'reproducibility' 검색 결과

Query the Ensembl REST API for gene/transcript/protein lookup, sequence retrieval, comparative genomics (Compara), variant effect prediction (VEP), regulatory features, and cross-species ortholog/paralog calls. Use when pulling Ensembl-native data (Ensembl Gene IDs, version-pinned releases, archive endpoints for reproducibility), gene/transcript/exon structure with stable IDs, or VEP for variant annotation. Encodes the 15 req/sec rate limit, archive (e110.rest.ensembl.org) for reproducibility, Ensembl divisions (vertebrates / plants / fungi / metazoa / bacteria), and the symbol-vs-ID stability problem.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn academic papers into structured reviews that explain findings, strengths, weaknesses, and research methods. Assess originality and supporting evidence, compare relevant literature, and provide specific improvement suggestions in a detailed review or brief summary.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understanding a paper's findings and limitations
• Preparing a draft peer review
• Checking experimental design and reproducibility

Generate images, video, and music through ComfyUI in OpenMontage. Helps adapt community workflows, select the final output, identify missing models, and choose workflows suited to available GPU memory while recording settings for reproducibility.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generating images, video, or music in OpenMontage
• Importing a community ComfyUI workflow
• Choosing workflows for limited GPU memory

Explore gene, drug, disease, and phenotype associations in a downloaded PrimeKG CSV. Retrieve direct relationships, disease summaries, and paths through one intermediate node while preserving relationship types and data provenance for reproducible research and hypothesis generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking associations between genes, drugs, and diseases
• Summarizing genes, drugs, and phenotypes linked to a disease
• Exploring short paths between two entities

Assess Web3 vulnerability findings before submission by checking scope, reproducibility, prerequisites, and attack costs. Organize the root cause, measurable impact, working proof of concept, and proposed fix into an Immunefi report, with examples for comparison.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking whether a finding is ready for submission
• Drafting an Immunefi vulnerability report
• Reviewing reproduction code and measurable impact

Evaluate security findings before writing a bug bounty report. Check reproducibility, scope, practical impact, duplicates, and behavior across user sessions, then use submission checklists and CVSS 3.1 guidance to prepare evidence and assess severity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Deciding whether a finding is ready to report
• Rechecking access across different user sessions
• Reviewing duplicate or intended behavior

Review vulnerability findings before drafting a bug bounty report. Seven validation questions and submission checklists help assess scope, reproducibility, demonstrated impact, duplicate reports, and whether the claimed severity matches the available evidence.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Decide whether a finding is ready to report
• Recheck suspected authentication bypasses
• Match severity claims to demonstrated impact

Validate the replication package for the sewage-house-prices project. Runs 10 checks covering script execution, file integrity, output freshness, dependency verification, data provenance, and README completeness (AEA format). This skill should be used when asked to "audit replication", "check the package", or "verify reproducibility".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan literature searches for six review types, from systematic to rapid reviews. Develop database-specific search queries, selection criteria, citation tracking, and search records, while comparing approaches for coverage, reproducibility, and available time.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose a review method for a research question
• Build search queries for multiple databases
• Map a research field and identify gaps

Run the R code review protocol on R scripts. Checks code quality, reproducibility, domain correctness, and professional standards. Produces a report without editing files. Make sure to use this skill whenever the user wants their existing R code evaluated or audited — not when they want new analysis written. Triggers include: "review my R script", "check my R code", "is my code replication-ready", "audit this R file", "does this code follow conventions", "will this reproduce", "check my analysis script", "code review", "review-r", or when the user has an existing .R file and wants quality feedback rather than new code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill covers reproducible research pipelines and replication packages. Use when the user is setting up a research project directory structure, configuring workflow managers (Make, Snakemake, DVC), managing computational environments, preparing replication packages for journal submission, or debugging reproducibility failures. Triggers on "reproducible", "replication package", "Makefile", "Snakemake", "DVC", "pipeline", "workflow manager", "data versioning", "conda environment", "Docker", "seed management", "AEA data editor", "replication", "project structure", or "submission checklist".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Carry out research implementation plans from environment setup through code, tests, and pull requests. The workflow checks estimation convergence, standard errors, diagnostics, and reproducibility while tracking completed tasks in the original plan.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implement an estimation method from a research plan
• Build and verify a research data pipeline
• Run robustness checks and document results

Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build and verify replication packages — routes to reproducibility-auditor agent

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Turn a recently solved research problem into searchable Markdown documentation. Capture symptoms, failed attempts, root causes, working code, and prevention checks, then organize the solution by category and link it to related records.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Recording a fix for estimation convergence failures
• Documenting causes and fixes for data processing errors
• Preserving solutions to reproducibility problems

Integrate AI tools into economic research, teaching, and policy analysis with attention to privacy and reproducibility.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Write research/technical reports with strong structure + figure standards. Supports Markdown/HTML/PDF outputs (Quarto optional), executive summary, methods, results, discussion, and reproducibility appendix.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Exports sanitized metadata, parameters, reproducibility details, quality metrics, and optional review artifacts from Medical AI inference runs or evidence packs to MLflow. Use after inference, including NV-Generate runs; not for live training tracking, model registration, or clinical use.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Meta-agent that routes bioinformatics requests to specialised sub-skills. Handles file type detection, analysis planning, report generation, and reproducibility export.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

ChIP-seq quality control metrics including FRiP (Fraction of Reads in Peaks), cross-correlation analysis (NSC/RSC), library complexity, and IDR (Irreproducibility Discovery Rate) for replicate concordance. Use to assess experiment quality before downstream analysis. Use when assessing ChIP-seq data quality metrics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Prepare evidence-bounded, constructive peer-review drafts and structured manuscript assessments. Use for authorized review of scientific manuscripts, protocols, preprints, or research proposals; reporting-guideline selection; claim–evidence checks; methods, statistics, reproducibility, ethics, figure/table, and citation critique; or revision-response planning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build safe, local, defensive reproductions of ALREADY-PATCHED, publicly disclosed security vulnerabilities (CVEs, vendor advisories, AI-agent incidents) for whitepapers, control testing, and education. Use this skill whenever the user wants to reproduce, demonstrate, or write up a known vulnerability or security incident — phrases like "reproduce this CVE", "build a repro for", "demonstrate the EchoLeak/Cursor/postmark-mcp issue", "show how this exploit worked", "add a reproducibility guide", or "pull the NVD/CVE details for". It fetches advisory metadata from NVD and CVE.org, confirms the issue is fixed, and produces a vulnerable-vs-patched harness that is offline, loopback-only, and uses synthetic markers. It NEVER discovers, develops, or weaponizes new zero-days and refuses any unpatched or live-target request.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Standards and audits for writing academic papers, especially ML/NLP submissions to NeurIPS, ICML, ICLR, ACL, EMNLP, NAACL. Covers the author's house prose rules (zero em dashes, zero fluff, every sentence earns its place), verification of hallucinated claims and hallucinated or malformed bibliography entries, the ACL policy on AI writing assistance and what must be disclosed, and best practices for paper structure, experiments, reproducibility checklists, and rebuttals. Use this skill whenever the user is drafting, editing, polishing, restructuring, or reviewing a paper, abstract, intro, related work, rebuttal, or camera-ready; whenever they ask to "clean up" or "tighten" academic writing; whenever citations or a .bib file need checking; and whenever they ask what has to be disclosed about AI assistance. Reach for it even when the user only says "make this section better", "check my references", or "is this ready to submit".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.