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LLM Evaluation Toolkit(LLM评测工具链)

LLM 评测工具链(可运行实现)——把评测方法论变成能直接跑的本地引擎:评测集管理(JSONL 建集/质量检查/规模统计)、幻觉检测引擎(数字一致性/引用校验/否定矛盾/关键论断互证四类规则检测)、RAG 指标计算(RAGAS 四指标的本地简化实现:忠实度/答案相关性/上下文精度/上下文召回)、回归对比(基线 vs 新结果差异判定)、报告生成与上线门禁(分场景得分/门禁判定/报告输出)。零依赖纯标准库,本地闭环不联网。与「LLM 质量评测」(方法论)互补——那个讲怎么做,这个给能跑的实现。面向 AI 工程师、测试与质量负责人。

Identity and source

Publisher attributionzhaoxinghua09-cellregistry owner unverified by skillvetai
Functional categoryAgent Engineering, Security & Governanceautomatically inferred · 54% rule confidence
Package forminstruction with code19 recorded files
Canonical sourceClawHub registryclawhub:zhaoxinghua09-cell:ai-eval-toolkit
Open canonical source ↗

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OpenClawnative officialProvenance: registry distribution

Independent structural checks

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Claude Codepasses structure
Checker 0.1.0 · agent-skills-2026-08-13+claude-code-docs-2026-08-13 · 8/27/2026.claude/skills/ai-eval-toolkit

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

OpenAI Codexpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+codex-docs-2026-08-13 · 8/27/2026.agents/skills/ai-eval-toolkit

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

OpenClawpasses structure
Checker 0.1.0 · agent-skills-2026-08-13+openclaw-docs-2026-08-13 · 8/27/2026skills/ai-eval-toolkit

Runtime, accounts, dependencies, permissions, network behavior and task quality remain untested.

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clawhub install @zhaoxinghua09-cell/ai-eval-toolkit
clawhub inspect @zhaoxinghua09-cell/ai-eval-toolkit --version 1.0.0

Security evidence

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Status
completed
Coverage
full text content
Files
18 / 19 inspected as text
Checked
8/27/2026, 10:18:59 PM
Scanner
0.1.3
Policy
1.0.3
3 inferred permission indicators
  • shell execution — automatically inferred
  • network access — automatically inferred
  • filesystem write — automatically inferred
2 dependency and API indicators
  • api: clawhub.ai
  • api: www.w3.org
External clawhub result: clean

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Evidence checked
8/27/2026, 5:51:09 PM
Release binding
Matches this record
  • vt: clean
  • skillspector: suspicious
  • llm: clean

Recorded files

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Package content hashsha256:dea7c7d9fb7c4c823a3903e036cc16ac8217de359a905dfbdc6749f50cfd8ce3
Show up to 19 recorded paths
  • ATTESTATION.md
  • LICENSE.md
  • manifest.json
  • references/01-工具链全景.md
  • references/02-评测集管理.md
  • references/03-幻觉检测引擎.md
  • references/04-RAG指标计算.md
  • references/05-回归对比.md
  • references/06-报告与门禁.md
  • references/07-与平台工具衔接.md
  • references/08-FAQ.md
  • SECURITY_AUDIT.md
  • skill-card.md
  • SKILL.md
  • tools/eval_toolkit.py
  • verify/eval_toolkit_quality_test.py
  • verify/gen_security_radar.py
  • verify/security_results.json
  • verify/security-radar.svg

Source changelog

v1.0.0: runnable LLM evaluation toolkit (zero-dependency, pure stdlib, local offline) - dataset management engine (JSONL init/quality check/statistics), hallucination detection engine (four rule-based detectors, numeric consistency, citation validation, negation contradiction, key-claim cross-checking), RAG metric engine (local simplified RAGAS, faithfulness/answer relevance/context precision/context recall), regression comparison engine (baseline vs new diff), report and launch-gate engine (per-scenario scores, gate decision); complements the LLM quality evaluation methodology playbook