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 tronghieu/agent-skills --skill system-prompt-creatorgit clone --depth 1 https://github.com/tronghieu/agent-skillsWrote 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/tronghieu/agent-skills/system-prompt-creator)<a href="https://agentmods.dev/skills/tronghieu/agent-skills/system-prompt-creator"><img src="https://agentmods.dev/badge/skills/tronghieu/agent-skills/system-prompt-creator/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/tronghieu/agent-skills/system-prompt-creator"><img src="https://agentmods.dev/badge/skills/tronghieu/agent-skills/system-prompt-creator.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 | $0.00142 | $0.02773 |
| Opus 5.5 | $0.00057 | $0.01109 |
| Sonnet 5.5 | $0.00028 | $0.00555 |
| Haiku 4.5 | $0.00014 | $0.00277 |
Grade A, and why
system-prompt-creator 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 — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
System Prompt Creator
Build effective system prompts for any LLM using a structured, research-backed process derived from the official prompting guides of Claude (Anthropic), GPT-5 (OpenAI), and Gemini (Google).
This skill guides you through interview, analysis, structuring, drafting, and optimization — producing system prompts that are clear, well-structured, and tailored to the target model.
Workflow
Follow these 5 steps in order. Each step builds on the previous.
Step 1: Interview — Gather Requirements
Before writing anything, collect these essentials from the user:
- Target model — Which LLM? (Claude, GPT, Gemini, open-source, or model-agnostic)
- Use case — What will the AI do? (chatbot, agent, content generator, data extractor, code assistant, etc.)
- Persona — Who should the AI be? (role, expertise level, personality traits)
- Audience — Who interacts with it? (developers, end users, internal team, customers)
- Input types — What will users send? (free text, code, documents, images, structured data)
- Output format — What should responses look like? (prose, JSON, markdown, code, tables)
- Tone & style — Formal, casual, technical, friendly? Concise or detailed?
- Constraints — What must it NOT do? (safety rules, topic limits, action restrictions)
- Tools & capabilities — Does it have tool access? (APIs, file system, web search, code execution)
- Examples — Can the user provide examples of good/bad responses?
If the user is vague on some points, suggest reasonable defaults and confirm before proceeding.
Push for completeness: After gathering basics, ask "What else might users ask about?" Many prompts fail not because they handle their core task poorly, but because they don't anticipate adjacent scenarios. A billing chatbot also gets account access questions, product questions, and complaints. A coding agent needs to handle not just building features but also debugging, refactoring, and project setup. Map the full territory before writing.
What ships with it
6 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 First seen · 220 lines · 142 tokens per session scan A 7193f20189ce
system-prompt-creator is a skill published in the GitHub repository tronghieu/agent-skills (87 stars, last pushed 10d ago), licensed MIT. It adds 142 tokens to every session and 2,773 once invoked, about $0.0006 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 2026-08-30.
Other skills, from other repositories
outlines
Outlines: structured JSON/regex/Pydantic LLM generation.
instructor
Structured LLM outputs validated with Pydantic.
guidance
Constrain LLM output with grammars; guarantee valid JSON.
dspy
DSPy: declarative LM programs, auto-optimize prompts, RAG.
claude-api
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude…
omni-compression
Configure RTK (command output), Caveman (prose), and stacked compression modes. Manage language packs, custom rules, and test prompt compression reducing tokens by 60–90%.