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 beel-collab/presets.dev --skill prompt-engineergit clone --depth 1 https://github.com/beel-collab/presets.devWrote 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/beel-collab/presets.dev/prompt-engineer)<a href="https://agentmods.dev/skills/beel-collab/presets.dev/prompt-engineer"><img src="https://agentmods.dev/badge/skills/beel-collab/presets.dev/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/beel-collab/presets.dev/prompt-engineer"><img src="https://agentmods.dev/badge/skills/beel-collab/presets.dev/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 | $0.00041 | $0.02613 |
| Opus 5.5 | $0.00016 | $0.01045 |
| Sonnet 5.5 | $0.00008 | $0.00523 |
| Haiku 4.5 | $0.00004 | $0.00261 |
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 — 281 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Purpose
This skill transforms raw, unstructured user prompts into highly optimized prompts using established prompting frameworks. It analyzes user intent, identifies task complexity, and intelligently selects the most appropriate framework(s) to maximize Claude/ChatGPT output quality.
The skill operates in "magic mode" - it works silently behind the scenes, only interacting with users when clarification is critically needed. Users receive polished, ready-to-use prompts without technical explanations or framework jargon.
This is a universal skill that works in any terminal context, not limited to Obsidian vaults or specific project structures.
When to Use
Invoke this skill when:
- User provides a vague or generic prompt (e.g., "help me code Python")
- User has a complex idea but struggles to articulate it clearly
- User's prompt lacks structure, context, or specific requirements
- Task requires step-by-step reasoning (debugging, analysis, design)
- User needs a prompt for a specific AI task but doesn't know prompting frameworks
- User wants to improve an existing prompt's effectiveness
- User asks variations of "how do I ask AI to..." or "create a prompt for..."
Workflow
Step 1: Analyze Intent
Objective: Understand what the user truly wants to accomplish.
Actions:
- Read the raw prompt provided by the user
- Detect task characteristics:
- Type: coding, writing, analysis, design, learning, planning, decision-making, creative, etc.
- Complexity: simple (one-step), moderate (multi-step), complex (requires reasoning/design)
- Clarity: clear intention vs. ambiguous/vague
- Domain: technical, business, creative, academic, personal, etc.
- Identify implicit requirements:
- Does user need examples?
- Is output format specified?
- Are there constraints (time, resources, scope)?
- Is this exploratory or execution-focused?
Detection Patterns:
- Simple tasks: Short prompts (<50 chars), single verb, no context
- Complex tasks: Long prompts (>200 chars), multiple requirements, conditional logic
- Ambiguous tasks: Generic verbs ("help", "improve"), missing object/context
- Structured tasks: Mentions steps, phases, deliverables, stakeholders
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 First seen · 281 lines · 41 tokens per session scan A 445cc3c5bdea
prompt-engineer is a skill published in the GitHub repository beel-collab/presets.dev (3 stars, last pushed 5mo ago), licensed MIT. It adds 41 tokens to every session and 2,613 once invoked, about $0.0002 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 2026-08-31.
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