'agent-skill' 검색 결과

Challenge important design and implementation decisions with a separate reviewer that does not inherit the working session's context. Identify hidden assumptions and failure cases, then assess each finding against the artifact and its requirements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Challenging assumptions in an architecture proposal
• Reviewing decisions in unfamiliar code
• Checking authentication or security-sensitive logic

Prepare a production release with checks for code quality, security, performance, accessibility, and infrastructure. Plan monitoring, gradual feature exposure, and rollback steps, then use release metrics and reliability limits to guide rollout decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check readiness for a first production release
• Plan a gradual rollout for a major feature
• Define monitoring and post-launch checks

Develop and fix code by first writing a failing test, then making it pass and improving the implementation. The skill follows the project's testing tools and checks both the intended behavior and existing functionality.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Implement new logic with tests written first
• Reproduce a reported bug before fixing it
• Check existing behavior when changing code

Turn clear requirements into small implementation tasks with acceptance criteria, verification steps, dependencies, and estimated scope. Produce a written plan and ordered task list, with checkpoints and opportunities for parallel work identified before coding begins.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Breaking a large feature into implementable tasks
• Ordering work by dependencies
• Defining completion criteria and verification steps

Find and fix slow page loads, API responses, and database queries using measurements. Compare results before and after each change, discard changes without clear gains, and add checks or monitoring to catch future slowdowns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating slow page loads and sluggish interactions
• Reducing API delays and repeated database queries
• Checking whether a recent change caused a slowdown

Investigate failing tests, broken builds, and unexpected behavior through a structured debugging process. Reproduce the failure, narrow down its source, fix the root cause, and add a regression test that catches the same problem.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating a test that fails after a code change
• Finding the cause of a broken build
• Tracing a feature that stopped working

Define project quality standards in CONSTRAINTS.md, connect measurable targets to checks, and inspect changes for weakened safeguards. It helps teams choose thresholds and catch skipped tests, suppressed warnings, unfinished code, or lowered standards during agent development.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set quality standards before starting agent development
• Choose coverage and performance thresholds
• Detect skipped tests and suppressed warnings

Turn a rough idea into a concrete concept through guided discussion. Explore alternatives, compare user value and feasibility, and produce a Markdown brief covering the recommended direction, assumptions to test, minimum scope, and explicit exclusions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Shaping a vague product or feature idea
• Exploring alternatives before choosing a direction
• Testing assumptions before committing to a plan

Plan the retirement of old APIs, features, and systems, then move their users to replacements in stages. The skill also guides database schema changes through additive steps that keep old and new application code compatible during rollout.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Moving users from an old API to its replacement
• Planning the retirement of an unused feature
• Renaming production database columns in stages

Review code changes before merging across correctness, readability, architecture, security, and performance. Get findings that distinguish required fixes from optional suggestions, with concrete ways to simplify code and checks on test coverage.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing a pull request before merging
• Checking code written by an AI agent
• Assessing whether a refactor reduces complexity

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Forecast sales and other time series with Google’s TimesFM without task-specific training. Prepare regularly spaced CSV data, export predicted values and uncertainty ranges, add explanatory variables, and evaluate forecasts against held-out observations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Forecast monthly sales or revenue from CSV data
• Export predicted values with uncertainty ranges
• Forecast with additional explanatory variables

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

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.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

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

Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining the feature contributions behind one prediction
• Visualizing attribution patterns across evaluation samples
• Selecting and explaining a class in a multiclass model

Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Estimating participant or sample counts before data collection
• Finding the smallest effect detectable with a fixed sample
• Preparing sample size justifications for grants or protocols

Stress-test research plans by comparing possible scenarios, such as sample losses, unavailable materials, or methods failing to transfer. Record assumptions and evidence, compare response options, and define contingency plans and observable triggers for changing course.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan responses to higher-than-expected sample losses
• Prepare backups for unavailable reagents or datasets
• Compare research options under resource constraints

Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build node and graph classification models
• Predict links between nodes
• Train models on graphs with multiple node and connection types

Prepare papers, research posters, and grant documents for a specific journal, conference, or funder. The skill guides official template selection, checks current submission requirements, adapts writing, and inspects PDF page counts and embedded fonts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a paper for a journal or conference
• Checking page limits and anonymous review requirements
• Drafting grant proposal components

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Work with large scientific arrays by storing data in smaller blocks and reading selected portions. Guides compression, cloud storage, format migration, and NumPy, Dask, or Xarray integration while preserving dimensions, units, and other metadata.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reading selected regions of large scientific arrays
• Choosing data blocks and compression settings
• Working with scientific arrays stored on S3 or GCS

Reduce data with many features into fewer dimensions using UMAP. Create 2D or 3D views, prepare inputs for clustering, compare parameter settings, and project new samples while checking whether the results remain stable and meaningful.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Visualizing data with many features in 2D or 3D
• Preparing reduced features for HDBSCAN clustering
• Guiding dimensionality reduction with partial labels

Run protein structure prediction, molecular design, and docking tools on Tamarind Bio without a local GPU. Guides tool selection, input validation, job submission, batch processing, and result retrieval through its REST API or MCP server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting protein structures in the cloud
• Running binder or antibody design tools
• Submitting batches of sequences for analysis

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics