{
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    "name": "Agent Evaluation",
    "source": "tencent",
    "type": "skill",
    "category": "开发工具",
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    "canonicalUrl": "https://clawhub.ai/rustyorb/agent-evaluation",
    "targetPlatform": "OpenClaw"
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    "sourcePlatform": "tencent",
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    "quickSetup": [
      "Download the package from Yavira.",
      "Extract the archive and review SKILL.md first.",
      "Import or place the package into your OpenClaw setup."
    ],
    "agentAssist": {
      "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
      "steps": [
        "Download the package from Yavira.",
        "Extract it into a folder your agent can access.",
        "Paste one of the prompts below and point your agent at the extracted folder."
      ],
      "prompts": [
        {
          "label": "New install",
          "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
        },
        {
          "label": "Upgrade existing",
          "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
        }
      ]
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      },
      "scope": "source",
      "summary": "Source download looks usable.",
      "detail": "Yavira can redirect you to the upstream package for this source.",
      "primaryActionLabel": "Download for OpenClaw",
      "primaryActionHref": "/downloads/agent-evaluation"
    },
    "validation": {
      "installChecklist": [
        "Use the Yavira download entry.",
        "Review SKILL.md after the package is downloaded.",
        "Confirm the extracted package contains the expected setup assets."
      ],
      "postInstallChecks": [
        "Confirm the extracted package includes the expected docs or setup files.",
        "Validate the skill or prompts are available in your target agent workspace.",
        "Capture any manual follow-up steps the agent could not complete."
      ]
    },
    "downloadPageUrl": "https://openagent3.xyz/downloads/agent-evaluation",
    "agentPageUrl": "https://openagent3.xyz/skills/agent-evaluation/agent",
    "manifestUrl": "https://openagent3.xyz/skills/agent-evaluation/agent.json",
    "briefUrl": "https://openagent3.xyz/skills/agent-evaluation/agent.md"
  },
  "agentAssist": {
    "summary": "Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.",
    "steps": [
      "Download the package from Yavira.",
      "Extract it into a folder your agent can access.",
      "Paste one of the prompts below and point your agent at the extracted folder."
    ],
    "prompts": [
      {
        "label": "New install",
        "body": "I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete."
      },
      {
        "label": "Upgrade existing",
        "body": "I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run."
      }
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    "primaryDoc": "SKILL.md",
    "sections": [
      {
        "title": "Agent Evaluation",
        "body": "You're a quality engineer who has seen agents that aced benchmarks fail spectacularly in\nproduction. You've learned that evaluating LLM agents is fundamentally different from\ntesting traditional software—the same input can produce different outputs, and \"correct\"\noften has no single answer.\n\nYou've built evaluation frameworks that catch issues before production: behavioral regression\ntests, capability assessments, and reliability metrics. You understand that the goal isn't\n100% test pass rate—it"
      },
      {
        "title": "Capabilities",
        "body": "agent-testing\nbenchmark-design\ncapability-assessment\nreliability-metrics\nregression-testing"
      },
      {
        "title": "Requirements",
        "body": "testing-fundamentals\nllm-fundamentals"
      },
      {
        "title": "Statistical Test Evaluation",
        "body": "Run tests multiple times and analyze result distributions"
      },
      {
        "title": "Behavioral Contract Testing",
        "body": "Define and test agent behavioral invariants"
      },
      {
        "title": "Adversarial Testing",
        "body": "Actively try to break agent behavior"
      },
      {
        "title": "⚠️ Sharp Edges",
        "body": "IssueSeveritySolutionAgent scores well on benchmarks but fails in productionhigh// Bridge benchmark and production evaluationSame test passes sometimes, fails other timeshigh// Handle flaky tests in LLM agent evaluationAgent optimized for metric, not actual taskmedium// Multi-dimensional evaluation to prevent gamingTest data accidentally used in training or promptscritical// Prevent data leakage in agent evaluation"
      },
      {
        "title": "Related Skills",
        "body": "Works well with: multi-agent-orchestration, agent-communication, autonomous-agents"
      }
    ],
    "body": "Agent Evaluation\n\nYou're a quality engineer who has seen agents that aced benchmarks fail spectacularly in production. You've learned that evaluating LLM agents is fundamentally different from testing traditional software—the same input can produce different outputs, and \"correct\" often has no single answer.\n\nYou've built evaluation frameworks that catch issues before production: behavioral regression tests, capability assessments, and reliability metrics. You understand that the goal isn't 100% test pass rate—it\n\nCapabilities\nagent-testing\nbenchmark-design\ncapability-assessment\nreliability-metrics\nregression-testing\nRequirements\ntesting-fundamentals\nllm-fundamentals\nPatterns\nStatistical Test Evaluation\n\nRun tests multiple times and analyze result distributions\n\nBehavioral Contract Testing\n\nDefine and test agent behavioral invariants\n\nAdversarial Testing\n\nActively try to break agent behavior\n\nAnti-Patterns\n❌ Single-Run Testing\n❌ Only Happy Path Tests\n❌ Output String Matching\n⚠️ Sharp Edges\nIssue\tSeverity\tSolution\nAgent scores well on benchmarks but fails in production\thigh\t// Bridge benchmark and production evaluation\nSame test passes sometimes, fails other times\thigh\t// Handle flaky tests in LLM agent evaluation\nAgent optimized for metric, not actual task\tmedium\t// Multi-dimensional evaluation to prevent gaming\nTest data accidentally used in training or prompts\tcritical\t// Prevent data leakage in agent evaluation\nRelated Skills\n\nWorks well with: multi-agent-orchestration, agent-communication, autonomous-agents"
  },
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    "sourceLabel": "tencent",
    "provenanceUrl": "https://clawhub.ai/rustyorb/agent-evaluation",
    "publisherUrl": "https://clawhub.ai/rustyorb/agent-evaluation",
    "owner": "rustyorb",
    "version": "1.0.0",
    "license": null,
    "verificationStatus": "Indexed source record"
  },
  "links": {
    "detailUrl": "https://openagent3.xyz/skills/agent-evaluation",
    "downloadUrl": "https://openagent3.xyz/downloads/agent-evaluation",
    "agentUrl": "https://openagent3.xyz/skills/agent-evaluation/agent",
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}