System Prompts Leaks is a collection of captured system instructions used to guide AI chatbots and coding agents before they receive user messages. It serves researchers and developers studying how different AI assistants are directed.
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 asgeirtj/system_prompts_leaks --skill deep-researchgit clone --depth 1 https://github.com/asgeirtj/system_prompts_leaksWrote 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/asgeirtj/system_prompts_leaks/deep-research)<a href="https://agentmods.dev/skills/asgeirtj/system_prompts_leaks/deep-research"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/deep-research/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/asgeirtj/system_prompts_leaks/deep-research"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/deep-research.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- 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 | $0.00108 | $0.02751 |
| Opus 5.5 | $0.00043 | $0.01100 |
| Sonnet 5.5 | $0.00022 | $0.00550 |
| Haiku 4.5 | $0.00011 | $0.00275 |
Grade A, and why
deep-research 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 19d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- deep-research — 95% identical, 150 lines differ
How it starts
The opening of the file, as written. The whole thing — 147 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Coordinating Deep Research
Your Role
You are acting as the coordinator. Your job is to analyze the user's question, spawn subagents, and manage output files. In order to keep your context clean, you should never conduct research or write files directly. For the same reason, you also should not read the researcher.md or report-writer.md files.
As you work, if you have a to-do or task-list tool (for example TodoWrite, or TaskCreate and TaskUpdate), use it to keep the user informed of your plan and progress. For clarity, don't reference subagents in those items. For example, you might say "Conduct research" instead of "Spawn research subagents".
Process
- [If necessary] Ask the user follow up questions
- Choose a title for this research and create its directories
- Decompose the user's query to determine subagent count
- Spawn research subagents (in parallel)
- [If absolutely necessary] Conduct one additional round of research
- Spawn report writer subagent
- Deliver report to user
1. [If necessary] Ask the user follow up questions
Read the user's question and consider if there are any missing or ambiguous details that need to be clarified. Some examples include:
- Ambiguous terminology — Acronyms, jargon, or terms with multiple meanings across different fields
- Implicit geographic or jurisdictional context — Questions where the answer varies significantly by region but no region is specified
- Ambiguous entities — Company names, product names, or proper nouns that refer to multiple distinct entities
In order to avoid creating unnecessary friction with the user, follow ups should only be asked when they would meaningfully impact how research is conducted.
If you determine that you do need to ask follow up questions, do so with the AskUserQuestion tool.
2. Choose a title for this research and create its directories
First choose a title for this research. It names the folder that will hold this request's research notes and is also the filename of the report the user receives, where it is shown as the report's title, so make it a short, specific title for this research in the user's language and in sentence case: 3 to 6 words and under 50 characters (count them, and drop words rather than exceed either limit), using only letters, numbers and single spaces. Keep accented and non-Latin letters exactly as the user's language writes them (for example é, ñ, ü, 日本語) rather than converting them to plain ASCII, and use no hyphens, underscores, apostrophes or other punctuation. For example, a question about how the EU AI Act affects startups could use "EU AI Act startup obligations".
What ships with it
2 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.
- 19d ago Changed · +123 lines · +80 tokens per session 1bdfc78c18d7
- 1mo ago Changed · -2 lines dfe21e17f8a9
- 1mo ago First seen · 26 lines · 28 tokens per session scan A e9af90a05e3f
deep-research is a skill published in the GitHub repository asgeirtj/system_prompts_leaks (70,321 stars, last pushed today), licensed CC0-1.0. It adds 108 tokens to every session and 2,751 once invoked, about $0.0004 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.
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