Dynamic code execution
- Finding
- Dynamic code execution detected.
Security checks across static analysis, malware telemetry, and agentic risk
This appears to be a finance signal-tracking helper with purpose-aligned market-data tools and local context use, but users should review its provenance, dependencies, and persisted analysis state before relying on it.
This skill is reasonable for tracking financial signals, but treat its outputs as research assistance rather than investment advice. Before installing or running helper code, verify the publisher, review the Python files and dependencies, and understand where any signal database or RAG context will be stored and reused.
VirusTotal findings are pending for this skill version.
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.
The agent may query external data tools and base investment-signal updates on their results.
The workflow delegates research to other search and stock-data skills. This is central to the stated finance-tracking purpose, but users should expect external tool calls and untrusted market/news inputs.
Use `alphaear-search` and `alphaear-stock` skills to gather the necessary data.
Verify important facts and market data before acting on the analysis, especially for investment decisions.
It may be harder to verify who maintains the code or how its dependencies should be installed safely.
The skill has sparse provenance/setup metadata while shipping Python code and referencing dependencies. The artifacts do not show hidden installation or auto-run behavior, but the origin and dependency setup are not well documented.
Source: unknown; Homepage: none; Install specifications: No install spec — this is an instruction-only skill; Code file presence 34 code file(s)
Install only if you trust the publisher; inspect the included scripts and pin/verify any dependencies before running helper code.
Prior generated content may influence later financial reports or signal updates.
The artifacts show RAG retrieval of previously generated report content. This is purpose-aligned for report continuity, but persisted context can carry stale or incorrect information into later analysis.
你拥有 RAG 搜索工具,可以检索已生成的章节内容以确保逻辑连贯性。
Review or clear stored context when analyses become stale, and do not treat retrieved prior content as automatically correct.