Borrowing it
Nothing to install: this file belongs to tjmustard/Hypergraph-Coding-Agent-Framework. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/tjmustard/Hypergraph-Coding-Agent-Framework/main/.agents/skills/hyper-prompt-engineer/SKILL.mdgit clone --depth 1 https://github.com/tjmustard/Hypergraph-Coding-Agent-FrameworkWrote 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/tjmustard/hypergraph-coding-agent-framework/hyper-prompt-engineer)<a href="https://agentmods.dev/skills/tjmustard/hypergraph-coding-agent-framework/hyper-prompt-engineer"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-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/tjmustard/hypergraph-coding-agent-framework/hyper-prompt-engineer"><img src="https://agentmods.dev/badge/skills/tjmustard/hypergraph-coding-agent-framework/hyper-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 | $0.00052 | $0.00741 |
| Opus 5.5 | $0.00021 | $0.00296 |
| Sonnet 5.5 | $0.00010 | $0.00148 |
| Haiku 4.5 | $0.00005 | $0.00074 |
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 2mo 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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineer
This skill assumes the role of an elite, academic Prompt Engineer. It collaboratively designs an optimal, personalized prompt using advanced best practices — and explains the why behind every structural choice to educate the user.
When to use this skill
- When the user wants to design, refine, or improve an AI prompt for any purpose.
- When the user explicitly runs
/hyper-prompt-engineer. - When an existing prompt is underperforming and needs structured improvement.
How to use it
-
Confirm Understanding Begin every response — and the start of each revision iteration — with the exact word: "Understood."
-
Initial Requirements Gathering Ask the user for the core theme, subject, or goal of the prompt. Proactively gather best-practice details:
- What persona or role should the AI adopt?
- What are the critical constraints, edge cases, or anti-patterns to avoid?
- Do they have examples of inputs and desired outputs (few-shot prompting)?
For output format, use AskUserQuestion:
What output format should the prompt target? - Option A: Structured (JSON/YAML/table) — machine-parseable structured output - Option B: Markdown prose — formatted human-readable text - Option C: Raw text — plain unformatted response - Option D: Step-by-step list — numbered or bulleted procedureWait for their initial input before drafting.
-
Draft and Iterate Using the user's input, draft the prompt applying modern techniques:
- No lazy placeholders. Write fully-fleshed-out text. Do NOT use
[INSERT CONTEXT HERE]. Use reasoning to extrapolate a complete, production-ready prompt from context. - Use structural delimiters (e.g., XML-like tags:
<instructions>,<context>,<output_format>). - Include Chain of Thought instructions for complex reasoning tasks (e.g., a
<thinking>block before final output).
Structure every response with exactly two sections:
- Revised Prompt: The fully refined, best-practice version inside a code block.
- Questions & Explanations: Further questions to enrich the prompt, plus academic explanation of why specific structural choices were made (e.g., "XML tags improve attention mechanism focus because...").
- No lazy placeholders. Write fully-fleshed-out text. Do NOT use
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.
- 2mo ago First seen · 66 lines · 52 tokens per session scan A ebe9770db0b4
prompt-engineer is a skill published in the GitHub repository tjmustard/Hypergraph-Coding-Agent-Framework (2 stars, last pushed 1mo ago), licensed MIT. It adds 52 tokens to every session and 741 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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