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Copy this and paste it into Claude Code, Cursor, or any AI assistant:
I want to add the "para-zero — Copilot Instructions" prompt rules to my project. Repository: https://github.com/SecScholar/para-zero Please read the repo to find the rules/prompt file, then: 1. Download it to the correct location (.cursorrules, .windsurfrules, .github/prompts/, or project root — based on the file type) 2. If there's an existing rules file, merge the new rules in rather than overwriting 3. Confirm what was added
Description
Autonomous Zero-Day Synthesis Engine. [Internal Use Only]
Architecture Overview
Three-Layer Pipeline: • Intelligence (pulse_monitor.py): Aggregates CVE data from NVD/RSS feeds → data/research_queue/ • Synthesis (core/template_gen.py): LLM generates verification code → validates with AST firewall → deploys to modules/ • Execution (engine.py): Runs verification modules on test traffic; logs findings to findings.json Critical Data Flow: ` CVE JSON (research_queue/) ↓ [TemplateSynthesizer reads] ↓ [LLMClient generates code] ↓ [ASTValidator firewall checks] ├→ SAFE: modules/cve_XXXX.py [hot-loaded by engine.py] └→ UNSAFE: data/quarantine/ [rejected] `
Copilot Instructions for Para-Zero
Para-Zero is a Dynamic Application Security Testing Research Framework that automates vulnerability verification through AI-generated code. This document guides AI agents to be immediately productive.
Multi-Provider LLM Architecture (NEW)
Para-Zero now supports local-first (Ollama) + cloud fallback LLM providers: Provider Hierarchy: • Primary: Ollama (local, OpenAI-compatible, http://localhost:11434/v1) • Fallback: OpenAI (GPT-4), Anthropic (Claude) Task-Aware Model Selection: • task_type="reasoning" → Uses MODEL_REASONING (default: llama3) • task_type="coding" → Uses MODEL_CODING (default: deepseek-coder-v2) Configuration: All settings in config.py via environment variables: `python LLM_PROVIDER = os.getenv("LLM_PROVIDER", "ollama") OLLAMA_BASE_URL = "http://localhost:11434/v1" MODEL_REASONING = "llama3" MODEL_CODING = "deepseek-coder-v2" ` Academy Mode (New): train.py ingests CTF writeups and local knowledge: `bash python train.py --url "https://ctf-writeup.com" --tags "RCE" "Spring" python train.py --file research.md --tags "CVE-2024-1234" ` Extracts content → sanitizes to 6,000 words → saves to data/research_queue/ with ACADEMY_MODE tag.
System Prompts (Task-Specific)
Reasoning Prompt (for CTF/knowledge analysis): • Extracts key vulnerability concepts • Summarizes exploit techniques educationally • Focuses on defensive lessons Coding Prompt (for verification module synthesis): ` "Inherit from BaseVerifier. Implement verify(target_url, session). Return True if vulnerable, False otherwise. Non-destructive only. FORBIDDEN: os, sys, subprocess, eval, exec, open(write), while True." `
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Works With
Any AI assistant that accepts custom rules or system prompts