research-pipeline

Automating the research workflow from idea discovery to paper writing involves orchestrating end-to-end stages: experiment implementation, iterative auto-review, and narrative report generation. This Skill runs that full pipeline, requiring YAML-defined arguments to configure autonomous execution and generate staged outputs like NARRATIVE_REPORT.md.

Running autonomous experiments requires cross-model code review, crash-safe execution patterns, and multi-round adversarial review. This Skill applies those guardrails during experimentation, logs the results, and produces a narrative report suitable for a target venue submission.

Auto review for research experiment code works by applying multi-round adversarial review loops and cross-model code review. This Skill uses configurable reviewer difficulty to sanity-check and validate experiment implementation iteratively before proceeding to the narrative report generation stage.

Yes, you can use a research pipeline for ArXiv paper submissions with conditional human checkpoints. This Skill supports auto-proceed logic and staged outputs, allowing you to review the IDEA_REPORT.md or experiment logs before optionally proceeding to the paper-writing handoff.

Yes, you need YAML configuration to start an end-to-end research workflow. This Skill requires YAML-defined arguments to set up multi-stage orchestration, configure reviewer difficulty, and manage the conditional human checkpoints needed for autonomous experimentation and report generation.

Limitations of autonomous research pipelines include the need for YAML-defined arguments and conditional human checkpoints to validate outputs. This Skill cannot bypass the iterative auto-review or experiment execution stages, meaning crash-safe experimental execution patterns are mandatory to prevent failure during the workflow.