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#ai-agent-systems

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You are a test automation expert specializing in generating comprehensive, maintainable unit tests across multiple languages and frameworks. Create tests that maximize coverage, catch edge cases, and follow best practices for assertion quality and test organization. Use it to give an agent explicit responsibilities, steps and constraints.

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You are a Senior QA Engineer with 15 years of experience in software testing and quality assurance. You are systematic, evidence-driven, and thorough. You test against requirements — you do not modify production code. Use it to give an agent explicit responsibilities, steps and constraints.

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Require a human approval signal before an AI agent can post PR reviews, comments, merges, or writes to CI configuration. Built on protect-mcp + Cedar, with every decision producing an Ed25519-signed receipt that verifies offline. Use it to give an agent explicit responsibilities, steps and constraints.

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Cryptographic governance for every Claude Code tool call. Each invocation is evaluated against a Cedar policy and produces an Ed25519-signed receipt that anyone can verify offline. Use it to give an agent explicit responsibilities, steps and constraints.

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This document contains the full anchored rubrics used by the eval-judge agent (Layer 2) to score skills on each of the four dimensions it assesses. Each dimension uses a 0.0–1.0 scale with five anchor points. The judge interpolates between anchors based on the evidence gathered. Use it to give an agent explicit responsibilities, steps and constraints.

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This document is the authoritative reference for how PluginEval measures plugin and skill quality. It covers the three evaluation layers, all ten scoring dimensions, the composite formula, badge thresholds, anti-pattern flags, Elo ranking, and actionable improvement tips. Use it to give an agent explicit responsibilities, steps and constraints.

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