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Implementing Repository Harness for AI Agents
harness-engineering
github/awesome-copilot
376
This skill enables the creation of a robust 'harness' system for coding agents within a repository. It addresses the need to prevent repeated AI-generated coding mistakes by implementing durable instructions, guardrails, regression tests, and failure memory. It treats the target repository as the source of truth, ensuring consistency and maintaining high reliability across all automated contributions.
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Assessing LLM System Prompt Leakage Risks
testing-for-system-prompt-leakage
mukul975/Anthropic-Cybersecurity-Skills
429
This guide provides a structured approach to red-teaming Large Language Models (LLMs) to detect sensitive data leakage from system prompts. It simulates advanced attacks—including prompt injection, instruction override, and encoding tricks—to identify embedded secrets, API keys, and proprietary business logic. Essential for validating adherence to OWASP LLM07 and securing AI applications against data exfiltration.
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