본문으로 건너뛰기
cccskills
무료GitHub에 공개

analyze-results

Compare machine learning experiment results from JSON or CSV files. Calculate improvements over a baseline, summarize variation across repeated runs, identify trends and unusual results, and suggest experiments to test possible explanations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

원문원문 설명 보기

Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.

wanshuiyin/Auto-claude-code-research-in-sleep
설치 방법 보기

이럴 때 유용합니다

  • Compare model performance across experimental conditions
  • Check variation across runs with different random seeds
  • Identify trends as a parameter changes
  • Turn findings into ideas for the next experiment

포함된 파일(1)

  • SKILL.md1.6 KB

SKILL.md(원문)

설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.

Analyze Experiment Results

Analyze: $ARGUMENTS

Workflow

Step 1: Locate Results

Find all relevant JSON/CSV result files:

  • Check figures/, results/, or project-specific output directories
  • Parse JSON results into structured data

Step 2: Build Comparison Table

Organize results by:

  • Independent variables: model type, hyperparameters, data config
  • Dependent variables: primary metric (e.g., perplexity, accuracy, loss), secondary metrics
  • Delta vs baseline: always compute relative improvement

Step 3: Statistical Analysis

  • If multiple seeds: report mean +/- std, check reproducibility
  • If sweeping a parameter: identify trends (monotonic, U-shaped, plateau)
  • Flag outliers or suspicious results

Step 4: Generate Insights

For each finding, structure as:

  1. Observation: what the data shows (with numbers)
  2. Interpretation: why this might be happening
  3. Implication: what this means for the research question
  4. Next step: what experiment would test the interpretation

Step 5: Update Documentation

If findings are significant:

  • Propose updates to project notes or experiment reports
  • Draft a concise finding statement (1-2 sentences)

Output Format

Always include:

  1. Raw data table
  2. Key findings (numbered, concise)
  3. Suggested next experiments (if any)

리뷰

아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.

댓글

로그인 없이 쓸 수 있습니다. 사용 후기, 질문, 작동하지 않은 환경 등을 편하게 남겨 주세요.

아직 댓글이 없습니다. 첫 댓글을 남겨 보세요.

보내면 바로 공개됩니다. 이메일 주소나 전화번호 같은 개인 정보는 쓰지 마세요. URL은 2개까지입니다.

0 / 1,000자

이 양식은 reCAPTCHA로 보호되며 Google 개인정보처리방침 및 서비스 약관이 적용됩니다.

같은 저장소의 스킬

ablation-planner

무료

Plan and run ablation studies, experiments that remove or replace parts of a method to test their contribution. Organize reviewer questions, priorities, expected outcomes, and compute costs, then record results and findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Test how much each component contributes
  • Plan experiments that address reviewer questions
  • Prioritize comparisons within a compute budget
wanshuiyin/Auto-claude-code-research-in-sleep1.8만2026년 10월 22일 업데이트

ablation-planner

무료

Plan ablation studies, experiments that remove or replace parts of a machine learning method to test their contribution. Define reviewer questions, expected outcomes, priorities, execution order, and compute estimates, then implement experiments and record findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Testing the contribution of individual method components
  • Planning additional experiments before paper submission
  • Prioritizing experiments within a GPU budget
wanshuiyin/Auto-claude-code-research-in-sleep1.8만2026년 10월 22일 업데이트

alphaxiv

무료

Understand a single arXiv paper through an AlphaXiv summary, with full text and LaTeX source available when needed. Use it to identify key findings or ask specific questions about methods, experiments, and equations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Get a quick overview of a paper from its URL
  • Check a paper's methods and experimental results
  • Ask about equations, proofs, or appendix details
wanshuiyin/Auto-claude-code-research-in-sleep1.8만2026년 10월 22일 업데이트

arxiv

무료

Search arXiv papers by topic or paper ID, review authors and abstracts, and summarize contributions stated in the abstracts. Download selected papers or all search results as PDFs to build a local paper library.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Find arXiv papers on a research topic
  • Retrieve details for a known arXiv paper ID
  • Download all PDFs from a set of search results
wanshuiyin/Auto-claude-code-research-in-sleep1.8만2026년 10월 22일 업데이트

auto-paper-improvement-loop

무료

Improve a compiled LaTeX paper through two rounds of Claude review, edits, and recompilation. The workflow addresses inconsistent reasoning, unsupported claims, unclear writing, and visual presentation, while keeping a cumulative log of changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Polish a generated paper before submission
  • Correct inconsistencies and unsupported claims
  • Improve figures, tables, and page layout
wanshuiyin/Auto-claude-code-research-in-sleep1.8만2026년 10월 22일 업데이트

auto-paper-improvement-loop

무료

Improve a compiled paper through two rounds of external model review, source edits, and PDF rebuilding. Check claims against evidence, resolve inconsistencies, and refine figures and layout, with optional checkpoints and restrictions on which edits are allowed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

  • Revising a generated paper before submission
  • Correcting mismatches between claims and evidence
  • Improving notation, figures, tables, and layout
wanshuiyin/Auto-claude-code-research-in-sleep1.8만2026년 10월 22일 업데이트

wanshuiyin의 스킬 모두 보기

이 스킬의 문제 신고