# Leaked AI System Prompt Aggregator (`jungle_synthesizer/leaked-system-prompt-collection-aggregator-scraper`) Actor Normalizes the scattered public GitHub collections of leaked/published AI system prompts (ChatGPT, Claude, Cursor, Devin, v0, Perplexity, and more) into one deduplicated dataset with product, vendor, version, and leak-date fields. Passive: reads only already-public repositories. - **URL**: https://apify.com/jungle\_synthesizer/leaked-system-prompt-collection-aggregator-scraper.md - **Developed by:** [BowTiedRaccoon](https://apify.com/jungle_synthesizer) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per event This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events. Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Leaked AI System Prompt Aggregator Scraper Aggregates the public GitHub collections of leaked and published AI system prompts — [jujumilk3/leaked-system-prompts](https://github.com/jujumilk3/leaked-system-prompts), [x1xhlol/system-prompts-and-models-of-ai-tools](https://github.com/x1xhlol/system-prompts-and-models-of-ai-tools), and [0xeb/TheBigPromptLibrary](https://github.com/0xeb/TheBigPromptLibrary) — into one normalized, deduplicated dataset. Returns product name, vendor, full prompt text, version, and leak date for hundreds of prompts spanning ChatGPT, Claude, Cursor, Devin, v0, Perplexity, and dozens of other AI products. --- ### Leaked AI System Prompt Aggregator Features - Unions three separately-maintained GitHub collections into one schema - Deduplicates across repos by content hash — the same leaked prompt posted in two collections shows up once - Extracts product, vendor, version, and leak date from each source repo's own filename conventions - Classifies each record into a coarse category (chat assistant, coding agent, search, image) - Pure API scraping against GitHub's own tree and raw-content endpoints — no browser, no proxy - Select which of the three source repos to pull from per run, or leave all three on for full coverage --- ### Who Uses Leaked System Prompt Data? - **Prompt engineers** — study real, shipped guardrail and tool-use prompts instead of guessing at structure - **AI safety researchers** — compare how different vendors constrain the same class of model, or at least the parts they were willing to leave in a system prompt - **Product teams** — benchmark their own system prompt against what competitors ship - **Security researchers** — track how prompt-injection defenses have evolved across product versions --- ### How the Aggregator Works 1. For each selected source repo, the actor fetches GitHub's full file tree in a single API call. 2. Each repo's file list is filtered down to actual prompt files using that repo's own naming conventions — the noise (READMEs, images, unrelated custom-instruction dumps) gets dropped before anything is fetched. 3. Matching files are fetched as raw text and hashed. A file whose content already appeared earlier in the run — the same prompt cross-posted to another collection — is skipped, not saved twice. 4. Each unique prompt is parsed into product, vendor, version, and leak date, then written to the dataset. --- ### Input ```json { "maxItems": 15, "sources": [ "jujumilk3/leaked-system-prompts", "x1xhlol/system-prompts-and-models-of-ai-tools", "0xeb/TheBigPromptLibrary" ] } ```` | Field | Type | Default | Description | |-------|------|---------|-------------| | `maxItems` | integer | `15` | Maximum number of leaked/published system prompt records to emit for this run. | | `sources` | array | all three | Which of the three source GitHub collections to aggregate. Leave all three selected for full union coverage. | To pull only from one collection: ```json { "maxItems": 25, "sources": ["x1xhlol/system-prompts-and-models-of-ai-tools"] } ``` *** ### Leaked AI System Prompt Aggregator Output Fields ```json { "product_name": "Claude 2.1", "vendor": "Anthropic", "prompt_text": "The assistant is Claude, created by Anthropic...", "prompt_version": "anthropic-claude_2.1", "leak_date": "2024-03-06", "source_repo": "jujumilk3/leaked-system-prompts", "source_file_path": "anthropic-claude_2.1_20240306.md", "source_url": "https://github.com/jujumilk3/leaked-system-prompts/blob/main/anthropic-claude_2.1_20240306.md", "char_count": 342, "model_family": "", "category": "chat_assistant", "dedup_hash": "8f08589aa2fe9dc5c571151d7875ab5cee53331a0034e731439a9b1677612739", "scraped_at": "2026-07-13T09:39:00.576Z" } ``` | Field | Type | Description | |-------|------|--------------| | `product_name` | string | Name of the AI product/tool the leaked prompt belongs to (e.g. Cursor, Claude Code, Devin) | | `vendor` | string | Company/organization that publishes the product (e.g. Anthropic, OpenAI, Cursor) | | `prompt_text` | string | Full text of the leaked/published system prompt | | `prompt_version` | string | Dated/version label for this prompt where given by the source | | `leak_date` | string | Date this version was leaked/published, `YYYY-MM-DD`, where derivable from the source filename | | `source_repo` | string | GitHub repo (`owner/name`) this record was aggregated from | | `source_file_path` | string | Path of the source file within the GitHub repo | | `source_url` | string | GitHub blob URL for the source file | | `char_count` | integer | Character length of `prompt_text` | | `model_family` | string | Underlying model family where labeled in the source; empty if not labeled | | `category` | string | Coarse product category: `chat_assistant`, `coding_agent`, `search`, `image`, or `other` | | `dedup_hash` | string | SHA-256 hex digest of the normalized prompt text, for cross-repo/cross-run dedup | | `scraped_at` | string | ISO-8601 timestamp when this record was scraped | *** ### FAQ #### How do I scrape leaked AI system prompts? Run this actor with `maxItems` set to however many records you want. It pulls from three public GitHub collections and hands back a deduplicated dataset — no manual repo-cloning or file-merging required. #### What data can I get from these leaked system prompt collections? Product name, vendor, full prompt text, version label, leak date, and the exact source file each prompt came from — enough to trace any record back to its origin repo and commit history. #### Does this actor need proxies? No. It reads GitHub's tree API and `raw.githubusercontent.com`, both of which are public and generous with unauthenticated traffic at this actor's scale. #### Can I filter by source collection? Yes. Set `sources` to any subset of the three supported repos. Leave it at the default to pull from all three. #### How much does this actor cost to run? Standard per-record pricing, no CAPTCHA-solving or residential-proxy surcharge — this is a plain GitHub API read. *** ### Need More Features? Need a fourth source repo added, or different category classification? [File an issue](https://console.apify.com/actors/issues) or get in touch. ### Why Use the Leaked AI System Prompt Aggregator? - **One dataset instead of three repos** — no more cloning and hand-merging separately-maintained collections - **Deduplicated by content, not filename** — a prompt that got cross-posted to two collections shows up once, with the hash to prove it - **Fully passive** — reads only already-public GitHub repositories; adds no new disclosure # Actor input Schema ## `sp_intended_usage` (type: `string`): Please describe how you plan to use the data extracted by this crawler. ## `sp_improvement_suggestions` (type: `string`): Provide any feedback or suggestions for improvements. ## `sp_contact` (type: `string`): Provide your email address so we can get in touch with you. ## `maxItems` (type: `integer`): Maximum number of leaked/published system prompt records to emit for this run. ## `sources` (type: `array`): Which public GitHub collections of leaked/published system prompts to aggregate. Leave all selected for full union coverage. ## Actor input object example ```json { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 15, "sources": [ "jujumilk3/leaked-system-prompts", "x1xhlol/system-prompts-and-models-of-ai-tools", "0xeb/TheBigPromptLibrary" ] } ``` # Actor output Schema ## `results` (type: `string`): No description # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 15, "sources": [ "jujumilk3/leaked-system-prompts", "x1xhlol/system-prompts-and-models-of-ai-tools", "0xeb/TheBigPromptLibrary" ] }; // Run the Actor and wait for it to finish const run = await client.actor("jungle_synthesizer/leaked-system-prompt-collection-aggregator-scraper").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 15, "sources": [ "jujumilk3/leaked-system-prompts", "x1xhlol/system-prompts-and-models-of-ai-tools", "0xeb/TheBigPromptLibrary", ], } # Run the Actor and wait for it to finish run = client.actor("jungle_synthesizer/leaked-system-prompt-collection-aggregator-scraper").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "sp_intended_usage": "Describe your intended use...", "sp_improvement_suggestions": "Share your suggestions here...", "sp_contact": "Share your email here...", "maxItems": 15, "sources": [ "jujumilk3/leaked-system-prompts", "x1xhlol/system-prompts-and-models-of-ai-tools", "0xeb/TheBigPromptLibrary" ] }' | apify call jungle_synthesizer/leaked-system-prompt-collection-aggregator-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=jungle_synthesizer/leaked-system-prompt-collection-aggregator-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Leaked AI System Prompt Aggregator", "description": "Normalizes the scattered public GitHub collections of leaked/published AI system prompts (ChatGPT, Claude, Cursor, Devin, v0, Perplexity, and more) into one deduplicated dataset with product, vendor, version, and leak-date fields. Passive: reads only already-public repositories.", "version": "0.1", "x-build-id": "EBQuKqPiEXpseMUCt" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/jungle_synthesizer~leaked-system-prompt-collection-aggregator-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-jungle_synthesizer-leaked-system-prompt-collection-aggregator-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/jungle_synthesizer~leaked-system-prompt-collection-aggregator-scraper/runs": { "post": { "operationId": "runs-sync-jungle_synthesizer-leaked-system-prompt-collection-aggregator-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/jungle_synthesizer~leaked-system-prompt-collection-aggregator-scraper/run-sync": { "post": { "operationId": "run-sync-jungle_synthesizer-leaked-system-prompt-collection-aggregator-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "required": [ "maxItems", "sources" ], "properties": { "sp_intended_usage": { "title": "What is the intended usage of this data?", "minLength": 1, "type": "string", "description": "Please describe how you plan to use the data extracted by this crawler." }, "sp_improvement_suggestions": { "title": "How can we improve this crawler for you?", "minLength": 1, "type": "string", "description": "Provide any feedback or suggestions for improvements." }, "sp_contact": { "title": "Contact Email", "minLength": 1, "type": "string", "description": "Provide your email address so we can get in touch with you." }, "maxItems": { "title": "Max Items", "type": "integer", "description": "Maximum number of leaked/published system prompt records to emit for this run.", "default": 15 }, "sources": { "title": "Source Repositories", "type": "array", "description": "Which public GitHub collections of leaked/published system prompts to aggregate. Leave all selected for full union coverage.", "items": { "type": "string", "enum": [ "jujumilk3/leaked-system-prompts", "x1xhlol/system-prompts-and-models-of-ai-tools", "0xeb/TheBigPromptLibrary" ] } } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```