Scan Python/ML codebases to generate program.md run specs with metric and guard configurations.
npx skills add https://github.com/Borda/AI-Rig --skill plan-borda
Please help me install this Agent Skill. Skill: plan Source: https://github.com/Borda/AI-Rig/tree/main/plugins/research/skills/plan Command: npx skills add https://github.com/Borda/AI-Rig --skill plan-borda
This Skill provides an interactive wizard that scans a codebase to propose a metric, guard, and agent configuration, then writes a program.md run spec and optionally profiles the target with cProfile to surface bottlenecks before optimization goals.
Run /research:plan with a goal or target file to generate a program.md plan and begin profiling.
High-intent search queries and answers about installing and using this skill.
ML experiment profiling uses cProfile to scan a target Python file and surface bottlenecks before execution, informing your optimization goals and metric selection. It helps identify performance constraints prior to running experiments.
A program.md run spec is a frontmatter-driven plan that captures your ML experiment's configuration blocks, including metrics, guards, and scope, ensuring a deterministic execution plan for your research project.
Yes, you can profile a target Python file with cProfile before planning optimization goals. This optional step surfaces bottlenecks and informs measurable goal selection for your ML experiments.
This research planning tool is applicable to Python and ML open-source software projects. It scans the codebase to propose structured configuration blocks and generate a run spec tailored to your project.
The best way to define metrics and guards for ML experiments is scanning your codebase to propose a deterministic configuration. This Skill surfaces measurable goals and generates structured metric, guard, and scope blocks in a program.md plan.
ACautomataInspect a codebase and generate a Claude Code workflow for ML experiments.
marlandojIteratively propose edits, run experiments, and commit metric improvements.
weebcoder101Profile Python code with cProfile and line_profiler to identify performance bottlenecks.
dominodatalabTrack MLflow experiments in Domino Data Lab with auto-logging.
