Use this to map a specific component or phase of a neural network (e.g., "training phase", "gradient calculation")
These states come from the source or distribution context. None of the entries below are SkillVetAI compatibility test results.
These checks parse the fixed package against dated platform rules. They do not execute the Skill or verify task behavior.
.claude/skills/<skill-name>skill.mdRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
.agents/skills/<skill-name>skill.mdRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
skills/<skill-name>skill.mdRuntime, accounts, dependencies, permissions, network behavior and task quality remain untested.
This command is recorded from the source ecosystem and resolves the registry's latest release. The fixed release shown on this page should be inspected before adoption.
clawhub install @3mper0rr/ai-nn-vectormappingclawhub inspect @3mper0rr/ai-nn-vectormapping --version 1.0.0This automated, non-executing scan is bound to this release hash. It is not a safety certification and may contain false positives or false negatives.
missing frontmatterThis is registry-supplied evidence for the recorded release, not an independent SkillVetAI scan. Check the canonical source for the full report, scanner versions, scope, and current moderation state.
The catalog stores hashes and an inventory summary for change detection. It does not republish the package contents.
sha256:97293521a31ab49ebbe224fb28f6f121ed91ad98db5814f1a34b0e62f9c76648skill-card.mdskill.mdInitial release of ai-nn-vectormapping skill. - Maps neural network components/phases to well-known adversarial attack vectors from literature. - Considers multiple attack categories: evasion (inference), poisoning (training), extraction/inversion, and gradient attacks. - Provides concise, actionable output: attack vectors, exploitation mechanisms, and recommended mitigations. - Designed for Red Teaming, adversarial ML, and threat modeling use cases. - Focuses strictly on neural network-level attack mechanics for defensive testing.