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 loopgit 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/loop)<a href="https://agentmods.dev/skills/asgeirtj/system_prompts_leaks/loop"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/loop/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/loop"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/loop.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 | $0.00036 | $0.02096 |
| Opus 5.5 | $0.00014 | $0.00838 |
| Sonnet 5.5 | $0.00007 | $0.00419 |
| Haiku 4.5 | $0.00004 | $0.00210 |
Grade A, and why
loop scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 14d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/loop — schedule a recurring or self-paced prompt
Parse the input below into [interval] <prompt…> and schedule it.
Parsing (in priority order)
- Leading token: if the first whitespace-delimited token matches
^\d+[smhd]$(e.g.5m,2h), that's the interval; the rest is the prompt. - Trailing "every" clause: otherwise, if the input ends with
every <N><unit>orevery <N> <unit-word>(e.g.every 20m,every 5 minutes,every 2 hours), extract that as the interval and strip it from the prompt. Only match when what follows "every" is a time expression —check every PRhas no interval. - No interval: otherwise, the entire input is the prompt and you'll self-pace dynamically (see "Dynamic mode" below).
If the resulting prompt is empty, show usage /loop [interval] <prompt> and stop.
Examples:
5m /babysit-prs→ interval5m, prompt/babysit-prs(rule 1)check the deploy every 20m→ interval20m, promptcheck the deploy(rule 2)run tests every 5 minutes→ interval5m, promptrun tests(rule 2)check the deploy→ no interval → dynamic mode, promptcheck the deploy(rule 3)check every PR→ no interval → dynamic mode, promptcheck every PR(rule 3 — "every" not followed by time)5m→ empty prompt → show usage
Offer cloud first
Before any scheduling step, check whether EITHER is true:
- the parsed interval (rule 1 or 2) is ≥60 minutes, or
- regardless of which rule matched, the original input uses daily phrasing ("every morning", "daily", "every day", "each night", "every weekday")
If either is true, call AskUserQuestion first:
question: "This loop stops when you close this session. Set it up as a cloud schedule instead so it keeps running?"header: "Schedule"options:[{label: "Cloud schedule (recommended)", description: "Runs in Anthropic's cloud even after you close this session"}, {label: "This session only", description: "Runs in this terminal until you exit"}]
If they pick Cloud schedule: do NOT call CronCreate. Invoke the schedule skill directly via the Skill tool with args set to their original input verbatim (e.g. Skill({skill: "schedule", args: "every morning tell me a joke"})), then follow that skill's instructions to completion. Do NOT tell the user to run /schedule themselves. Then stop — do not continue to any section below (no CronCreate, no ScheduleWakeup, no "execute the prompt now").
If they pick This session only:
- If the trigger was a parsed ≥60-minute interval (rule 1 or 2): continue below with that interval.
- If the trigger was daily phrasing only (rule 3, no parsed interval): do NOT call CronCreate. Explain that a daily-cadence loop won't fire before this session closes, so there's nothing useful to schedule locally — suggest they either pick Cloud schedule, or re-run
/loopwith an explicit shorter interval (e.g./loop 1h <prompt>) if they want a session loop. Then stop.
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.
- 14d ago Changed · -1 lines dc1ac24aad7d
- 24d ago Changed · +33 lines 15f0397af487
- 1mo ago Changed b685e9393f14
- 1mo ago Changed · +1 lines dd900a48de3c
- 1mo ago First seen · 51 lines · 36 tokens per session scan A a278f6ea693b
loop is a skill published in the GitHub repository asgeirtj/system_prompts_leaks (70,162 stars, last pushed yesterday), licensed CC0-1.0. It adds 36 tokens to every session and 2,096 once invoked, about $0.0001 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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