Implement LLMValidator as a cost-aware gate for trading signals.
npx skills add https://github.com/bitandbytes/Argus --skill llm-validator
Please help me install this Agent Skill. Skill: llm-validator Source: https://github.com/bitandbytes/Argus/tree/main/.claude/skills/llm-validator Command: npx skills add https://github.com/bitandbytes/Argus --skill llm-validator
The LLM Validator provides a structured, cost-aware gate that analyzes qualitative risks for actionable signals, ensuring only vetted signals reach risk management and execution.
Run the LLM Validator in your trading pipeline to assess a high-confidence signal before execution.
High-intent search queries and answers about installing and using this skill.
You add an LLM validation gate by wiring a SignalFilter between the meta-model and risk manager, using deterministic OpenAI calls with a fixed JSON schema to vet trading signals before they reach execution.
You control OpenAI API costs by implementing per-ticker and per-day caching alongside token-cost logging, minimizing redundant API usage and tracking expenses for trading signal validation.
You test an OpenAI trading signal filter without real API calls by using mocked OpenAI responses to verify the deterministic JSON schema parsing and integration points in your pipeline.
An LLM validator for trading signals requires context curation including news context, earnings data, sector information, and date to properly assess qualitative risks before risk management.
Yes, you can integrate an LLM validator into an existing trading pipeline by creating a plugin-compatible class with clear integration points between the meta-model and risk manager, ensuring structured signal vetting.
You tune the threshold for a trading signal filter by adjusting the deterministic OpenAI prompt design and evaluating mocked responses to ensure only high-confidence, vetted signals pass through to risk management.
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