Use when the user wants an autonomous ML research loop that explores the space broadly rather than hill-climbing one approach. A temperature scheduler replaces the usual hypothesis step: it forces several wild, diverse swings (full rewrites, different architectures/regimes) early, then enters an adaptive phase that…
Use when the user wants an autonomous ML research loop that does more than blindly try changes. After every training run the agent analyses what actually happened inside the model — gradients, activations, embeddings, errors, data — and grounds the next change in that evidence. A on/off dial adds scientific-literature…
Use when the user has a messy tabular data dump (CSV/TSV/parquet/Excel/JSON) and wants it iteratively cleaned to an inferred data contract — a checklist of deterministic pass/fail checks, not a quality score. A single agent profiles the table, synthesizes a per-column contract compiled into binary checks (types…
Set up, scaffold, and administer agentic loops in Claude Code — headless while-loops around claude -p, evaluator-optimizer (generator/critic) loops, meta / prompt-refinement loops (a loop that refines the prompts another loop runs), and orchestrator fan-out. Every loop ships with non-negotiable guardrails: a…
Use when the user wants to "set up a loop", build a self-running or run-until-done agent, automate a recurring engineering task (triage, keep-CI-green, dependency bumps, backlog burndown), or stop hand-prompting an agent turn by turn. Sets up an autonomous engineering loop in the current project. It discovers work…