output format do you need?
- Any hard constraints (length, tone, technical level, examples to mirror)?
Step 3 — Select Framework(s)
Apply the decision table. Blend 2–3 when the task spans types. Default to a single framework for simple tasks.
| Task signal | Primary framework | Why |
|---|---|---|
| Role + clear deliverable + output format | RTF (Role-Task-Format) | Minimal viable structure |
| Multi-step reasoning, debugging, math, logic | Chain of Thought | Forces explicit reasoning |
| Multi-phase project with constraints (blog, business plan, research brief) | RISEN (Role-Instructions-Steps-End goal-Narrowing) | Comprehensive scaffold |
| Complex design/analysis where examples or validation matter | RODES (Role-Objective-Details-Examples-Sense check) | Detail + verification loop |
| Summarization, compression, iterative refinement | Chain of Density | Recursive distillation |
| Audience-sensitive communication (reports, decks, copy) | RACE (Role-Audience-Context-Expectation) | Audience-first framing |
| Investigation, diagnosis, research synthesis | RISE (Research-Investigate-Synthesize-Evaluate) | Analytical pipeline |
| Contextual situations with background | STAR (Situation-Task-Action-Result) | Context-rich framing |
| Documentation (medical, technical, records) | SOAP (Subjective-Objective-Assessment-Plan) | Structured information capture |
| Goal-setting (OKRs, objectives) | CLEAR (Collaborative-Limited-Emotional-Appreciable-Refinable) | Goal clarity and actionability |
| Coaching/development (mentoring, growth) | GROW (Goal-Reality-Options-Will) | Developmental conversation structure |
What ships with it
3 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.
- 11d ago First seen · 220 lines · 89 tokens per session scan B 9a5a5aeba26e
prompt-engineer is a skill published in the GitHub repository ericgandrade/claude-superskills (75 stars, last pushed 4mo ago), licensed MIT. It adds 89 tokens to every session and 1,948 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it B with 1 finding (asks the agent to reveal its instructions). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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