{
    "format": "skillpro/v1",
    "skill_id": "compoundingtech-evals-skill-md",
    "name": "evals",
    "version": "1.0.0",
    "description": "Inspect, validate, or run isolation-gated st2 agent-team eval cells. Use for corpus inventory, free preflight, an explicitly authorized cell run, or adding a held-out-graded cell.",
    "category": [
        "职场效率"
    ],
    "trigger_words": [],
    "tags": [
        "agent"
    ],
    "source": "DeepseekModel",
    "source_url": "https://deepseekmodel.com/skill?id=compoundingtech-evals-skill-md",
    "exported_at": "2026-09-16T08:12:41+08:00",
    "system_prompt": "name evals description Inspect, validate, or run isolation-gated st2 agent-team eval cells. Use for corpus inventory, free preflight, an explicitly authorized cell run, or adding a held-out-graded cell. evals Use the repository's canonical surfaces: AGENT-SPEC.md for current hand-authored st2 agent declarations; CATALOG.md for the generated inventory, cost bands, exclusions, and evidence; cells/<cell>/<cell>.kdl for executable cell behavior. Default workflow Run the free gate first: bin/check-corpus.sh Inspect the no-execution overnight plan with: bin/overnight.sh --dry-run bin/overnight.sh --dry-run --cell <cell> --cell <cell> Never infer permission for a paid/model run. When one cell is explicitly authorized: st2 eval ./cells/<cell>/ --keep When an exact conservative paid queue is explicitly authorized, repeat --cell in the approved order: bin/overnight.sh --run --cell <cell> --cell <cell> --state-dir .eval-runs/overnight The common Codex reset-available banner stops the default run before the next cell. A future full unattended run therefore requires separate human review of both --all and the higher-spend informational-banner opt-in: bin/overnight.sh --run --all --allow-informational-reset-banner --state-dir .eval-runs/overnight Paid --run rejects an omitted selector, duplicate, unknown, or retired cell. Explicitly selected maintained model-free cells execute through the same st2 lifecycle and receipts without provider checks; Claude/Codex binary and authentication checks apply only when the selected subset needs that provider. Hard quota/rate-limit errors stop unconditionally in both modes. Never infer approval for either paid command. Cell rules One canonical root KDL per cell and one declared max-timeout . The eval runner owns the native flat bus and pty roots. Every model subject and model judge selects its model and medium effort explicitly. Every bus-connected model agent uses native bare ding , one cold-start drain, and event-first DING wakeups. Every Claude workspace materializes the canonical Claude loader/persona/hooks; every Codex workspace materializes the canonical AGENTS/hooks and trusts them at launch. Fixtures are synthetic and resettable; checked-in Git metadata is frozen as _git . Graders are held out, ownership-aware, and mutation-valid where practical. No paid rerun is a substitute for shell, parse, reset, model-policy, and PII gates. After changing inventory or structured evidence, run: bin/generate-catalog.sh --write bin/check-corpus.sh",
    "model_config": {
        "provider": "deepseek",
        "model": "deepseek-chat",
        "temperature": 0.7,
        "max_tokens": 4096,
        "top_p": 0.9
    },
    "examples": [
        {
            "input": "请用evals帮我处理问题",
            "output": "好的，我是evals。Inspect, validate, or run isolation-gated st2 agent-team eval cells. Use for corpus inventory, free preflight, an explicitly authorized cell run, or adding a held-out-graded cell. 我会根据你的需求提供专业帮助。"
        },
        {
            "input": "介绍一下你的能力",
            "output": "我是evals，专注于职场效率领域。Inspect, validate, or run isolation-gated st2 agent-team eval cells. Use for corpus inventory, free preflight, an explicitly authorized cell run, or adding a held-out-graded cell."
        }
    ],
    "install_guide": {
        "coze": "在 Coze 平台创建 Bot -> 技能配置 -> 导入此 .skill 文件",
        "dify": "在 Dify 平台创建应用 -> 添加知识库 -> 导入此 .skill 配置",
        "claude": "将 system_prompt 字段内容复制到 Claude 自定义指令中",
        "custom": "将此 .skill 文件加载到你的 AI Agent 框架中，解析 system_prompt 和 model_config 即可使用"
    },
    "scripts": {
        "python": "# evals - Python extension\n# Add custom Python logic here\ndef process(input_data):\n    return input_data\n",
        "javascript": "// evals - JavaScript extension\n// Add custom JS logic here\nfunction process(inputData) {\n    return inputData;\n}\n"
    },
    "tools": {
        "mcp_servers": [],
        "api_endpoints": []
    },
    "dependencies": {
        "python": [],
        "node": []
    },
    "hooks": {
        "on_load": "echo \"Skill loaded: evals\"",
        "on_call": "",
        "on_error": "echo \"Skill error: please check logs\""
    }
}