v1.0.2

Stock AI exposure analysis for investing

BenignClawScan verdict for this skill. Analyzed May 1, 2026, 6:19 AM.

Analysis

This appears to be a coherent public-data stock analysis skill with no evidence of credential use, data exfiltration, persistence, or destructive actions.

GuidanceBefore installing, confirm you are comfortable with the agent browsing public financial sources and optionally running local Python helpers. Treat any buy, avoid, or short-style output as research, not as standalone investment advice.

Findings (3)

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.

Abnormal behavior control

Checks for instructions or behavior that redirect the agent, misuse tools, execute unexpected code, cascade across systems, exploit user trust, or continue outside the intended task.

Tool Misuse and Exploitation

SeverityLowConfidenceHighStatusNote

README.md

**Web access** — The agent uses `web_search` and `web_fetch` to retrieve SEC filings, earnings transcripts, and patent data

The skill expects broad web retrieval from public sources. This is necessary for the stated financial research workflow, but users should be aware the agent will choose and fetch external sources.

User impactThe agent may browse multiple public financial and patent sources to build the report, so source quality can affect the analysis.

RecommendationReview the cited sources in the final report and prefer official filings, investor-relations pages, and reputable data providers.

Agentic Supply Chain Vulnerabilities

SeverityLowConfidenceHighStatusNote

requirements.txt

pandas>=2.0.0
openpyxl>=3.1.0

The dependency versions are lower-bound only rather than pinned. The artifacts do not show automatic dependency installation, but this is still relevant if a user chooses to run the helper scripts.

User impactIf dependencies are installed manually, the exact package versions may vary over time.

RecommendationInstall dependencies from trusted package indexes and consider pinning versions in a local environment before running the Python helpers.

Human-Agent Trust Exploitation

SeverityInfoConfidenceHighStatusNote

references/scoring_calculations.md

AI Fortified ... Strong buy signal ... AI Endangered ... Short candidate ... Avoid / reduce

The framework can produce investment-action language. This is disclosed and purpose-aligned, but it can influence financial decisions if treated as authoritative.

User impactUsers could place too much confidence in the generated investment labels or trading suggestions.

RecommendationUse the output as one research input, verify assumptions and data independently, and consult qualified financial advice for investment decisions.