{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"dvc-cli","slug":"dvc-cli","name":"DVC CLI","type":"cli","url":"https://github.com/iterative/dvc","page_url":"https://unfragile.ai/dvc-cli","categories":["cli-tools"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"dvc-cli__cap_0","uri":"capability://data.processing.analysis.content.addressable.data.versioning.with.multi.backend.remote.storage","name":"content-addressable data versioning with multi-backend remote storage","description":"DVC tracks large data files and ML models using content-addressable storage (hash-based) with a local cache layer, enabling efficient deduplication and synchronization across multiple cloud backends (S3, GCS, Azure, etc.). The Output class associates files with checksums and manages retrieval from local cache or remote storage, while the Repo class coordinates cache operations and remote synchronization. This architecture allows teams to keep workspaces clean while maintaining full data lineage in Git metadata.","intents":["Version large datasets and model artifacts without storing them directly in Git","Share data across team members by pushing/pulling from cloud storage while tracking metadata in Git","Deduplicate identical files across experiments and pipelines to minimize storage costs","Reproduce experiments by retrieving exact data versions from remote storage"],"best_for":["ML teams managing multi-gigabyte datasets and model checkpoints","Data scientists collaborating on shared projects with limited local storage","Organizations using AWS S3, Google Cloud Storage, or Azure Blob Storage"],"limitations":["Requires external remote storage configuration — DVC does not provide hosted storage itself","Hash computation for large files adds initial overhead during dvc add operations","No built-in encryption for data in transit or at rest — relies on cloud provider security","Cache synchronization can be slow for projects with thousands of large files"],"requires":["Git repository initialized in project root","Python 3.8+","Cloud storage credentials (AWS, GCS, Azure, or compatible S3-like service)","Sufficient local disk space for cache layer"],"input_types":["file paths (local or remote)","directory structures","configuration files (.dvc/config)"],"output_types":[".dvc metadata files (YAML)","cache directory structure","remote storage synchronization"],"categories":["data-processing-analysis","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_1","uri":"capability://automation.workflow.dag.based.pipeline.definition.and.smart.incremental.execution","name":"dag-based pipeline definition and smart incremental execution","description":"DVC pipelines are defined as directed acyclic graphs (DAGs) where each Stage represents a step with explicit dependencies and outputs. The Stage Management system tracks which stages need re-execution based on changes to inputs, code, or parameters, enabling smart caching that skips unchanged stages. The Reproduction and Caching subsystem compares file checksums and parameter values to determine if a stage is stale, then executes only affected downstream stages, avoiding redundant computation.","intents":["Define multi-step data processing and model training workflows in dvc.yaml","Automatically skip pipeline stages that haven't changed since last run","Understand data dependencies and execution order visually","Reproduce exact pipeline results by re-running from any checkpoint"],"best_for":["ML engineers building reproducible training pipelines with multiple stages","Data teams with complex ETL workflows spanning data ingestion, transformation, and validation","Researchers needing to track which code/data changes triggered model retraining"],"limitations":["DAG must be acyclic — circular dependencies are not supported","Stage caching is file-hash based, not semantic — renaming a file invalidates cache even if content is identical","No built-in parallelization across independent stages — execution is sequential by default","Parameter tracking requires explicit dvc.yaml configuration; implicit dependencies in Python code are not detected"],"requires":["dvc.yaml file in project root defining pipeline stages","Git repository for tracking dvc.yaml changes","Bash or shell environment for executing stage commands","All stage dependencies (data files, code) must be tracked by DVC or Git"],"input_types":["dvc.yaml pipeline definition (YAML)","params.yaml parameter files","code scripts (Python, shell, etc.)","data files and directories"],"output_types":["dvc.lock file (stage execution graph with checksums)","generated data files and model artifacts","execution logs and status"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_10","uri":"capability://tool.use.integration.python.api.for.programmatic.dvc.operations.and.integration","name":"python api for programmatic dvc operations and integration","description":"DVC provides a Python API (dvc.repo.Repo class) enabling programmatic access to all DVC operations: adding files, running pipelines, tracking experiments, and querying metrics. The API mirrors CLI commands but allows integration into Python scripts, Jupyter notebooks, and custom tools. This enables teams to build automated workflows, custom dashboards, and CI/CD integrations without shelling out to CLI commands.","intents":["Integrate DVC operations into Python scripts and Jupyter notebooks for automated workflows","Build custom dashboards or tools that query DVC metrics and experiment history","Automate data pipeline execution and experiment tracking in CI/CD systems","Programmatically add files, run pipelines, and track experiments without CLI commands"],"best_for":["Python developers building custom ML workflows and automation tools","Teams integrating DVC into CI/CD pipelines (GitHub Actions, GitLab CI, etc.)","Data scientists using Jupyter notebooks for interactive experiment tracking"],"limitations":["Python API is less stable than CLI — breaking changes can occur between minor versions","API documentation is sparse compared to CLI documentation — requires reading source code for advanced usage","No async/await support — API calls block until completion, limiting parallelization","Error handling is inconsistent — some operations raise exceptions, others return error codes"],"requires":["Python 3.8+","dvc package installed (pip install dvc)","Git repository initialized","DVC initialized in project (dvc init)"],"input_types":["Python objects (file paths, parameters, metrics)","Git commits and branches","experiment names and IDs"],"output_types":["Python objects (metrics, parameters, experiment results)","file paths and checksums","execution status and logs"],"categories":["tool-use-integration","code-generation-editing"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_11","uri":"capability://automation.workflow.progress.reporting.and.user.feedback.during.long.running.operations","name":"progress reporting and user feedback during long-running operations","description":"DVC's Progress Reporting subsystem provides real-time feedback during long-running operations (data synchronization, pipeline execution, hash computation) via progress bars and status messages. The system tracks operation progress (bytes downloaded, files processed) and displays estimated time remaining. This improves user experience during operations that can take minutes or hours.","intents":["Monitor progress of dvc push/pull operations for large datasets","Track pipeline execution progress across multiple stages","Understand hash computation progress when adding large files","Get estimated time remaining for long-running operations"],"best_for":["Users working with large datasets (multi-gigabyte) requiring long sync times","Teams running complex pipelines with multiple stages","Data scientists needing visibility into background operations"],"limitations":["Progress reporting adds overhead — can slow down operations by 5-10%","Estimated time remaining is inaccurate for variable-speed operations (network-dependent)","Progress bars are terminal-only — no progress reporting in non-interactive environments","No built-in progress persistence — progress is lost if operation is interrupted"],"requires":["Terminal with ANSI color support for progress bars","Python 3.8+ with tqdm library (DVC's progress reporting dependency)"],"input_types":["operation type (push, pull, repro, etc.)","total work units (bytes, files)"],"output_types":["progress bars with percentage and ETA","status messages and logs"],"categories":["automation-workflow","user-experience"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_12","uri":"capability://automation.workflow.index.based.pipeline.loading.and.caching","name":"index-based pipeline loading and caching","description":"DVC's Index System loads and caches the pipeline DAG structure, avoiding repeated parsing of dvc.yaml files. The Index class builds a graph of stages and their dependencies, enabling efficient traversal for operations like status checking, reproduction, and visualization. Index caching is invalidated when dvc.yaml or dvc.lock files change, ensuring consistency.","intents":["Efficiently load large pipelines without re-parsing dvc.yaml files","Cache pipeline structure for repeated operations","Enable fast DAG traversal for status and reproduction","Detect pipeline changes and invalidate cache"],"best_for":["Projects with large pipelines (50+ stages)","Workflows requiring repeated pipeline operations","Teams optimizing DVC performance"],"limitations":["Index caching adds complexity; cache invalidation bugs can cause stale state","Index is in-memory; large pipelines may consume significant memory","Cache invalidation is file-based; programmatic changes to pipeline structure are not detected","No persistent index; cache is rebuilt on each DVC invocation"],"requires":["dvc.yaml pipeline definition","dvc.lock file (for dependency tracking)"],"input_types":["dvc.yaml","dvc.lock"],"output_types":["cached pipeline DAG","stage and dependency information"],"categories":["automation-workflow","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_2","uri":"capability://memory.knowledge.experiment.tracking.and.comparison.with.parameter.metric.versioning","name":"experiment tracking and comparison with parameter/metric versioning","description":"DVC's Experiment Management system queues and executes ML experiments as isolated Git branches, tracking parameters (from params.yaml), metrics (from JSON/CSV files), and outputs (models, plots) for each run. The Experiment Tracking and Comparison subsystem stores experiment metadata in a local Git repository, enabling comparison of metrics across runs without a centralized server. Each experiment is a Git commit with associated parameter and metric snapshots, allowing teams to query and visualize experiment history.","intents":["Run multiple model training experiments with different hyperparameters and track results","Compare metrics (accuracy, loss, F1) across experiments to identify best model","Reproduce exact experiment conditions by checking out experiment Git commits","Share experiment results with team members via Git without external experiment tracking servers"],"best_for":["Data scientists iterating on model hyperparameters and architectures","Teams wanting experiment tracking without MLflow/Weights & Biases infrastructure","Researchers needing reproducible experiment history tied to code and data versions"],"limitations":["Experiments are stored as Git commits, which can bloat repository history for high-volume experiment runs (100+ experiments)","No built-in distributed experiment execution — queued experiments run sequentially on single machine","Metric comparison requires manual dvc exp show commands; no real-time dashboard or web UI in open-source version","Parameter and metric files must follow specific formats (YAML for params, JSON/CSV for metrics) — custom formats not supported"],"requires":["params.yaml file defining experiment parameters","Metrics files (JSON or CSV) generated by training scripts","Git repository with clean working directory (experiments create new commits)","Python 3.8+ and dvc[experiments] package"],"input_types":["params.yaml (parameter definitions)","metrics files (JSON, CSV, YAML)","training scripts that read params and write metrics","dvc.yaml pipeline definition"],"output_types":["experiment metadata (stored in .dvc/tmp/exps)","comparison tables (dvc exp show output)","Git commits for each experiment","plots and visualizations"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_3","uri":"capability://data.processing.analysis.multi.format.metrics.and.plots.extraction.with.visualization","name":"multi-format metrics and plots extraction with visualization","description":"DVC's Metrics and Parameters subsystem extracts metrics from JSON, YAML, and CSV files generated by training scripts, and generates plots from CSV/JSON data using configurable axes and grouping. The Visualization and Analysis layer parses metric files, compares values across experiments, and renders plots (scatter, line, confusion matrix) via dvc plots commands. This enables teams to visualize model performance trends without external visualization tools.","intents":["Extract and compare metrics (accuracy, loss, F1) from training output files across experiments","Generate plots (training curves, confusion matrices) from CSV/JSON metric files","Visualize metric trends across experiment history to identify best model","Share metric comparisons and plots with team via dvc plots show"],"best_for":["ML teams tracking training metrics and generating performance visualizations","Researchers comparing model performance across hyperparameter sweeps","Data scientists needing lightweight metric tracking without external dashboards"],"limitations":["Metrics must be in JSON, YAML, or CSV format — binary formats (HDF5, Parquet) not supported","Plot generation is static (PNG/HTML) — no interactive dashboards in open-source version","Metric comparison requires explicit dvc.yaml configuration; automatic metric detection not supported","Large metric files (100k+ rows) can slow down dvc plots generation"],"requires":["Metrics files in JSON, YAML, or CSV format","dvc.yaml with metrics section defining which files to track","Python 3.8+ with matplotlib/plotly for plot rendering","Git repository for tracking metric file changes"],"input_types":["JSON metric files (e.g., {\"accuracy\": 0.95, \"loss\": 0.05})","CSV files with metric columns","YAML metric definitions","dvc.yaml plots configuration"],"output_types":["metric comparison tables (dvc metrics show)","plot images (PNG, HTML)","metric diff reports (dvc metrics diff)"],"categories":["data-processing-analysis","visualization"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_4","uri":"capability://tool.use.integration.file.system.abstraction.with.multi.protocol.data.access","name":"file system abstraction with multi-protocol data access","description":"DVC's File System Abstraction layer provides a unified interface for accessing data across local filesystem, HTTP/HTTPS, S3, GCS, Azure Blob Storage, and SSH/SFTP backends. The abstraction uses protocol-specific drivers (e.g., S3FileSystem, LocalFileSystem) that implement common operations (read, write, exists, remove) while handling authentication and connection pooling. This enables DVC to seamlessly work with data stored in different locations without requiring users to handle protocol-specific code.","intents":["Access data files from multiple cloud storage backends (S3, GCS, Azure) using unified DVC commands","Configure remote storage locations in .dvc/config and automatically sync data without manual protocol handling","Import data from HTTP URLs or SSH servers into DVC-tracked projects","Switch between local and remote storage backends transparently"],"best_for":["Teams using multiple cloud providers (AWS, GCP, Azure) for data storage","Organizations with hybrid on-premise and cloud infrastructure","Data scientists needing to access data from various sources without learning protocol-specific APIs"],"limitations":["Authentication must be configured per backend (AWS credentials, GCS service account, etc.) — no unified auth system","Performance varies significantly by backend; S3 operations are faster than SSH/SFTP due to connection pooling","No built-in data encryption in transit — relies on backend security (HTTPS, SSH key-based auth)","Custom filesystem backends require implementing protocol-specific driver class"],"requires":["Backend-specific credentials (AWS_ACCESS_KEY_ID for S3, GOOGLE_APPLICATION_CREDENTIALS for GCS, etc.)","Network connectivity to remote storage","Python 3.8+ with fsspec library (DVC's filesystem abstraction dependency)",".dvc/config file with remote storage configuration"],"input_types":["remote storage URLs (s3://bucket/path, gs://bucket/path, /local/path)",".dvc/config configuration files","authentication credentials (env vars, config files)"],"output_types":["data files from remote storage","synchronization status and logs","filesystem operation results (exists, size, etc.)"],"categories":["tool-use-integration","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_5","uri":"capability://tool.use.integration.git.integration.for.scm.aware.data.tracking.and.reproducibility","name":"git integration for scm-aware data tracking and reproducibility","description":"DVC integrates deeply with Git through the SCM Integration layer, storing pipeline definitions (dvc.yaml) and metadata (.dvc files) in Git while tracking actual data in remote storage. The Repository Class manages Git operations (commit, checkout, branch) and coordinates with DVC's cache and remote storage. This enables reproducibility by tying data versions to Git commits, allowing teams to checkout exact code+data combinations from history.","intents":["Track data file versions alongside code changes in Git history","Reproduce exact experiment conditions by checking out Git commits with associated data versions","Collaborate on data-driven projects using Git workflows (branches, pull requests, merges)","Maintain clean Git repositories by storing large files in remote storage while tracking metadata"],"best_for":["Teams already using Git for code version control wanting to extend it to data","ML projects needing reproducibility tied to code commits","Organizations wanting to avoid separate data versioning systems"],"limitations":["Git history can become large if .dvc files are frequently updated (e.g., daily experiments) — no built-in history pruning","Merging dvc.lock files from parallel branches requires manual conflict resolution (no automatic merge strategy)","Git's text-based diff is not optimal for binary .dvc metadata files — conflicts can be hard to resolve","Requires Git knowledge from team members — adds complexity vs standalone data versioning tools"],"requires":["Git repository initialized in project root","Git 2.0+ with configured user.name and user.email","DVC initialized in Git repository (dvc init creates .dvc directory)","Network access to Git remote (GitHub, GitLab, etc.) for collaboration"],"input_types":["Git commits and branches","dvc.yaml and .dvc files","data file paths"],"output_types":["Git commits with DVC metadata","dvc.lock files recording pipeline execution state",".dvc files tracking data versions"],"categories":["tool-use-integration","version-control"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_6","uri":"capability://data.processing.analysis.dependency.and.output.tracking.with.automatic.cache.invalidation","name":"dependency and output tracking with automatic cache invalidation","description":"DVC's Output and Dependency System tracks file dependencies (inputs to stages) and outputs (generated artifacts) using content-based checksums (MD5 or SHA256). The Index System maintains a mapping of file paths to checksums, enabling fast detection of changes. When dependencies change, the Reproduction and Caching subsystem marks dependent stages as stale and triggers re-execution. This enables smart pipeline caching where only affected stages are re-run.","intents":["Automatically detect when input data or code changes and mark dependent pipeline stages for re-execution","Skip pipeline stages that haven't changed since last run, avoiding redundant computation","Track which files are inputs and outputs of each pipeline stage","Reproduce exact pipeline results by re-running from any checkpoint with unchanged dependencies"],"best_for":["ML teams with long-running pipelines wanting to avoid redundant computation","Data scientists needing to understand data dependencies across pipeline stages","Researchers tracking which code/data changes triggered model retraining"],"limitations":["Dependency tracking is file-based, not semantic — renaming a file invalidates cache even if content is identical","Hash computation for large files adds overhead during dvc add and dvc repro operations","No built-in support for tracking implicit dependencies (e.g., Python imports) — only explicit file dependencies","Cache invalidation is conservative — any file change triggers re-execution of dependent stages"],"requires":["dvc.yaml defining stage dependencies and outputs","All dependencies must be tracked by DVC or Git","Python 3.8+ for hash computation","Sufficient disk space for cache layer"],"input_types":["file paths (dependencies and outputs)","dvc.yaml stage definitions","data files and directories"],"output_types":["dvc.lock file with checksums and execution state","cache directory structure","stage execution status"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_7","uri":"capability://tool.use.integration.configuration.management.with.multi.level.settings.hierarchy","name":"configuration management with multi-level settings hierarchy","description":"DVC's Configuration System manages settings across multiple levels: system-wide (/etc/dvc/config), user-level (~/.config/dvc/config), project-level (.dvc/config), and local-only (.dvc/config.local). The Repo class loads and merges configurations in precedence order, enabling users to set defaults globally and override per-project. Configuration includes remote storage definitions, cache settings, and authentication credentials, all stored in INI format.","intents":["Configure remote storage locations (S3, GCS, Azure) at project or user level","Set default cache directory and storage backend for all DVC projects","Store authentication credentials (AWS keys, GCS service accounts) securely in user-level config","Override global settings per-project without modifying user configuration"],"best_for":["Teams managing multiple DVC projects with shared remote storage configuration","Organizations wanting to set default storage backends across all projects","Users needing to store credentials securely without committing to Git"],"limitations":["Configuration is stored in plain text INI format — credentials are not encrypted (rely on file permissions)","No built-in secret management — credentials must be managed via environment variables or external tools","Configuration merging is simple (last-write-wins) — no support for complex inheritance or templating","Changes to .dvc/config require dvc config commands or manual editing; no GUI for configuration"],"requires":["DVC initialized in project (.dvc directory exists)","Write permissions to .dvc/config and ~/.config/dvc/config","Knowledge of DVC configuration keys (remote.myremote.url, cache.dir, etc.)"],"input_types":["dvc config commands (dvc remote add, dvc config cache.dir, etc.)","INI-format configuration files (.dvc/config, ~/.config/dvc/config)","environment variables for credential override"],"output_types":["merged configuration dictionary","remote storage definitions","cache and authentication settings"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_8","uri":"capability://data.processing.analysis.data.import.and.external.data.source.integration","name":"data import and external data source integration","description":"DVC's Adding and Importing Data subsystem enables importing data from external sources (HTTP URLs, S3 buckets, GCS, etc.) into DVC-tracked projects via dvc import-url and dvc import commands. The import process downloads data, computes checksums, and creates .dvc metadata files, while tracking the source URL for future updates. This enables teams to incorporate external datasets without duplicating storage.","intents":["Import datasets from public URLs or cloud storage into DVC-tracked projects","Track external data sources and update imported data when upstream changes","Incorporate third-party datasets without duplicating storage or committing large files to Git","Share imported data versions with team members via dvc pull"],"best_for":["Teams using public datasets (Kaggle, UCI, etc.) in ML projects","Organizations sharing data across projects via cloud storage","Data scientists needing to track external data dependencies"],"limitations":["Import tracking is URL-based — if external data changes, dvc update must be run manually","No built-in support for incremental imports — entire dataset is re-downloaded on update","Import sources must be publicly accessible or require authentication configuration","Large external datasets can take significant time to import and compute checksums"],"requires":["Network access to external data source (HTTP, S3, GCS, etc.)","Backend-specific credentials if importing from private cloud storage","Sufficient disk space for imported data","dvc import-url or dvc import commands"],"input_types":["external data URLs (http://, s3://, gs://, etc.)","authentication credentials for private sources"],"output_types":[".dvc metadata files tracking import source and checksum","imported data files in project directory","dvc.lock entries for imported data"],"categories":["data-processing-analysis","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__cap_9","uri":"capability://data.processing.analysis.status.and.diff.reporting.for.data.code.and.metrics.changes","name":"status and diff reporting for data, code, and metrics changes","description":"DVC's Diff and Status subsystem provides dvc status and dvc diff commands that compare current workspace state against Git commits and remote storage. The status command shows which files are modified, deleted, or new. The diff command compares metrics, parameters, and data across commits or experiments, displaying changes in a human-readable format. This enables teams to understand what changed between pipeline runs without manual inspection.","intents":["Check which data files, code, or parameters have changed since last commit","Compare metrics and parameters across experiments to identify improvements","Understand data changes between Git commits (file additions, deletions, modifications)","Generate diff reports for code review and experiment comparison"],"best_for":["ML teams reviewing experiment changes before committing","Data scientists comparing model performance across runs","Teams needing to understand what changed in data pipelines"],"limitations":["Diff output is text-based — large metric changes can be hard to visualize without plots","Status checking requires computing checksums for all tracked files — can be slow for large projects","Diff comparison is limited to files tracked by DVC or Git — implicit dependencies not detected","No built-in filtering for diff output — comparing specific metrics requires manual parsing"],"requires":["Git repository with commits to compare against","DVC-tracked files and metrics","Python 3.8+ for diff computation"],"input_types":["Git commits or branches to compare","experiment names or IDs","metric and parameter files"],"output_types":["status report (modified, deleted, new files)","diff tables (metrics, parameters, data changes)","human-readable change summaries"],"categories":["data-processing-analysis","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"dvc-cli__headline","uri":"capability://data.processing.analysis.data.version.control.cli.for.machine.learning.projects","name":"data version control cli for machine learning projects","description":"DVC is a command-line tool that enables data scientists and ML engineers to version control data files, models, and pipelines, ensuring reproducibility and efficient collaboration in machine learning projects.","intents":["best data version control tool","data version control for machine learning","how to version control ML models","tools for reproducible ML experiments","best CLI for data management in ML"],"best_for":["data scientists","ML engineers"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":57,"verified":false,"data_access_risk":"high","permissions":["Git repository initialized in project root","Python 3.8+","Cloud storage credentials (AWS, GCS, Azure, or compatible S3-like service)","Sufficient local disk space for cache layer","dvc.yaml file in project root defining pipeline stages","Git repository for tracking dvc.yaml changes","Bash or shell environment for executing stage commands","All stage dependencies (data files, code) must be tracked by DVC or Git","dvc package installed (pip install dvc)","Git repository initialized"],"failure_modes":["Requires external remote storage configuration — DVC does not provide hosted storage itself","Hash computation for large files adds initial overhead during dvc add operations","No built-in encryption for data in transit or at rest — relies on cloud provider security","Cache synchronization can be slow for projects with thousands of large files","DAG must be acyclic — circular dependencies are not supported","Stage caching is file-hash based, not semantic — renaming a file invalidates cache even if content is identical","No built-in parallelization across independent stages — execution is sequential by default","Parameter tracking requires explicit dvc.yaml configuration; implicit dependencies in Python code are not detected","Python API is less stable than CLI — breaking changes can occur between minor versions","API documentation is sparse compared to CLI documentation — requires reading source code for advanced usage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.9,"ecosystem":0.39999999999999997,"match_graph":0.25,"freshness":0.52,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.28,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:04.691Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=dvc-cli","compare_url":"https://unfragile.ai/compare?artifact=dvc-cli"}},"signature":"UvdcI7SmmPTVifyG9uPtpg9olXuIgeqFnxHjYqBJ5ZxUfct3gZ1mV+0TAG8kBJOrJ1Y2C/+yfLvFpaF9o4e4Cw==","signedAt":"2026-06-20T14:28:25.512Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/dvc-cli","artifact":"https://unfragile.ai/dvc-cli","verify":"https://unfragile.ai/api/v1/verify?slug=dvc-cli","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}