Que fait auto_train_skills ?
Apply ML training loops to prompt engineering — run evals, LLM-grade outputs, and track pass rates to iteratively improve Claude Code skills
Apply ML training loops to prompt engineering — run evals, LLM-grade outputs, and track pass rates to iteratively improve Claude Code skills
auto_train_skills is a Claude Code agent skill that apply ML training loops to prompt engineering — run evals, LLM-grade outputs, and track pass rates to iteratively improve Claude Code skills.
npx skills add dave-wind/auto_train_skillsInstalled? Explore more Recherche et analyse de données skills: affaan-m/uspto-database, affaan-m/scholar-evaluation, affaan-m/literature-review · View all 6 →
Apply ML training loops to prompt engineering — run evals, LLM-grade outputs, and track pass rates to iteratively improve Claude Code skills
USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.
Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.
Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.
The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond.
Agent skill repository discovered by 10x-chat research.
Review backend code for quality, security, maintainability, and best practices based on established checklist rules. Use when the user requests a review, analysis, or improvement of backend files (e.g., `.py`) under the `api/` directory. Do NOT use for frontend files (e.g., `.tsx`, `.ts`, `.js`). Supports pending-change review, code snippets review, and file-focused review.