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 agentsgit 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/agents)<a href="https://agentmods.dev/skills/asgeirtj/system_prompts_leaks/agents"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/agents/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/agents"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/agents.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 | $0.00130 | $0.03054 |
| Opus 5.5 | $0.00052 | $0.01222 |
| Sonnet 5.5 | $0.00026 | $0.00611 |
| Haiku 4.5 | $0.00013 | $0.00305 |
Grade A, and why
agents 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 21d 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 — 206 lines — stays where its author put it; the contents beside it link to each section on GitHub.
agents
- Decide: yourself unless several lanes or a long wait (§ 1); an irreversible step → § 2's question, then act.
- Clarify: no target you could verify →
read_skill grill, its interview until settled, no plan before; one decision to confirm → one grill-shaped question before the act;remember --decisionas each settles. - Init: the message on stdin,
init - --slug … --done-means … --goal … --repo …;--engine codex|claudewhen the user names the workers' engine. - Propose (only when § 1 says threads; else do it now): the threads as
JSON →
propose. - Open or ask: a plain split →
go, arm the wake, tell the plan; else the texts to choose between, or § 2's question. - Each wake:
contextonce; inbox, oldest first; propose ready threads; one name-first line per thread; wake armed; end the turn. - Done: verify,
accepteach thread,agents.py archive, say what closed and stayed.
Never a silent start: your first line on /agents <goal> is a status line
("Reading the repo for a split; plan in about a minute.") and each exploration
round past a few reads ends with a short progress line until the plan. Run
python3 <skill-dir>/scripts/agents.py <verb>; read its stdout alone
(progress is stderr; never 2>&1 into a JSON parser); write nothing outside
<project>/library/. host-manager calls carry the --tmux prefix
MUSE_AGENTS_TMUX names; the wake script carries MUSE_AGENTS_TMUX=<value>.
Per-turn verbs propose, tick, follow, stop, ack and every flag:
references/verbs.md; context reads the inbox it returns.
1. Decide
Every child is an agents.py lane; never subagent_spawn. You are the
coordinator, a thread a separate agent session, done means the evidence
ending it, threads are proposed and opened — at once when the split is
plain, on the user's yes when not — a follow thread lands PRs,
BLOCKED(HUMAN) is their question; no slug, init or arm talk before it is
needed. Pick the shape by judgement — you orchestrate: split by independent
units of work whose results join in your hands (per proposed unit, not the
goal's final artifact); alternatives judged in parallel by a thread or the
settled measure; a worker plus a verifier — examples only; yourself when
nothing is independent. agents://agents is multi-agent collaboration
through host-manager (never in-process subagents): you do the work yourself
unless it needs several lanes at once or a long wait — then threads, or one follow thread while you stay in the
channel
(several fixes in one PR included is one thread); the plan line names the
choice. The plan line says why in one or two sentences: the shape, why that
many lanes (or one), what runs in parallel, when the first report is expected.
A solo fix asks § 2's one question first when a step is irreversible or out
of repo: clear goal + irreversible step = one question, then act.
"Pick up " is resume <slug> --takeover --confirm "<their words>",
never init: run it before any git or test in their clone.
What ships with it
8 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.
- 21d ago First seen · 206 lines · 130 tokens per session scan A f9fb351b9783
agents is a skill published in the GitHub repository asgeirtj/system_prompts_leaks (70,207 stars, last pushed today), licensed CC0-1.0. It adds 130 tokens to every session and 3,054 once invoked, about $0.0005 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 2026-10-05.
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