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EvaluationExperimental

hermes-skilleval

by Raidriar7170

Verification-gated skill routing and self-improvement harness for agent skills

Python
Updated Jul 21, 2026
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What It Does

Implements a verification-gated skill routing and self-improvement harness for Hermes-style agent skills. Routes candidate skills through reranking and verification gates before execution, then collects outcomes to drive iterative skill refinement. Focuses on gating risky or low-quality skill calls and producing measurable signal for skill-level improvement. Includes Evaluation-Driven Development (EDDOps) practices to drive continuous improvement.

The Value Proposition

As agents compose specialist skills, it's easy for bad or unverified skills to degrade system behaviour — you need a way to gate and measure skill-level reliability. This harness provides A2A evaluation patterns and continuous feedback so teams can build an agent track record and improve skills over time. It shifts evaluation closer to deployment by making verification and reranking first-class parts of the skill pipeline. For robust gating, consider the Defense in Depth Pattern.

When to Use

Researchers and engineering teams validating and iterating Hermes-style agent skills who need gated execution and continuous evaluation of skill reliability. Consider aligning with the Model Context Protocol (MCP) Pattern for consistent context handling during evaluation and routing.

Applications

  • Gate candidate skills before execution to prevent low-quality or risky behavior
  • Continuously evaluate and rerank skills to build a reputation-like track record
  • Collect structured outcomes for automated skill self-improvement and CI release-gates
Topics
agent-evaluationbenchmarkcillm-agentspython-clirelease-gatererankingretrievalskill-routing
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Keywords
A2A evaluationmulti-agent trustagent track recordskill routing