Run external LLM reviews across Gemini and OpenAI with consistent JSON outputs.
npx skills add https://github.com/TeamSPWK/nova-algorithm --skill llm-review-teamspwk
Please help me install this Agent Skill. Skill: llm-review Source: https://github.com/TeamSPWK/nova-algorithm/tree/main/skills/llm-review Command: npx skills add https://github.com/TeamSPWK/nova-algorithm --skill llm-review-teamspwk
Orchestrates and standardizes external LLM reviews across Gemini and OpenAI to ensure consistent outputs and traceable reasoning.
Prompt the system to compare inputs using Gemini and GPT and return a unified JSON result.
High-intent search queries and answers about installing and using this skill.
To standardize JSON output across Gemini and OpenAI, a wrapper enforces fixed model mappings and consistent output fields. This ensures uniform API responses and traceable reasoning across different providers for evaluation tasks.
Yes, fixed model configurations can be enforced for code review to prevent agent modification. This approach promotes reproducibility by standardizing the external LLM review process and ensuring the evaluation logic remains controlled.
The best way to handle API errors during multi-LLM reviews is implementing standardized error handling and retry policies within your wrapper. This maintains reliability when querying Gemini and OpenAI for structured evaluation tasks.
Yes, multi-LLM review supports content auditing by reading optional sources to enrich prompts. This allows deeper regulatory thinking and auditable reasoning while maintaining a consistent JSON output format across providers.
Your LLM wrapper returns inconsistent results across providers due to varying model behaviors and lack of enforced output structures. Implementing fixed model mappings and standardized JSON output fields resolves this by enforcing uniform response schemas.
You need a unified wrapper for external LLM reviews when performing structured evaluation tasks like code review, regulatory thinking, or content auditing. It ensures consistent JSON outputs, error handling, and reproducible reasoning across Gemini and OpenAI.
leonanpereirapintoExtract structured, source-grounded data from unstructured text into JSONL outputs.
Bbar0n234Enforce predefined Pydantic schemas and constrained decoding for structured LLM reasoning.
D3HunterFormat code review findings into severity-sorted JSON with rubric metadata.
zxc1a1a1Standardize LLM provider integrations into a unified contract for requests, responses, streaming, and errors.
