US Stock Analyst by leading AI LLM models with Bloomberg Data, Twitter Sentiment and Wall Street Equity Research Reports
PassAudited by VirusTotal on May 12, 2026.
Overview
Type: OpenClaw Skill Name: openclaw-aisa-us-stock-analyst Version: 1.0.1 The OpenClaw AgentSkills skill bundle for 'us-stock-analyst' is benign. The Python scripts (`stock_analyst.py`, `basic_analysis.py`, `batch_analysis.py`, `deep_analysis.py`, `test_api_data.py`) and documentation (`SKILL.md`, `README.md`, `TEST_REPORT.md`) are consistent with the stated purpose of providing stock analysis using the AIsa API. The skill correctly retrieves an API key from environment variables, makes HTTP requests to the documented `api.aisa.one` endpoints, and saves analysis reports to the local filesystem, all for legitimate purposes. There is no evidence of data exfiltration to unauthorized endpoints, malicious execution, persistence mechanisms, obfuscation, or prompt injection attempts against the OpenClaw agent itself. The prompts constructed for external LLMs are for legitimate analytical tasks, not for subverting the agent.
Findings (0)
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.
Running analyses can use the user's AIsa credits and expose requested tickers or portfolio queries to the AIsa service.
The skill uses the user's AISA_API_KEY as a bearer credential for AIsa API requests; this is expected for the service but grants access to a paid provider account.
"Authorization": f"Bearer {api_key}"Use a dedicated or limited AIsa key if available, monitor usage and remaining credits, and revoke the key if you stop using the skill.
Large portfolios, repeated runs, or deep analysis can make many paid API/LLM calls.
The analysis workflow fans out to multiple external API calls, especially in standard and deep modes; this matches the purpose but affects cost and rate limits.
tasks.append(("financial_metrics", self._get_financial_metrics(ticker))) ... tasks.append(("twitter", self._get_twitter_data(ticker))) ... if depth == "deep":Confirm the requested tickers, depth, and batch size before running, and review usage/cost returned by the provider.
Installing dependencies could pull newer package versions than the author tested.
The documented Python dependencies are installed from package sources and are not fully pinned; this is normal for a Python example package but leaves dependency provenance to the user.
httpx>=0.24.0 asyncio
Install in a virtual environment and consider pinning or reviewing dependency versions before use.
Stock queries, portfolio tickers, and gathered analysis context may be sent to AIsa and potentially downstream LLM providers.
The skill discloses that analysis can be routed through an AIsa LLM gateway and multiple model providers; this is purpose-aligned but is an external provider data flow.
LLM Gateway (OpenAI Compatible) ... Supported Models: GPT-4, GPT-4 Turbo ... Claude 3 Opus ... Gemini 1.5 Pro
Avoid submitting sensitive portfolio details unless you are comfortable with AIsa's and any downstream providers' data policies.
Users may overestimate the provenance or professional status of the generated investment analysis.
The title advertises Bloomberg Data and Wall Street research, while the provided implementation and API examples primarily show AIsa/MarketPulse, web, social, YouTube, and LLM endpoints.
Name: US Stock Analyst by leading AI LLM models with Bloomberg Data, Twitter Sentiment and Wall Street Equity Research Reports
Verify the actual data sources and treat the output as informational rather than as licensed financial advice.