Core Refinery

Security checks across static analysis, malware telemetry, and agentic risk

Overview

Core Refinery is an instruction-only synthesis skill whose main caution is that user-provided source material may be processed by the configured model and reflected in reusable summaries.

This skill appears safe for its stated purpose. Before installing or using it, consider whether the sources you provide are allowed to be processed by your configured model provider, and review any synthesized or shareable outputs before treating them as canonical or sharing them publicly.

Static analysis

No static analysis findings were reported for this release.

VirusTotal

64/64 vendors flagged this skill as clean.

View on VirusTotal

Risk analysis

Artifact-based informational review of SKILL.md, metadata, install specs, static scan signals, and capability signals. ClawScan does not execute the skill or run runtime probes.

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ASI07: Insecure Inter-Agent Communication
Low
What this means

Private or proprietary source material provided for synthesis could be sent to the user's configured cloud model provider.

Why it was flagged

The skill may process the user's supplied sources through the model provider configured for the agent. This is disclosed and purpose-aligned, but matters for confidential inputs.

Skill content
If your agent uses a cloud-hosted LLM (Claude, GPT, etc.), data is processed by that service as part of normal agent operation.
Recommendation

Use only sources that are allowed under your model/provider policy, or use a local/private model or redact sensitive material before synthesis.

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ASI06: Memory and Context Poisoning
Low
What this means

A synthesized summary could preserve mistakes, biases, or confidential details from the input sources if reused without review.

Why it was flagged

The skill encourages reuse of synthesized conclusions as canonical material. This is central to the skill and it includes caveats, but users should review outputs before relying on them.

Skill content
"Use Golden Master candidates as your canonical source"
Recommendation

Treat Golden Master outputs as candidates, review them against the original sources, and avoid sharing or reusing them where confidential source-derived content would be inappropriate.