interpretable-attribution@1.0.0
CAUTION· static onlyPublisher claims: "可解释归因引擎（model-agnostic）。针对任意模型/规则在单个样本上的预测，给出 "为什么"：全局排列重要性、局部特征消融、最少特征反事实翻转、自然语言决策理由。 超越黑箱输出，给出特征级归因，是"s可解释归因"前沿认知能力的实体化落地。" Observed in sandbox: static analysis only — behavior not yet observed.
Coverage
Static only; nothing was run yet.The label rests on the code, the instruction files and the publisher. The honeypot run replaces this verdict when it completes.
Is [email protected] safe to install?
KillerSkills analysed it statically (a sandbox run is pending) on 2026-11-09 and scored it 71/100 (Caution). It is not known to be malicious, but it has behaviour or code a security policy should decide on before install. Summary: static analysis only — behavior not yet observed.
Behavior trace
Queued for honeypot run · static analysis onlyThis version has not been detonated yet. The score above counts behavior as unknown (50/100), so it cannot read Verified until the honeypot run completes and replaces this verdict.
Static findings
No findings across 435 lines in 5 files.
Cost to the agent
Tokens estimated at four characters each. Skills load their instruction text when triggered; the figure is what that costs.
Known vulnerabilities
Vulnerability advisories (OSV) cover npm and PyPI packages; this registry has no advisory database.
What would make this Verified
9 points to 80 · weights: behavior 45%, static 25%, prompt 15%, publisher 15%- Unclaimed publisher≈ +8 on the totalClaim qq435912743: a verified publisher gets +50 on publisher trust. It is free and usually instant with GitHub sign-in.