resource-grounded-coding

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

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

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

Review policy, evaluations, cost, lineage, and risk before recording a human promotion or rejection decision.

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

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

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

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

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

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.