in-the-loop-labs
Official@in-the-loop-labs
Offers specialized interfaces for iterative code review, pull request management, and multi-model reasoning orchestration within development environments.
Agent Skills by in-the-loop-labs
Showing 10 vetted skills indexed across 1 GitHub repositories.
in-the-loop-labsreview-requests
Open pending GitHub pull requests in the pair-review web UI with auto-analysis.
in-the-loop-labsai-critic
Fetches and applies AI-generated code review suggestions from pair-review.
in-the-loop-labslocal
Open uncommitted local code changes in the pair-review web UI.
in-the-loop-labspr
Open the current branch's GitHub pull request in the pair-review web UI.
in-the-loop-labsuser-critic
Fetch pair-review comments and apply code changes in local or PR contexts.
in-the-loop-labsanalyze
Analyze code via the pair-review MCP server with configurable tiers.
in-the-loop-labsUpdate Provider Models
Update AI provider model configurations in pair-review source code files.
in-the-loop-labsloop
Orchestrate iterative code implementation, AI analysis, and issue fixing cycles.
in-the-loop-labsreview-roulette
Dispatch code review tasks to three random reasoning models and merge attributed summaries.
in-the-loop-labsreview-model-guidance
Guide AI model selection for code review tasks by complexity and cost.
Frequently Asked Questions About in-the-loop-labs
FAQPage SchemaWhat specific tasks are enabled by in-the-loop-labs?▼
These capabilities enable developers to open pending GitHub pull requests, analyze uncommitted local changes, and orchestrate iterative code implementation cycles. Users can dispatch review tasks to multiple reasoning models and apply generated suggestions directly to local source code or active pull request contexts.
Which personas benefit from these development capabilities?▼
Software engineers and technical leads focused on code quality and peer review efficiency benefit from these capabilities. The system is designed for developers who require structured, multi-model analysis of pull requests and local code changes to streamline the feedback loop during the software development lifecycle.
What are the prerequisites for running these review cycles?▼
Execution requires an active connection to the pair-review MCP server and configured access to GitHub repositories. Users must maintain valid provider model configurations within their source code files to enable the dispatching of review tasks and the application of suggested code modifications.