ai-cli

You can consolidate multiple LLM CLI outputs by running parallel invocations of models like qwen, gemini, and codex, then aggregating them into a unified transcript. The ai-cli Skill routes queries via keyword rules and merges results automatically.

Cross-model validation compares responses from multiple LLMs to ensure consistency. An aggregated LLM output works by running CLIs in parallel, combining their responses, and optionally applying a critic subagent to evaluate confidence and evidence.

You route queries to specific LLM CLIs using config-driven defaults and keyword-based routing rules. This allows the aggregation layer to select appropriate CLIs like qwen or gemini dynamically based on your query intent.

Yes, you need pyyaml and python-dotenv dependencies to aggregate CLI-backed LLM responses. These libraries support config-driven defaults and environment variable loading required for routing rules and parallel CLI execution.

Yes, you can evaluate aggregation confidence using an optional ai-cli-critic subagent. This quality gate evaluates parallel LLM outputs for confidence, consistency, and evidence before returning the final consolidated JSON output.

Limitations of parallel LLM CLI invocation include dependency on external CLIs like opencode and codex, requiring keyword-based routing rules for selection. Consolidated results depend on the availability and response quality of the configured backend models.