experiment-code
Experiment Code
Generate and iteratively improve ML experiment code for research papers.
Input
$0— Task:generate,improve,debug,plot$1— Research plan, idea description, or error message
References
- Experiment prompts and patterns:
~/.claude/skills/experiment-code/references/experiment-prompts.md - Code patterns (error handling, repair, hill-climbing):
~/.claude/skills/experiment-code/references/code-patterns.md
Action: generate
Generate initial experiment code following this structure:
More from lingzhi227/claude-skills
literature-search
Search academic literature using Semantic Scholar, arXiv, and OpenAlex APIs. Returns structured JSONL with title, authors, year, venue, abstract, citations, and BibTeX. Use when the user needs to find papers, check related work, or build a bibliography.
24data-analysis
Generate statistical analysis code with 4-round review. Select appropriate statistical tests, interpret results, and produce analysis reports with p-values, effect sizes, and confidence intervals. Use when analyzing experimental data for a paper.
18paper-revision
Revise papers based on reviewer feedback. Map reviewer concerns to specific sections, apply targeted edits, run additional experiments if needed, and verify improvements. Use after receiving peer review with revision requests.
17deep-research
Conduct systematic academic literature reviews in 6 phases, producing structured notes, a curated paper database, and a synthesized final report. Output is organized by phase for clarity.
16idea-generation
Generate novel research ideas with iterative refinement and novelty checking against literature. Score ideas on Interestingness, Feasibility, and Novelty. Use when brainstorming research directions or validating idea novelty.
15rebuttal-writing
Write point-by-point rebuttals to reviewer comments. Extract concerns from reviews, generate evidence-based responses, and format as a structured rebuttal document. Use after receiving peer review feedback.
14