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 create-data-contextgit 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/create-data-context)<a href="https://agentmods.dev/skills/asgeirtj/system_prompts_leaks/create-data-context"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/create-data-context/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/create-data-context"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/create-data-context.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 | $0.00055 | $0.09183 |
| Opus 5.5 | $0.00022 | $0.03673 |
| Sonnet 5.5 | $0.00011 | $0.01837 |
| Haiku 4.5 | $0.00006 | $0.00918 |
Grade A, and why
create-data-context 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 yesterday.
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 — 367 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Create Data Context
Create compact, editable context for how the user wants analysis, reports, and dashboards produced. Context can contain a single tool preference, report or dashboard look and feel, analysis practices, data definitions, or a useful combination. Keep guidance for the same audience in one skill and one plugin; data definitions are optional. Establish what the guidance covers separately from who may receive it. Honor draft-only, source-only, package-only, no-install, and already-reviewed instructions.
Principles
- Call metric definitions, source maps, and caveats "data context" in user-facing copy. Preserve actual provider names, URLs, and technical identifiers.
- Reuse answered questions, honor plain-text requests, and ask only currently useful unresolved questions. Group a few related, independently answerable blocking questions when that saves a round trip, such as references/scan direction and personal versus shared use. Explain the shared context above the group and each choice where needed. Separate questions whose answer depends on an earlier decision, and honor a request for one question at a time. Prefer a nonblocking elicitation (
request_user_input_asyncon Codex when callable and supported), following the host contract. Keep pending questions available where the host supports persistence; do not repeatedly recreate answered or unchanged questions. Use each actual answer before dependent work and continue authorized independent work while waiting. - Put 1–2 short sentences explaining the current choice or useful inputs immediately above the question inside the elicitation. Use its instruction/description field when supported, otherwise put the explanation before the question in its title. Do not leave essential instructions only in commentary that may collapse. Use P1A's audience explanation once; avoid repeating local-install or sharing disclaimers in the intro and each question.
- Whenever a turn ends awaiting user input, the last final response must contain all currently pending questions, their useful choices or reply instructions, and enough context to answer without expanding progress or finding a form. During initial setup, include the P1 introduction in that final response if it has not yet appeared in a completed turn's final response. This applies even when a form was called or an earlier message was labeled as an answer. Text followed by more tool calls or a later final reply does not satisfy this visible handoff. Finish independent reads first, then send the self-contained final response and yield; if work continues after an earlier question, include the necessary introduction and pending questions again in the last response. Never replace them with "answer the questions above" or a skill-requirement explanation.
- Required choices still need an actual reply before dependent work proceeds. Retain the pending elicitation when the host supports it across turns, with the final-response handoff above regardless of form persistence. If a form is unavailable or renders as ordinary text, ask the pending questions directly in the final response. Do not keep a turn alive just to hold a card open or claim persistence the host does not support. Optional preferences may use a safe fallback; they do not resolve required choices.
- Keep one coherent scope and preserve unrelated context. Save only useful, non-obvious information; omit generic policy/background, installed-tool inventories, and visible capability restatements.
- Apply the intended audience established for each draft or update; use P1A when it is unclear. Keep shared working guidance broadly useful and scoped to the relevant data work or deliverable. Treat optional shared style conventions as defaults that leave room for personal preferences; do not let style preferences redefine metrics or override mandatory policies. Preserve explicit workflow requirements and personal exceptions with their actual scope. Split only when different audiences need different guidance or the user explicitly requests it, never merely because definitions and style are different content types.
- Write preferences as direct model instructions, preserving their meaning and scope without quoting answers or labeling them "explicit user input." Prepopulate facts only with very high-confidence support from current, applicable authoritative sources and adjacent citations. Do not infer analytical methods, query budgets, interaction choices, or tool preferences from nearby examples.
- Keep state, accepted answers, skips, and deferrals in task notes, never the generated skill. Load substantial conditional knowledge only when relevant.
- Use personal memories only when authorized. Present their summary for user review before incorporating them into shared drafts or packages. Exclude secrets, personal details, customer-specific records, and private transcripts from shared context; corroborate recollections before treating them as shared standards.
What ships with it
3 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.
- yesterday First seen · 367 lines · 55 tokens per session scan A ec6c3fc779c1
create-data-context is a skill published in the GitHub repository asgeirtj/system_prompts_leaks (71,023 stars, last pushed today), licensed CC0-1.0. It adds 55 tokens to every session and 9,183 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 2026-11-07.
create-data-context compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| create-data-contextasgeirtj/system_prompts_leaks | Skill | 71k | today | CC0-1.0 | A | 55 | Claude Code, Codex |
| llm-wikiNousResearch/hermes-agent | Skill | 262k | today | MIT | B | 19 | Claude Code, Codex |
| qmdNousResearch/hermes-agent | Skill | 262k | today | MIT | B | 14 | Claude Code, Codex |
| ai-persistenceTanStack/ai | Skill | 3.3k | today | MIT | A | 88 | Claude Code |
| ai-persistence/serverTanStack/ai | Skill | 3.3k | today | MIT | A | 83 | Claude Code |
| ai-core/client-persistenceTanStack/ai | Skill | 3.3k | today | MIT | A | 141 | Claude Code |
| tanstack-ai-memory-redisTanStack/ai | Skill | 3.3k | today | MIT | A | 52 | Claude Code |