cross-model-knowledge-extraction
by community
Use this page as a decision snapshot for cross-model-knowledge-extraction: trust signal, install momentum, real user feedback, and high-intent related pages you can compare next.
Description
跨模型蒸馏工程化的第一环：从 WorkBuddy 内部教师技能的 SKILL.md（或任意技能正文） 中结构化提取能力签名——工作流步骤、触发场景、已知限制/坑、工具脚本、以及可被 蒸馏的决策规则（if/when-then/必须）。输出 JSON 能力画像，直接喂给 model-distillation 的签名合成与对抗验证。纯标准库、零依赖、可本地实跑（--selftest 自带样例）。
Install cross-model-knowledge-extraction
Run this in your OpenClaw agent to add cross-model-knowledge-extraction from the ClawHub registry.
openclaw skills install cross-model-knowledge-extractionRequires ClawHub registry access. Review the security analysis below before installing.
Security Analysis
Open Source
Code is publicly available for audit.
Community Verified
Reviewed by the ClawHub community.
Community Reviews
Real user ratings only — separate from the editorial assessment and ClawHub signal.
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Frequently asked questions
Is cross-model-knowledge-extraction safe to install?
cross-model-knowledge-extraction has a SkillsReview security score of 81/100. It is open source and community-verified on ClawHub. Check the full Security Analysis on this page before installing.
How much does cross-model-knowledge-extraction cost?
cross-model-knowledge-extraction is free to install for OpenClaw via ClawHub.
What are the best alternatives to cross-model-knowledge-extraction?
You can compare cross-model-knowledge-extraction side by side with similar OpenClaw skills on the SkillsReview comparison page to find the best fit for your workflow.
How do I install cross-model-knowledge-extraction?
Install cross-model-knowledge-extraction from ClawHub at clawhub.ai/skills/cross-model-knowledge-extraction, or use the install action on this page to copy the command for your OpenClaw agent.
Community Signal
Historical movement
Timeline plus trend snapshots for security, reviews, and reputation tilt.
Trend Charts
30 / 90 / 180 day snapshots for ranking movement and security-score movement.
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