Operator-ready prompt for reuse, tuning, and workspace runs.
This item is set up for developers who want to inspect the original language, fork it into Workspace, and adapt the evidence model without losing the source prompt structure.
Implementation handoffs, eval setup, and prompt tuning where you need the original structure intact.
Inspect first, copy once, then fork into Workspace when you want variants, notes, and model settings attached to the same run.
Swap domain facts, examples, and any hard-coded entities for your own context.
Tighten the evidence or verification requirement if this is headed toward production.
Decide which failure mode you want to evaluate first before you branch the prompt.
This prompt already carries implementation detail, tool context, and a final-output instruction. Keep that structure intact when you tune it, or your comparison runs get noisy fast.
Open this prompt inside Workspace when you want a live iteration loop.
Copy for quick reuse, or run it in Workspace to keep prompt variants, model settings, and prompt-history changes in one place.
Structured source with 1 active lines to adapt.
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Prompt content
Original prompt text with formatting preserved for inspection and clean copy.
Using your chosen PQC library (e.g., `oqs-python`), implement the core key encapsulation/decapsulation mechanism (KEM) and digital signature algorithm (DSA). Ensure key pair generation, key exchange, encryption/decryption of a simple payload, and message signing/verification functions are robust. Use CodeLlama to generate secure code snippets for these cryptographic primitives and review potential vulnerabilities.
Adaptation plan
Keep the source stable, then branch your edits in a predictable order so the next prompt run is easier to evaluate.
Hold the task contract and output shape stable so generated implementations remain comparable.
Update libraries, interfaces, and environment assumptions to match the stack you actually run.
Test failure handling, edge cases, and any code paths that depend on hidden context or secrets.
Copy once for a pristine source snapshot, then move the prompt into Workspace when you want variants, run history, and side-by-side tuning without losing the original.
Prompt diagnostics
Quick signals for how structured this prompt already is and where adaptation work is likely to happen first.
This prompt is mostly narrative and instruction-driven, so you can adapt examples and output constraints first without disturbing the structure.
Design & Implement Quantum-Resistant Secure Channel with PQC, CodeLlama & Weaviate
The rise of quantum computing poses a significant threat to current cryptographic standards, particularly public-key encryption. This challenge requires participants to design and implement a secure communication channel utilizing Post-Quantum Cryptography (PQC) algorithms, specifically focusing on key encapsulation mechanisms (KEMs) and digital signature algorithms (DSAs) that are resistant to quantum attacks. The solution should demonstrate end-to-end encrypted communication between two parties. Participants will leverage modern AI tools to accelerate development and enhance security. CodeLlama will be instrumental in generating secure code snippets for cryptographic operations and suggesting best practices. LangGraph will orchestrate the setup, key exchange, encryption, and decryption processes, ensuring a robust and fault-tolerant communication flow. A Weaviate vector database will serve as a knowledge base for PQC algorithm details, their security parameters, and known vulnerabilities, allowing the system to query and adapt to the latest PQC research. The primary goal is to build a demonstrably secure communication pathway resilient against both classical and quantum adversaries, emphasizing practical implementation over theoretical quantum mechanics. Success will be measured by the ability to establish secure communication, correctly exchange data, and integrate the specified AI tools effectively to manage the cryptographic components.
Use the challenge page to recover the original task boundaries before you tune the prompt. That keeps your variants grounded in the same evaluation target instead of drifting into a different problem.