'sam' 검색 결과

Use this skill when an auditor will test change management, or the team wants to know before they do, and asks "sample our production changes", "check our PR approvals", "do we have segregation of duties on deploys", "test change management before the audit", "were emergency changes approved" or "can a developer push to production alone". Builds the change population from what actually reached production, reconciles it with tickets and pull requests, draws a recorded sample, tests approval, testing, segregation and emergency attributes, and writes the results and the exceptions as facts.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when working with Aws Sam — aWS Serverless Application Model (SAM) management. Covers template validation, local testing, build and packaging, deployment orchestration, stack inspection, log tailing, and API Gateway debugging. Use when building serverless applications with SAM, testing locally, deploying Lambda functions, or troubleshooting API Gateway endpoints.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

View, convert, and understand SAM/BAM/CRAM alignment files using samtools and pysam. Use when inspecting alignments, converting between formats, or understanding alignment file structure.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use psamvault MCP server: credential vault, browser login, API key injection, .env secret protection. MCP tools keep secrets out of the agent's context window.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

An example skill loaded through resources_discover and supplied by the dynamic-resources extension. The material identifies its role as a sample but does not describe a specific task, workflow, or output.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a sample loaded through resources_discover
• Checking an example supplied by an extension
• Identifying the sample's documented scope

Create and run automated tests that score LLM answers against expected outputs or judging criteria. Monitor progress, inspect individual results, compare models on the same tests, and use score thresholds in CI, an automated checking workflow.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating tests with expected answers
• Monitoring evaluation progress
• Inspecting failed samples and scores

Review author information in structured data, a machine-readable description of page content. Check names, profile links, and connections to articles or pages, then identify corrections using the rendered HTML and HTTP response.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check author data on article pages
• Connect authors to external profiles
• Review author markup in page templates

Review local character animations before sharing previews or final renders. Check structured design data, asset files, browser errors, sampled motion frames, and MP4 metadata, then classify the result as pass, revise, or fail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking character animation previews before sharing
• Finding missing assets and browser errors
• Inspecting sampled frames for motion and visual issues

Create market research reports that connect claims to sources, compare competitors, and estimate market size. Build alternative forecasts with explicit assumptions, so readers can review the calculations, uncertainties, and evidence behind each conclusion.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a market report for a new business
• Comparing market size estimates and their assumptions
• Building competitor comparisons with traceable sources

Process next-generation sequencing data with deepTools: convert alignment files into coverage tracks, normalize signals, compare samples, and check quality. Create heatmaps and profiles around genes or peak regions for ChIP-seq, RNA-seq, and ATAC-seq experiments.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Converting BAM files to normalized bigWig tracks
• Checking sequencing quality and replicate agreement
• Plotting heatmaps around transcription start sites

Organize experiment photos, audio, and notes into Markdown logs with structured metadata. Assign experiment and sample batch IDs, archive original attachments, and optionally connect records to Feishu group messages or an Obsidian notebook.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turning experiment photos and voice notes into consistent logs
• Tracking the same sample batch across multiple experiments
• Archiving original files with links from experiment logs

Build and deploy AWS Lambda functions without managing servers. Guidance covers APIs, scheduled jobs, event triggers, permissions, shared dependencies, and performance tuning, with code and command examples for deployment and troubleshooting.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building lightweight APIs with API Gateway or function URLs
• Running scheduled jobs with EventBridge
• Processing S3 uploads or SQS messages

Develop firmware and drivers for ARM Cortex-M microcontrollers, including Teensy, STM32, nRF52, and SAMD. Get complete modules with initialization and usage examples, plus guidance on interrupts, memory handling, data transfers, performance, and fault diagnosis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build an SPI driver for an external sensor
• Design interrupt-driven UART reception
• Review DMA transfers and cache consistency

Plan an A/B test to compare product changes, with a clear hypothesis, variants, sample estimates, and tracking checks. Define success metrics and stopping rules before launch, then document results and decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning a signup form A/B test
• Estimating sample size and test duration
• Checking event tracking before launch

Solve competition mathematics problems and examine proofs through independent agents that actively look for gaps, counterexamples, and misused theorems. Produces a checked argument or clearly states what remains unresolved, with optional PDF output.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Solve an IMO or Putnam problem
• Check a competition proof for gaps
• Find counterexamples to claims within a proof

Analyze binaries, malware samples, memory dumps, and packet captures in sandboxed security challenges. Trace program behavior, investigate crashes, and verify exploit conditions while preserving original files, decoded artifacts, and observable evidence for reproducible findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reverse engineer a binary’s structure and behavior
• Unpack a malware sample and separate its components
• Investigate crashes and verify exploit conditions

Research social discussions using public posts or authorized exports, and produce reports that link each claim to inspected evidence. Track duplicate posts, distinguish speakers, document access gaps, and audit conclusions that exceed the observed sample.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing product discussions across social platforms
• Creating cited reports from supplied post exports
• Checking repost duplication and comment attribution

Check Microsoft API usage against official documentation and code samples. Find methods, parameters, and packages for Azure SDKs, .NET libraries, and Microsoft APIs, then compare implementations to investigate errors or outdated patterns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify methods and parameters before writing code
• Find official code samples for authentication or uploads
• Investigate missing methods and type errors