human-in-the-loop-promotion

Sample successful and failed historical ClimbHill Attempts before planning new work.

Prepare a repository for controlled recursive optimization by defining goals, agent guidance, policies, evaluations, resources, and protected surfaces.

Classify proposed paths and enforce denied or approval-required ClimbHill policy before edits.

Ground implementation decisions in recorded ClimbHill resources and cited prior work.

Recombine proven parts of multiple evaluated attempts into a coherent follow-up attempt.

Convert evidence-backed ClimbHill experiment findings into reviewable GitHub issue proposals.

Analyze ClimbHill runs, attempts, lineage, costs, failures, and recurring constraints.

Produce evidence-backed run and attempt reports with evaluations, costs, risks, and promotion recommendations.

Coordinate independent, bounded coding attempts against one goal and compare them after policy and evaluation checks.

Run Perplexity Sonar and Sonar Deep Research for web-grounded answers with citations and bounded search context.

Build and validate custom-corpus inputs for deep research from Google Docs, YouTube, local files, S3, arXiv, databases, Slack, email, tickets, and similar…

Run Exa-backed deep research, neural search, and page-content retrieval with bounded result and page-read counts.