'reproducible' 검색 결과

Use the gget command line or Python package to look up genes, retrieve sequences, compare similar sequences, and explore biological database results. Save outputs and query details to support reproducible first-pass bioinformatics research.

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• Finding gene identifiers and transcript information
• Retrieving nucleotide or amino-acid sequences
• Running quick BLAST or BLAT sequence searches

Plan and write literature reviews using multiple scholarly databases. Document searches, screen papers, synthesize findings, and check citations, then prepare Markdown or PDF reports with transparent selection counts, evidence limitations, and reproducible search records.

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• Prepare a literature review for a thesis or research paper
• Record reproducible searches across scholarly databases
• Track screening decisions and exclusion reasons

Check an MCP server before release through a real client session. Compare available features with source code and documentation, test invalid requests and installation, and produce a verdict backed by reproducible commands and responses.

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• Checking an MCP server before release
• Comparing live features with source code and documentation
• Testing invalid requests and error responses

Evaluate language models on knowledge, math, coding, safety, and image understanding benchmarks. The skill guides reproducible runs in containers using local Docker, Slurm clusters, or cloud execution, with shared configurations for model comparison and result export.

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• Comparing models on the same benchmark tasks
• Running a small evaluation sample before a full benchmark
• Running large evaluations on a Slurm cluster

Validate security patches by comparing vulnerable and patched code against exploit tests, related attack variants, and normal behavior. Produce reproducible logs showing remaining flaws, regressions, newly introduced security failures, and gaps that still need testing.

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• Validate an AI-generated security patch before review
• Check related attack variants for incomplete fixes
• Check whether a fix breaks normal behavior

Prioritizes candidate genes from GWAS, studies linking genetic variants to traits. Combines locus predictions, gene expression evidence, and rare variant support into ranked results with source attribution, confidence labels, reproducible files, and optional figures.

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• Find candidate genes for a disease or trait
• Rank genes near specified lead variants
• Compare evidence and tissue support across genes

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".

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Enhancement overlay — version the WHOLE deployable LLM-app artifact as one bundle: prompts + compiled programs + model snapshot pins + retrieval config + eval-set version, versioned together so a deploy is reproducible and rollback is atomic. Activate when preparing to deploy an LLM app, when asking "what exactly is running in prod right now?", when a deploy must be reproducible months later, or when an incident needs a clean rollback. The core reframe: an LLM app artifact is NOT an ML model — it is a manifest over many independently-mutable parts, not one weights file. Do NOT activate for one-off prompt edits with no deploy, for a single-component demo, or where a vendor owns the whole prompt lifecycle. For versioning ONE compiled prompt use [[agentsop-per-model-artifacts]]; for the CI comparison mechanism use [[agentsop-regression-gate]]. Search keywords: prompt versioning, reproducible deploy, what is running in prod, rollback LLM app, model pinning, prompt registry, version prompts and config.

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Define reproducible development environments with Dev Containers. Use when a user asks to standardize dev environments, set up VS Code remote containers, create reproducible dev setups, or onboard developers faster.

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Create a Quarto document for reproducible reports, presentations, or websites. Covers YAML configuration, code chunk options, output formats, cross-references, and rendering. Use when creating a reproducible analysis report, building a presentation with embedded code, generating HTML, PDF, or Word documents from code, or migrating an existing R Markdown document to Quarto.

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Create reproducible, cross-platform development environments with Flox — a declarative environment manager built on Nix. ALWAYS use this skill when the user needs to: set up a project with system-level dependencies (compilers, databases, native libraries like openssl, libvips, BLAS, LAPACK); configure reproducible toolchains for Python, Node.js, Rust, Go, C/C++, Java, Ruby, Elixir, PHP, or any language; manage environments that must work identically across macOS and Linux; pin exact package versions for a team; run local services (PostgreSQL, Redis, Kafka) alongside development tools; onboard new developers with a single command; or solve 'works on my machine' problems. Especially valuable for AI-assisted and vibe coding — Flox lets agents install tools into a project-scoped environment without sudo, system pollution, or sandbox restrictions, and the resulting environment is committed to the repo so anyone can reproduce it instantly. Use this skill even if the user doesn't mention Flox — if they describe needing reproducible, declarative, cross-platform dev environments with system packages, this is the right tool. Also use when the user mentions .flox/, manifest.toml, flox activate, or FloxHub.

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Set up reproducible development environments with Flox on macOS and Linux. Define tools, package versions, setup steps, and local services in one configuration, then share it with teammates or add project-specific tools for AI agents.

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• Sharing a consistent development setup across a team
• Combining language tools and system dependencies
• Running local PostgreSQL and Redis services

Turn speedup requests into a measured optimization process with limits on time, cost, and variants. Compare each change against a baseline and correctness checks, then retain the fastest safe option with reproducible commands and measurements.

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• Speeding up a slow operation using measured comparisons
• Comparing implementation variants under the same input conditions
• Evaluating latency, throughput, or cost per run

Test an authorized web application, map its exposed pages and inputs, and verify suspected vulnerabilities with reproducible evidence. Produce a security report covering confirmed findings, their impact, reproduction steps, and suggested fixes.

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• Check a staging web application for vulnerabilities
• Verify whether a suspected vulnerability is reproducible
• Compile evidence and fixes into a security report

Create videos programmatically with React for branded explainers, short social clips, and dashboard visuals. Use code to define motion graphics that can be reproduced through the same workflow.

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• Creating branded explainer videos
• Producing short videos for social media
• Turning dashboards into videos

Create generative art, or images made with programmed rules, using p5.js. Fixed random seeds make each rendering reproducible, supporting procedural posters, still images that suggest motion, and studies of artistic frame composition.

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• Posters generated with programmed rules
• Still images that suggest motion
• Studies of artistic frame composition

Simulate concentration changes over time from biochemical models in SBML or Antimony, and compare fixed parameter conditions. Checks model units and exports CSV results plus COMBINE archives containing models and experiment settings, then replays them to compare results.

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• Calculate concentration changes in reaction networks
• Compare baseline and altered kinetic parameters
• Inspect model units and consistency warnings

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

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• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Build and test SimPy models of queues, production, logistics, and other event-driven systems. Set execution limits, monitor waiting times and resource use, and analyze repeated runs with recorded random seeds for reproducible results.

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• Comparing waiting times with different staffing levels
• Modeling shared equipment in production or logistics
• Testing priority queues and interrupted work

Calculate equilibrium phases, their molar fractions, and compositions from a local thermodynamic database using pycalphad. Compare temperatures, check material balance and sampling stability, and export results with recorded conditions and database identifiers.

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• Calculate equilibrium phase fractions for an alloy composition
• Compare stable phases across specified temperatures
• Check material balance and sampling stability

Build and review numerical workflows for MATLAB or GNU Octave, including arrays, tables, graphics, tests, and MAT files. Helps plan release migrations, reproducible projects, and Python integration while checking runtime, compatibility, and license requirements.

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• Reviewing array operations and numerical tolerances
• Planning MATLAB release migrations and compatibility checks
• Preparing reproducible projects and unit test scaffolds

Build and operate genomics workloads on DNAnexus using its command-line tools and Python SDK. The skill covers data transfers, app configuration checks, workflow imports, execution monitoring, and explicit cost controls for reproducible analysis workflows.

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• Transfer and organize genomics data on DNAnexus
• Validate app configuration and build applets
• Import WDL, CWL, or Nextflow workflows

Turn a theme into generative art, artwork created by computational rules, using p5.js. Receive a written artistic philosophy and an interactive HTML viewer with adjustable parameters, reproducible variations, and PNG download controls.

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• Creating generative art from a theme
• Exploring particle and flow-based compositions
• Comparing reproducible variations using seeds

Analyze encrypted ZIP challenges in a CTF sandbox and use bkcrack to recover legacy ZipCrypto keys from known file contents. Create a decrypted archive, validate extraction, and preserve the commands and evidence needed to reproduce the result.

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• Identify the encryption used by a ZIP archive
• Recover ZipCrypto keys from known file contents
• Validate and extract a decrypted archive