'identification' 검색 결과

Inspect public repositories, build artifacts, and container images for software supply chain risks. The skill identifies exposed internal package names, unsafe workflow configurations, and leaked secrets, while requiring build evidence before treating unclaimed package names as exploitable.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate exposed internal package names
• Review public GitHub Actions workflows
• Inspect public container layers for secrets

Investigate server-side template injection, where user input is interpreted as template code. The skill covers engine identification, testing email and PDF rendering, and checking whether an injection can lead to command execution on the server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assessing email template rendering
• Testing PDF and report generators
• Reviewing authenticated CMS template editors

Choose quantitative or qualitative methods that fit a research question and dataset. Compare alternatives, check assumptions, generate analysis code, interpret results, and examine sensitivity, meaning how conclusions change under different analytical choices.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing methods for a research question
• Generating analysis code in R or Python
• Planning theme identification in interview data

Check an economics paper before submission with two parallel reviewers. Assess its contribution, whether the design supports causal conclusions, and unsupported claims, then receive a short report with fixes ranked by severity.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking an economics paper before submission
• Reviewing whether evidence supports causal claims
• Finding unsupported mechanisms and broad generalizations

Assess a social science paper outline in a .qmd file for missing research questions, contributions, hypotheses, data, and analysis plans. Receive criterion ratings and concrete suggestions in a separate report, with causal assumptions assessed when relevant.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether a paper outline covers essential research elements
• Clarify contributions relative to previous studies
• Review causal assumptions in a research outline

Review academic manuscripts for argument quality, causal evidence, statistical methods, citations, and presentation. Receive a structured report with strengths, major concerns, suggested improvements, and likely referee objections tied to specific parts of the paper.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a manuscript before submission
• Checking the assumptions behind causal claims
• Anticipating referee objections and possible responses

Generate 15–20 research directions from a question, topic, or data opportunity, then narrow them to 5–7 candidates. Receive ranked ideas with feasibility assessments, key risks, confidence scores, and reasons for rejecting the others.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explore research directions from a broad topic
• Find research uses for newly available data
• Compare ideas by feasibility and contribution

Plan and implement instrumental variable estimation and propensity score matching, methods for estimating causal effects under different assumptions. Includes Python, R, and Stata examples, diagnostic checks, and guidance on interpreting and reporting results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choose between instrumental variables and matching
• Implement two-stage least squares estimation
• Check weak instruments and estimation diagnostics

Turn a financial economics research idea into a structured, 12-part empirical proposal. It outlines hypotheses, matching data sources, methods for testing causal effects, illustrative results tables, and robustness checks, while examining the study’s contribution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting a financial economics research proposal
• Finding Chinese and international data sources
• Planning tests of causal effects

Plan philosophy and interdisciplinary papers by comparing preprint platforms, searching literature, identifying unanswered questions, and assessing originality. Receive a detailed outline with section word counts, key citations, and improvements based on a reviewer-style assessment.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a philosophy or interdisciplinary paper
• Choosing a suitable preprint platform
• Finding research gaps and assessing originality

Python interface to OpenMS for mass spectrometry data analysis. Use for LC-MS/MS proteomics and metabolomics workflows including file handling (mzML, mzXML, mzTab, FASTA, pepXML, protXML, mzIdentML), signal processing, feature detection, peptide identification, and quantitative analysis. Apply when working with mass spectrometry data, analyzing proteomics experiments, or processing metabolomics datasets.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Query openFDA API for drugs, devices, adverse events, recalls, regulatory submissions (510k, PMA), substance identification (UNII), for FDA regulatory data analysis and safety research.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run the PAIDF Orchestration Image Attribute Augmentation DAG on Kubernetes - person-crop clothing augmentation, attribute search, and augmented dataset generation. Select for requests about image attribute augmentation, person attribute search, person re-identification data, clothing augmentation, attribute captions, augmentation payloads, run status, or result retrieval. Runs environment setup first when controller readiness is unknown. Not for video or defect-image generation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Used for header-only preflight of one DICOM series folder before conversion or inference. Not for de-identification or clinical clearance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Automate Amplitude tasks via Rube MCP (Composio): events, user activity, cohorts, user identification. Always search tools first for current schemas.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Query Open Targets Platform for target-disease associations, drug target discovery, tractability/safety data, genetics/omics evidence, known drugs, for therapeutic target identification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when analyzing weekly price charts for stocks, stock indices, cryptocurrencies, or forex pairs. Use this skill when the user provides chart images and requests technical analysis, trend identification, support/resistance levels, scenario planning, or probability assessments based purely on chart data without consideration of news or fundamental factors.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

This skill should be used when analyzing sector rotation patterns and market cycle positioning. It fetches sector uptrend data from CSV (no API key required) and optionally accepts chart images for supplementary analysis. Use this skill when the user requests sector rotation analysis, cyclical vs defensive assessment, overbought/oversold identification, or market cycle phase estimation. All analysis and output are conducted in English.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Screen US stocks using William O'Neil's CANSLIM growth stock methodology. Use when user requests CANSLIM stock screening, growth stock analysis, momentum stock identification, or wants to find stocks with strong earnings and price momentum following O'Neil's investment system.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Protein grouping and inference from peptide identifications. Use when resolving protein ambiguity from shared peptides. Handles protein groups and protein-level FDR control using parsimony and probabilistic approaches.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Predict peptide-MHC class I and II binding affinity using MHCflurry and NetMHCpan neural network models. Identify potential T-cell epitopes from protein sequences. Use when predicting MHC binding for vaccine design or neoantigen identification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Specialized lipidomics analysis for lipid identification, quantification, and pathway interpretation. Covers LC-MS lipidomics with LipidSearch, MS-DIAL, and LipidMaps annotation. Use when analyzing lipid classes, chain composition, or lipid-specific pathways.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Metabolite identification from m/z and retention time. Covers database matching, MS/MS spectral matching, and confidence level assignment. Use when assigning compound identities to detected features in untargeted metabolomics.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Unsupervised clustering and cell type identification for flow/mass cytometry. Covers FlowSOM, Phenograph, and CATALYST workflows. Use when discovering cell populations in high-dimensional cytometry data without predefined gates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.