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 report-to-pdfgit 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/report-to-pdf)<a href="https://agentmods.dev/skills/asgeirtj/system_prompts_leaks/report-to-pdf"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/report-to-pdf/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/report-to-pdf"><img src="https://agentmods.dev/badge/skills/asgeirtj/system_prompts_leaks/report-to-pdf.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 | $0.00017 | $0.00975 |
| Opus 5.5 | $0.00007 | $0.00390 |
| Sonnet 5.5 | $0.00003 | $0.00195 |
| Haiku 4.5 | $0.00002 | $0.00097 |
Grade A, and why
report-to-pdf 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 today.
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 — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Report To PDF
Create a polished PDF from an existing Data dashboard or report.
Overall Instructions
- Follow the shared Data instructions throughout this workflow.
- When converting a Data report or dashboard to PDF, follow the Data App Contract for source preservation and export.
Dependencies
Apply the shared dependency resolution policy to the categories below.
- Knowledge & Files: Source dashboards or reports, supporting artifacts, and requested styling references.
Workflow guidance
Use this skill only when the user explicitly asks for a PDF. Author through the canonical [@pdf](plugin://pdf@openai-primary-runtime) plugin.
Route
- For an existing dashboard or report, use its verified local
dist/index.htmland reviewedsrc/data.json, or its exact published HTTPS.chatgpt.siteor.chatgpt-team.siteURL with reviewed snapshot and presentation state. Follow Reading a linked dashboard or report to preserve the linked view and requested scope, including all reader-visible tabs and sections for an entire-dashboard export. - Convert the existing app directly without creating an intermediate report or rebuilding it. If the user requested a new report and no source app exists yet, use
$build-reportonce and reuse its verified HTML and evidence for every requested conversion. - Do not print an app shell, create a PDF-specific app runtime, or author from an unstructured chat summary.
Workflow
- Preflight capabilities. Confirm that the canonical PDF skill is available before conversion. If it is unavailable, explain the blocker and retain any verified source HTML.
- Resolve the existing app. For a local app, verify
dist/index.htmland usesrc/data.json. For a published app, verify the exact HTTPS.chatgpt.siteor.chatgpt-team.sitesource URL, load the compiled app, and fetch its reviewed snapshot and saved presentation from the source origin's/api/snapshotand/api/presentationendpoints. Follow Published Site authentication before declaring a401blocked. Reject unreadable or unverified sources. Preserve the reviewed evidence, requested dashboard/report scope, and current filters, selections, and presentation. For local apps, if browser access or page tools are unavailable or fail, continue from the verified compiled HTML and reviewed data. Apply supported link parameters and any supplied or saved presentation, otherwise use the authored presentation. Include all reader-visible tabs and sections for an entire-dashboard export. Briefly note after export that browser-only edits may not be included. - Author with the PDF plugin. Read and follow the canonical PDF skill completely. Preserve the title, narrative, charts, tables, metric definitions, caveats, freshness, source labels, and links. For chart figures from an open Data app, prefer
list_data_app_cards({})followed byget_data_app_card_image({ cardId, scale: 3 })orget_data_app_card_images({ cardIds, scale: 3 }); follow Card images for slides and documents to discover tools, verify scope, and decode local PNGs without printing base64. If capture is unavailable or unsuitable, use the chart's vector output or reviewed-data rendering. Keep surrounding report text selectable and avoid duplicating titles already captured in a card. Omit interactive controls and internal runtime or local-path metadata. - Render, inspect, and repair. Confirm page count and metadata, extract text when selectable text is expected, and inspect every page. Fix blank pages, clipping, overlap, broken tables, missing chart marks, unreadable glyphs, unresolved placeholders, and leaked app controls.
- Validate and deliver. Confirm the PDF is non-empty, opens cleanly, and contains the required claims, definitions, caveats, freshness, and provenance. Return the verified PDF and, when useful, the source report HTML.
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.
- today First seen · 48 lines · 17 tokens per session scan A 7d1f38de8aef
report-to-pdf is a skill published in the GitHub repository asgeirtj/system_prompts_leaks (70,966 stars, last pushed today), licensed CC0-1.0. It adds 17 tokens to every session and 975 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 2026-11-07.
report-to-pdf compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| report-to-pdfasgeirtj/system_prompts_leaks | Skill | 71k | today | CC0-1.0 | A | 17 | Claude Code, Codex |
| pdfNousResearch/hermes-agent | Skill | 261k | today | MIT | C | 18 | Claude Code, Codex |
| plan-deck-specEverMind-AI/Raven | Skill | 6.1k | yesterday | Apache-2.0 | A | 71 | Claude Code, Codex |
| pdfAstrBotDevs/AstrBot | Skill | 46k | 2d ago | AGPL-3.0 | A | 45 | Claude Code, Codex |
| pptxagents-flex/agents-flex | Skill | 1.1k | yesterday | Apache-2.0 | A | 50 | Claude Code, Codex |
| docxnextlevelbuilder/goclaw | Skill | 3.7k | today | none | A | 168 | Claude Code, Codex |
| pdfnextlevelbuilder/goclaw | Skill | 3.7k | today | none | A | 92 | Claude Code, Codex |